Search results for: segmentation preference

Authors: Muhammad Tariq, Bushra Siddique, Muhammad Naeem, Asim Gulzar

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The chemical ecology of natural enemies can play a pivotal role in any Integrated Pest Management (IPM) program. Different chemical cues help to correspond in the diversity of associations between prey and host plant species. Coccinellaseptempunctata and Diaeretiellarapae have the abilities to explore several chemical cues released by plants under herbivore attack that may enhance their efficiency of foraging. In this study, the behavioral responses of Coccinellaseptempunctata and Diaeretiellarapae were examined under the application of two semiochemicals and a plant extract and their combinations using four-arm olfactometer. The bioassay was consists of a pairwise treatment comparison. Data pertaining to the preference of C. septempunctata and D. rapae after treatment application were recorded and analyzed statistically. The mean number of entries and time spent of Coccinellaseptempunctata and D. rapaewere greater in arms treated with E-β-Farnesene. However, the efficacy of E-β-Farnesene was enhanced when combined with β-pinene. Thus, the mean number of entries and time spent of C. septempunctata and D. rapaewere highest in arms treated with the combination of E-β-Farnesene x β-pinene as compared with other treatments. The current work has demonstrated that the insect-derived semiochemicals may enhance the efficacy of natural enemies when applied in combination.

Keywords: olfectometer, parasitoid, predator, preference

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Authors: Kristina Sakalauskiene, Renaldas Raisutis, Gintare Linkeviciute, Skaidra Valiukeviciene

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Cutaneous melanoma is a melanocytic skin tumour, which has a very poor prognosis while is highly resistant to treatment and tends to metastasize. Thickness of melanoma is one of the most important biomarker for stage of disease, prognosis and surgery planning. In this study, we hypothesized that the automatic analysis of spectrophotometric images and high-frequency ultrasonic 2D data can improve differential diagnosis of cutaneous melanoma and provide additional information about tumour penetration depth. This paper presents the novel complex automatic system for non-invasive melanocytic skin tumour differential diagnosis and penetration depth evaluation. The system is composed of region of interest segmentation in spectrophotometric images and high-frequency ultrasound data, quantitative parameter evaluation, informative feature extraction and classification with linear regression classifier. The segmentation of melanocytic skin tumour region in ultrasound image is based on parametric integrated backscattering coefficient calculation. The segmentation of optical image is based on Otsu thresholding. In total 29 quantitative tissue characterization parameters were evaluated by using ultrasound data (11 acoustical, 4 shape and 15 textural parameters) and 55 quantitative features of dermatoscopic and spectrophotometric images (using total melanin, dermal melanin, blood and collagen SIAgraphs acquired using spectrophotometric imaging device SIAscope). In total 102 melanocytic skin lesions (including 43 cutaneous melanomas) were examined by using SIAscope and ultrasound system with 22 MHz center frequency single element transducer. The diagnosis and Breslow thickness (pT) of each MST were evaluated during routine histological examination after excision and used as a reference. The results of this study have shown that automatic analysis of spectrophotometric and high frequency ultrasound data can improve non-invasive classification accuracy of early-stage cutaneous melanoma and provide supplementary information about tumour penetration depth.

Keywords: cutaneous melanoma, differential diagnosis, high-frequency ultrasound, melanocytic skin tumours, spectrophotometric imaging

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

Procedia PDF Downloads 129

Authors: Xinyi Le

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In this paper, 3D printing orientations were determined through our perceptual model. Some FDM (Fused Deposition Modeling) 3D printers, which are widely used in universities and industries, often require support structures during the additive manufacturing. After removing the residual material, some surface artifacts remain at the contact points. These artifacts will damage the function and visual effect of the model. To prevent the impact of these artifacts, we present a fuzzy extreme learning machine approach to find printing directions that avoid placing supports in perceptually significant regions. The proposed approach is able to solve the evaluation problem by combing both the subjective knowledge and objective information. Our method combines the advantages of fuzzy theory, auto-encoders, and extreme learning machine. Fuzzy set theory is applied for dealing with subjective preference information, and auto-encoder step is used to extract good features without supervised labels before extreme learning machine. An extreme learning machine method is then developed successfully for training and learning perceptual models. The performance of this perceptual model will be demonstrated on both natural and man-made objects. It is a good human-computer interaction practice which draws from supporting knowledge on both the machine side and the human side.

Keywords: 3d printing, perceptual model, fuzzy evaluation, data-driven approach

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Authors: Hossein Nikandam Kermanshah, Babak Samavatian, Akbar Hemmati Sabet, Mohammad Ahmadpanah

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This is a quasi-experimental study that has been conducted in order to compare the effectiveness of social skills training and stress management training on self-esteem and aggression in first grade high school students. Forty-five people were selected from research community and were put randomly in there groups of social skills training, stress management training and control ones. Collecting data tools in this study was devise, self-esteem and AGQ aggression questionnaire. Self-esteem and aggression questionnaires has been conducted as the pre-test and post-test. Social skills training and stress management groups participated in eight 1.5 hour session in a week. But control group did not receive any therapy. For descriptive analysis of data, statistical indicators like mean, standard deviation were used, and in inferential statistics level multi variable covariance analysis have been used. The finding result show that group training social skills and stress management is significantly effective on the self-esteem and aggression, there is a meaningful difference between training social skills and stress management on self-esteem that the preference is with group social skills training, in the difference between group social skills training and stress management on aggression, the preference is with group stress management.

Keywords: social skill training, stress management training, self-esteem aggression, psychological sciences

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Authors: C. A. Anser Pasha

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In this paper, a new Vehicular Speed Detection Camera System that is applicable as an alternative to traditional radars with the same accuracy or even better is presented. The real-time measurement and analysis of various traffic parameters such as speed and number of vehicles are increasingly required in traffic control and management. Image processing techniques are now considered as an attractive and flexible method for automatic analysis and data collections in traffic engineering. Various algorithms based on image processing techniques have been applied to detect multiple vehicles and track them. The SDCS processes can be divided into three successive phases; the first phase is Objects detection phase, which uses a hybrid algorithm based on combining an adaptive background subtraction technique with a three-frame differencing algorithm which ratifies the major drawback of using only adaptive background subtraction. The second phase is Objects tracking, which consists of three successive operations - object segmentation, object labeling, and object center extraction. Objects tracking operation takes into consideration the different possible scenarios of the moving object like simple tracking, the object has left the scene, the object has entered the scene, object crossed by another object, and object leaves and another one enters the scene. The third phase is speed calculation phase, which is calculated from the number of frames consumed by the object to pass by the scene.

Keywords: radar, image processing, detection, tracking, segmentation

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Authors: Ingrid Argote, John Archila, Marcelo Becker

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In agriculture, intelligent systems applications have generated great advances in automating some of the processes in the production chain. In order to improve the efficiency of those systems is proposed a vision system to estimate the amount of fruits in sweet orange trees. This work presents a system proposal using capture of thermal images and fuzzy logic. A bibliographical review has been done to analyze the state-of-the-art of the different systems used in fruit recognition, and also the different applications of thermography in agricultural systems. The algorithm developed for this project uses the metrics of the fuzzines parameter to the contrast improvement and segmentation of the image, for the counting algorith m was used the Hough transform. In order to validate the proposed algorithm was created a bank of images of sweet orange Citrus (L.) Osbeck acquired in the Maringá Farm. The tests with the algorithm Indicated that the variation of the tree branch temperature and the fruit is not very high, Which makes the process of image segmentation using this differentiates, This Increases the amount of false positives in the fruit counting algorithm. Recognition of fruits isolated with the proposed algorithm present an overall accuracy of 90.5 % and grouped fruits. The accuracy was 81.3 %. The experiments show the need for a more suitable hardware to have a better recognition of small temperature changes in the image.

Keywords: Agricultural systems, Citrus, Fuzzy logic, Thermal images.

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Authors: Jacinta Ifeoma Obidile

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Teachers (vocational and technical educators inclusive) use Information and Communications Technology (ICT) for tasks outside of their normal working hours. This expansion of work duties to non-work time challenges their work-life balance. However, there has been inconsistency in the results on how these relationships correlate. This, therefore, calls for further research studies to examine the moderating mechanisms of such relationships. The present study, therefore, ascertained how vocational and technical educators’ technology acceptance relates to their work-related ICT use beyond their working hours and work-life balance, as well as how their integration affects these relationships. The population of the study comprised 320 Vocational and Technical Educators from the Southeast geopolitical zone of Nigeria. Data were collected from the respondents using the structured questionnaire. The questionnaire was validated by three experts. The reliability of the study was conducted using 20 vocational and technical educators from the South who were not part of the population. The overall reliability coefficient of 0.81 was established using Cronbach’s alpha method. The data collected was analyzed using Structural equation modeling. Findings, among others, revealed that vocational and technical educators’ work-life balance was mediated by increased digital learning environment use after work hours, although reduced by social influence.

Keywords: vocational and technical educators, digital learning environment, working hours, work-life balance, integration preference

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Authors: Vaidehi Murarka, Sneha Mehta, Dishant Upadhyay

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Our project topic is “Alphabet Recognition using pixel probability distribution”. The project uses techniques of Image Processing and Machine Learning in Computer Vision. Alphabet recognition is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files etc. Alphabet Recognition based OCR application is sometimes used in signature recognition which is used in bank and other high security buildings. One of the popular mobile applications includes reading a visiting card and directly storing it to the contacts. OCR's are known to be used in radar systems for reading speeders license plates and lots of other things. The implementation of our project has been done using Visual Studio and Open CV (Open Source Computer Vision). Our algorithm is based on Neural Networks (machine learning). The project was implemented in three modules: (1) Training: This module aims “Database Generation”. Database was generated using two methods: (a) Run-time generation included database generation at compilation time using inbuilt fonts of OpenCV library. Human intervention is not necessary for generating this database. (b) Contour–detection: ‘jpeg’ template containing different fonts of an alphabet is converted to the weighted matrix using specialized functions (contour detection and blob detection) of OpenCV. The main advantage of this type of database generation is that the algorithm becomes self-learning and the final database requires little memory to be stored (119kb precisely). (2) Preprocessing: Input image is pre-processed using image processing concepts such as adaptive thresholding, binarizing, dilating etc. and is made ready for segmentation. “Segmentation” includes extraction of lines, words, and letters from the processed text image. (3) Testing and prediction: The extracted letters are classified and predicted using the neural networks algorithm. The algorithm recognizes an alphabet based on certain mathematical parameters calculated using the database and weight matrix of the segmented image.

Keywords: contour-detection, neural networks, pre-processing, recognition coefficient, runtime-template generation, segmentation, weight matrix

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Authors: Dor Oppenheim, Yael Edan, Guy Shani

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This paper presents an image analysis algorithm to detect and count yellow tomato flowers in a greenhouse with uneven illumination conditions, complex growth conditions and different flower sizes. The algorithm is designed to be employed on a drone that flies in greenhouses to accomplish several tasks such as pollination and yield estimation. Detecting the flowers can provide useful information for the farmer, such as the number of flowers in a row, and the number of flowers that were pollinated since the last visit to the row. The developed algorithm is designed to handle the real world difficulties in a greenhouse which include varying lighting conditions, shadowing, and occlusion, while considering the computational limitations of the simple processor in the drone. The algorithm identifies flowers using an adaptive global threshold, segmentation over the HSV color space, and morphological cues. The adaptive threshold divides the images into darker and lighter images. Then, segmentation on the hue, saturation and volume is performed accordingly, and classification is done according to size and location of the flowers. 1069 images of greenhouse tomato flowers were acquired in a commercial greenhouse in Israel, using two different RGB Cameras – an LG G4 smartphone and a Canon PowerShot A590. The images were acquired from multiple angles and distances and were sampled manually at various periods along the day to obtain varying lighting conditions. Ground truth was created by manually tagging approximately 25,000 individual flowers in the images. Sensitivity analyses on the acquisition angle of the images, periods throughout the day, different cameras and thresholding types were performed. Precision, recall and their derived F1 score were calculated. Results indicate better performance for the view angle facing the flowers than any other angle. Acquiring images in the afternoon resulted with the best precision and recall results. Applying a global adaptive threshold improved the median F1 score by 3%. Results showed no difference between the two cameras used. Using hue values of 0.12-0.18 in the segmentation process provided the best results in precision and recall, and the best F1 score. The precision and recall average for all the images when using these values was 74% and 75% respectively with an F1 score of 0.73. Further analysis showed a 5% increase in precision and recall when analyzing images acquired in the afternoon and from the front viewpoint.

Keywords: agricultural engineering, image processing, computer vision, flower detection

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Authors: Marrone Silverio Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner, Djamel Fawzi Hadj Sadok

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The creation of a dataset is time-consuming and often discourages researchers from pursuing their goals. To overcome this problem, we present and discuss two solutions adopted for the automation of this process. Both optimize valuable user time and resources and support video object segmentation with object tracking and 3D projection. In our scenario, we acquire images from a moving robotic arm and, for each approach, generate distinct annotated datasets. We evaluated the precision of the annotations by comparing these with a manually annotated dataset, as well as the efficiency in the context of detection and classification problems. For detection support, we used YOLO and obtained for the projection dataset an F1-Score, accuracy, and mAP values of 0.846, 0.924, and 0.875, respectively. Concerning the tracking dataset, we achieved an F1-Score of 0.861, an accuracy of 0.932, whereas mAP reached 0.894. In order to evaluate the quality of the annotated images used for classification problems, we employed deep learning architectures. We adopted metrics accuracy and F1-Score, for VGG, DenseNet, MobileNet, Inception, and ResNet. The VGG architecture outperformed the others for both projection and tracking datasets. It reached an accuracy and F1-score of 0.997 and 0.993, respectively. Similarly, for the tracking dataset, it achieved an accuracy of 0.991 and an F1-Score of 0.981.

Keywords: RJ45, automatic annotation, object tracking, 3D projection

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Authors: Al Sayed Al Soudy, Valeria Maselli, Gianluca Polese, Anna Di Cosmo

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Cephalopods are considered as a model organism with a rich behavioral repertoire. Sophisticated behaviors were widely studied and described in different species such as Octopus vulgaris, who has evolved the largest and more complex nervous system among invertebrates. In O. vulgaris, cognitive abilities in problem-solving tasks and learning abilities are associated with long-term memory and spatial memory, mediated by highly developed sensory organs. They are equipped with sophisticated eyes, able to discriminate colors even with a single photoreceptor type, vestibular system, ‘lateral line analogue’, primitive ‘hearing’ system and olfactory organs. They can recognize chemical cues either through direct contact with odors sources using suckers or by distance through the olfactory organs. Cephalopods are able to detect widespread waterborne molecules by the olfactory organs. However, many volatile odorant molecules are insoluble or have a very low solubility in water, and must be perceived by direct contact. O. vulgaris, equipped with many chemosensory neurons located in their suckers, exhibits a peculiar behavior that can be provocatively described as 'smell by touch'. The aim of this study is to establish the priority given to chemical vs. visual perception in food choice. Materials and methods: Three different types of food (anchovies, clams, and mussels) were used, and all sessions were recorded with a digital camera. During the acclimatization period, Octopuses were exposed to the three types of food to test their natural food preferences. Later, to verify if food preference is maintained, food was provided in transparent screw-jars with pierced lids to allow both visual and chemical recognition of the food inside. Subsequently, we tested alternatively octopuses with food in sealed transparent screw-jars and food in blind screw-jars with pierced lids. As a control, we used blind sealed jars with the same lid color to verify a random choice among food types. Results and discussion: During the acclimatization period, O. vulgaris shows a higher preference for anchovies (60%) followed by clams (30%), then mussels (10%). After acclimatization, using the transparent and pierced screw jars octopus’s food choices resulted in 50-50 between anchovies and clams, avoiding mussels. Later, guided by just visual sense, with transparent but not pierced jars, their food preferences resulted in 100% anchovies. With pierced but not transparent jars their food preference resulted in 100% anchovies as first food choice, the clams as a second food choice result (33.3%). With no possibility to select food, neither by vision nor by chemoreception, the results were 20% anchovies, 20% clams, and 60% mussels. We conclude that O. vulgaris uses both chemical and visual senses in an integrative way in food choice, but if we exclude one of them, it appears clear that its food preference relies on chemical sense more than on visual perception.

Keywords: food choice, Octopus vulgaris, olfaction, sensory organs, visual sense

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Authors: Irena Smetackova

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The important part of school mathematics are word problems which represent the connection between school knowledge and life reality. To find the reasons why students consider word problems to be difficult, it is necessary to take into consideration the motivational settings, besides mathematical knowledge and reading skills. Our goal is to identify whether the familiar or unfamiliar context of math word problem influences solving success rate and if so, whether the reasons are motivational or cognitive. For this purpose, we conducted three steps study in group of fifty pupils 9-10 years old. In the first step, we asked pupils to create ‘the best’ word problems for entered numerical formula. The set of 19 word problems with different contexts were selected. In the second step, pupils were asked to evaluate (without solving) how they like each item and how easy it is for them. The 6 word problems with low preference and low estimated success rate were selected and combined with other 6 problems with high preference and success rate. In the third step, the same pupils were asked to solve the word problems. The analysis showed that pupils attitudes and solving toward word problems varied by the context. The strong gender patterns both in preferred contexts and in estimated success rates were identified however the real success rate did not differ so strongly. The success gap between word problems with and without preferred contexts were stronger than the gap between problems with and without real experience with the context. The hypothesis that motivational factors are more important than cognitive factors was confirmed.

Keywords: mathematics, context of reality, motivation, cognition, word problems

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Authors: Piotr Tylec, Julia Wierzbicka, Natalia Gajewska, Krzysztof Przeczek, Grzegorz Torbicz, Alicja Dudek, Magdalena Pisarska-Adamczyk, Mateusz Wierdak, Michal Pedziwiatr

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Introduction: Many patients have described changes in taste perception after weight loss surgery. However, little data is available about short term changes in taste after surgery. Aim: We aimed to evaluate short-term changes in taste preference after bariatric surgeries in comparison to colorectal surgeries. Material and Methods: Between April 2018 and April 2019, a total of 121 bariatric patients and 63 controls participated. Bariatric patients underwent laparoscopic sleeve gastrectomy or Roux-en-Y gastric by-pass. Controls underwent oncological colorectal surgeries. Patients who developed clinical complications requiring restriction of oral intake after surgery or withdraw their consent were excluded from the study. In the end, 85 bariatric patients and 44 controls were included. In all of them, the 16-item ERAS Protocol was applied. Using 10-points Numeric Rating Scale (1-10) patients completed questionnaire and rated their appetite and thirst (1 - no appetite/not thirsty, 10 – normal appetite/very thirsty) and flavoured standardized liquids' taste (1- horrible, 10-very tasty) and food images for the 6 group of taste (sweet, umami, sour, spicy, bitter and salty) (1 - not appetizing, 10 - very appetizing) preoperatively and on the first postoperative day. Data were analysed with Statistica 13.0 PL. Results: Analysed group consist of 129 patients (85 bariatric, 44 controls). Mean age and BMI in a research group was 44.91 years old, 46.22 kg/m² and in control group 62.09 years old, 25.87 kg/m², respectively. Our analysis revealed significant differences in changes of appetite between both groups (research: -4.55 ± 3.76 vs. control: -0.85 ± 4.37; p < 0.05), ratings bitter (research: 0.60 ± 2.98 vs. control: -0.88 ± 2.58; p < 0.05) and salty (research: 1.20 ± 3.50 vs. control: -0.52 ± 2.90; p < 0.05) flavoured liquids and ratings for sweet (research: 1.62 ± 3.31 vs. control: 0.01 ± 2.63; p < 0.05) and bitter (research: 1.21 ± 3.15 vs. control: -0.09 ± 2.25; p < 0.05) food images. There were statistically significant results in the ratings of other images, but in comparison to the control group, they were not statistically significant. Conclusion: The study showed that bariatric surgeries quickly decreases appetite and desire to eat certain types of food, such as salty. Moreover, the bitter taste was more desirable in the research group in comparison to control group. Nevertheless, the sweet taste was more appetible in the bariatric group than in control.

Keywords: bariatric surgery, general surgery, obesity, taste preference

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Authors: Sanjeev Prasher, T. Sai Vijay, Chandan Parsad, Abhishek Banerjee, Sahakari Nikhil Krishna, Subham Chatterjee

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With rapid rise in internet retailing, the industry is set for a major implosion. Due to the little difference among competitors, companies find it difficult to segment and target the right shoppers. The objective of the study is to segment Indian online shoppers on the basis of the factors – website characteristics and shopping values. Together, these cover extrinsic and intrinsic factors that affect shoppers as they visit web retailers. Data were collected using questionnaire from 319 Indian online shoppers, and factor analysis was used to confirm the factors influencing the shoppers in their selection of web portals. Thereafter, cluster analysis was applied, and different segments of shoppers were identified. The relationship between income groups and online shoppers’ segments was tracked using correspondence analysis. Significant findings from the study include that web entertainment and informativeness together contribute more than fifty percent of the total influence on the web shoppers. Contrary to general perception that shoppers seek utilitarian leverages, the present study highlights the preference for fun, excitement, and entertainment during browsing of the website. Four segments namely Information Seekers, Utility Seekers, Value Seekers and Core Shoppers were identified and profiled. Value seekers emerged to be the most dominant segment with two-fifth of the respondents falling for hedonic as well as utilitarian shopping values. With overlap among the segments, utilitarian shopping value garnered prominence with more than fifty-eight percent of the total respondents. Moreover, a strong relation has been established between the income levels and the segments of Indian online shoppers. Web shoppers show different motives from being utility seekers to information seekers, core shoppers and finally value seekers as income levels increase. Companies can strategically use this information for target marketing and align their web portals accordingly. This study can further be used to develop models revolving around satisfaction, trust and customer loyalty.

Keywords: online shopping, shopping values, effectiveness of information content, web informativeness, web entertainment, information seekers, utility seekers, value seekers, core shoppers

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Authors: Guilherme Giacomini, Ana Luiza Menegatti Pavan, Allan Felipe Fattori Alves, Marcela de Oliveira, Fernando Antonio Bacchim Neto, José Ricardo de Arruda Miranda, Seizo Yamashita, Diana Rodrigues de Pina

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The maxillary sinus (MS), part of the paranasal sinus complex, is one of the most enigmatic structures in modern humans. The literature has suggested that MSs function as olfaction accessories, to heat or humidify inspired air, for thermoregulation, to impart resonance to the voice and others. Thus, the real function of the MS is still uncertain. Furthermore, the MS anatomy is complex and varies from person to person. Many diseases may affect the development process of sinuses. The incidence of rhinosinusitis and other pathoses in the MS is comparatively high, so, volume analysis has clinical value. Providing volume values for MS could be helpful in evaluating the presence of any abnormality and could be used for treatment planning and evaluation of the outcome. The computed tomography (CT) has allowed a more exact assessment of this structure, which enables a quantitative analysis. However, this is not always possible in the clinical routine, and if possible, it involves much effort and/or time. Therefore, it is necessary to have a convenient, robust, and practical tool correlated with the MS volume, allowing clinical applicability. Nowadays, the available methods for MS segmentation are manual or semi-automatic. Additionally, manual methods present inter and intraindividual variability. Thus, the aim of this study was to develop an automatic tool to quantity the MS volume in CT scans of paranasal sinuses. This study was developed with ethical approval from the authors’ institutions and national review panels. The research involved 30 retrospective exams of University Hospital, Botucatu Medical School, São Paulo State University, Brazil. The tool for automatic MS quantification, developed in Matlab®, uses a hybrid method, combining different image processing techniques. For MS detection, the algorithm uses a Support Vector Machine (SVM), by features such as pixel value, spatial distribution, shape and others. The detected pixels are used as seed point for a region growing (RG) segmentation. Then, morphological operators are applied to reduce false-positive pixels, improving the segmentation accuracy. These steps are applied in all slices of CT exam, obtaining the MS volume. To evaluate the accuracy of the developed tool, the automatic method was compared with manual segmentation realized by an experienced radiologist. For comparison, we used Bland-Altman statistics, linear regression, and Jaccard similarity coefficient. From the statistical analyses for the comparison between both methods, the linear regression showed a strong association and low dispersion between variables. The Bland–Altman analyses showed no significant differences between the analyzed methods. The Jaccard similarity coefficient was > 0.90 in all exams. In conclusion, the developed tool to quantify MS volume proved to be robust, fast, and efficient, when compared with manual segmentation. Furthermore, it avoids the intra and inter-observer variations caused by manual and semi-automatic methods. As future work, the tool will be applied in clinical practice. Thus, it may be useful in the diagnosis and treatment determination of MS diseases. Providing volume values for MS could be helpful in evaluating the presence of any abnormality and could be used for treatment planning and evaluation of the outcome. The computed tomography (CT) has allowed a more exact assessment of this structure which enables a quantitative analysis. However, this is not always possible in the clinical routine, and if possible, it involves much effort and/or time. Therefore, it is necessary to have a convenient, robust and practical tool correlated with the MS volume, allowing clinical applicability. Nowadays, the available methods for MS segmentation are manual or semi-automatic. Additionally, manual methods present inter and intraindividual variability. Thus, the aim of this study was to develop an automatic tool to quantity the MS volume in CT scans of paranasal sinuses. This study was developed with ethical approval from the authors’ institutions and national review panels. The research involved 30 retrospective exams of University Hospital, Botucatu Medical School, São Paulo State University, Brazil. The tool for automatic MS quantification, developed in Matlab®, uses a hybrid method, combining different image processing techniques. For MS detection, the algorithm uses a Support Vector Machine (SVM), by features such as pixel value, spatial distribution, shape and others. The detected pixels are used as seed point for a region growing (RG) segmentation. Then, morphological operators are applied to reduce false-positive pixels, improving the segmentation accuracy. These steps are applied in all slices of CT exam, obtaining the MS volume. To evaluate the accuracy of the developed tool, the automatic method was compared with manual segmentation realized by an experienced radiologist. For comparison, we used Bland-Altman statistics, linear regression and Jaccard similarity coefficient. From the statistical analyses for the comparison between both methods, the linear regression showed a strong association and low dispersion between variables. The Bland–Altman analyses showed no significant differences between the analyzed methods. The Jaccard similarity coefficient was > 0.90 in all exams. In conclusion, the developed tool to automatically quantify MS volume proved to be robust, fast and efficient, when compared with manual segmentation. Furthermore, it avoids the intra and inter-observer variations caused by manual and semi-automatic methods. As future work, the tool will be applied in clinical practice. Thus, it may be useful in the diagnosis and treatment determination of MS diseases.

Keywords: maxillary sinus, support vector machine, region growing, volume quantification

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Authors: Feyisayo Odunitan-Wayas, Unathi Kolanisi, Micheal Chimonyo, Muthulisi Siwela

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Consumers’ surveys have indicated low acceptability of provitamin A biofortified maize (PABM), a high vitamin A (HVA) maize in Southern Africa to curb vitamin A deficiency (VAD). Indigenous chickens are reared and consumed by almost all rural households which are the major VAD prone areas in southern Africa. The objective of this study was to determine if HVA diet fed to the Ovambo indigenous chicken breed will influence consumers’ acceptability of the meat. The leg (thigh and drumstick) of the male birds (21 weeks old) were used for the sensory characteristics. 52 consumer panellists evaluated the sensory characteristics on based on a 5-point hedonic scale and preference test. There was no significant difference (P<0.05) in the preference test between the two diets. There were no significant differences (P<0.05) between the diets based on all sensory characteristics. Age and gender of the consumers and their interactions had no effect (P<0.05) on the acceptability and sensory characteristic ratings. It was concluded that indigenous chickens fed provitamin A biofortified maize can be a possible tool for curbing VAD in southern Africa regions where there is low acceptability of the human consumption of provitamin A biofortified maize. It was concluded that PABM diet fed to indigenous chickens will not influence the acceptability of the chicken meat by VAD vulnerable consumers.

Keywords: biofortified pro-vitamin a maize, ovambo, chicken meat, sensory characteristics, gender, age

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Authors: Ricardo Daziano

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In the analysis and planning of the mobility ecosystem, preferences for ride-hailing over incumbent street-hailing services need better understanding. In this paper, a seminonparametric discrete choice model that allows for flexible preference heterogeneity is fitted with data from a discrete choice experiment among adult commuters in Montreal, Canada (N=760). Participants chose among Uber, Teo (a local electric ride-hailing service that was in operation when data was collected in 2018), and a standard taxi when presented with information about cost, time (on-trip, waiting, walking), powertrain of the car (gasoline/hybrid) for Uber and taxi, and whether the available electric Teo was a Tesla (which was one of the actual features of the Teo fleet). The fitted flexible model offers several behavioral insights. Waiting time for ride-hailing services is associated with a statistically significant but low marginal disutility. For other time components, including on-ride, and street-hailing waiting and walking the estimates of the value of time show an interesting pattern: whereas in a conditional logit on-ride time reductions are valued higher, in the flexible LML specification means of the value of time follow the expected pattern of waiting and walking creating a higher disutility. At the same time, the LML estimates show the presence of important, multimodal unobserved preference heterogeneity.

Keywords: discrete choice, electric taxis, ridehailing, semiparametrics

Procedia PDF Downloads 162

Authors: Angelis P. Barlampas

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Learning Objectives: The purpose of this study is to inform briefly the reader about the applications of AI in chest radiology. Background: Currently, there are 190 FDA-approved radiology AI applications, with 42 (22%) pertaining specifically to thoracic radiology. Imaging findings OR Procedure details Aids of AI in chest radiology1: Detects and segments pulmonary nodules. Subtracts bone to provide an unobstructed view of the underlying lung parenchyma and provides further information on nodule characteristics, such as nodule location, nodule two-dimensional size or three dimensional (3D) volume, change in nodule size over time, attenuation data (i.e., mean, minimum, and/or maximum Hounsfield units [HU]), morphological assessments, or combinations of the above. Reclassifies indeterminate pulmonary nodules into low or high risk with higher accuracy than conventional risk models. Detects pleural effusion . Differentiates tension pneumothorax from nontension pneumothorax. Detects cardiomegaly, calcification, consolidation, mediastinal widening, atelectasis, fibrosis and pneumoperitoneum. Localises automatically vertebrae segments, labels ribs and detects rib fractures. Measures the distance from the tube tip to the carina and localizes both endotracheal tubes and central vascular lines. Detects consolidation and progression of parenchymal diseases such as pulmonary fibrosis or chronic obstructive pulmonary disease (COPD).Can evaluate lobar volumes. Identifies and labels pulmonary bronchi and vasculature and quantifies air-trapping. Offers emphysema evaluation. Provides functional respiratory imaging, whereby high-resolution CT images are post-processed to quantify airflow by lung region and may be used to quantify key biomarkers such as airway resistance, air-trapping, ventilation mapping, lung and lobar volume, and blood vessel and airway volume. Assesses the lung parenchyma by way of density evaluation. Provides percentages of tissues within defined attenuation (HU) ranges besides furnishing automated lung segmentation and lung volume information. Improves image quality for noisy images with built-in denoising function. Detects emphysema, a common condition seen in patients with history of smoking and hyperdense or opacified regions, thereby aiding in the diagnosis of certain pathologies, such as COVID-19 pneumonia. It aids in cardiac segmentation and calcium detection, aorta segmentation and diameter measurements, and vertebral body segmentation and density measurements. Conclusion: The future is yet to come, but AI already is a helpful tool for the daily practice in radiology. It is assumed, that the continuing progression of the computerized systems and the improvements in software algorithms , will redder AI into the second hand of the radiologist.

Keywords: artificial intelligence, chest imaging, nodule detection, automated diagnoses

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Authors: Muhammad Rafiq Shahid, Ali Hassan, Umm-e- Rubab, Muhammad Nadeem

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Insecticidal property of plant extracts was assessed toward dusky bug of cotton. Plant extracts consisted of bari pata (Ziziphus jajuba), Ak (Calotropis gigantean), Tobacco (Nicotiana tabacum), Bakine (Melia azedarach),Kanar (Nerium oleander),Kurtuma (Mitragyna speciosa) and one Control was also included with distilled water treatment. Forced feeding experiment was used to determine the antibiotic effect of bug plant extracts on dusky bug whereas Multi-choice experiment to determine the antixenosis/ repellent property of botanicals. It is evident from the results that mortality and antibiosis percentage of dusky bug due to the use of botanicals ranged from 15-95% and 20-87.3% respectively that was maximum in tobacoo extract followed by bakain and kurtama, minimum was on Ak, kanair and bakain extract. Non preference ranged from 14.28 to 85.7 where maximum non preference of dusky bug was found on bakain and kurtama followed by ak and kanair however minimum was on Bari pata extract. It was further found that local plant extract possessed insecticidal property toward dusky bug as well as also possesses repellency effect toward dusky bug, thus should be included in integrated pest management program of cotton in order to minimize the ill effects of pesticides it is compulsory to adopt eco-friendly methods of insect pest management.

Keywords: botanical extract, insecticidal and repellency activity, Gossypium hirsutum, oxycarenus laetus

Procedia PDF Downloads 474

Authors: Naouel Zoghlami

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Spoken-word recognition involves the simultaneous activation of potential word candidates which compete with each other for final correct recognition. In continuous speech, the activation-competition process gets more complicated due to speech reductions existing at word boundaries. Lexical processing is more difficult in L2 than in L1 because L2 listeners often lack phonetic, lexico-semantic, syntactic, and prosodic knowledge in the target language. In this study, we investigate the on-line lexical segmentation hypotheses that French listeners of L2 English form and then revise as subsequent perceptual evidence is revealed. Our purpose is to shed further light on the processes of L2 spoken-word recognition in context and better understand L2 listening difficulties through a comparison of skilled and unskilled reactions at the point where their working hypothesis is rejected. We use a variant of the gating experiment in which subjects transcribe an English sentence presented in increments of progressively greater duration. The spoken sentence was “And this amazing athlete has just broken another world record”, chosen mainly because it included common reductions and phonetic features in English, such as elision and assimilation. Our preliminary results show that there is an important difference in the manner in which proficient and less-proficient L2 listeners handle connected speech. Less-proficient listeners delay recognition of words as they wait for lexical and syntactic evidence to appear in the gates. Further statistical results are currently being undertaken.

Keywords: gating paradigm, spoken word recognition, online lexical segmentation, L2 listening

Procedia PDF Downloads 464

Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

Procedia PDF Downloads 130

Authors: Villiam Ambong, Kevin Banawag, Wynne Shane Bugatan, Mark Alvin Jay Carpio, Hwan Hee Choi, Moises Kevin Chungalao

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This survey research investigated the perception of primary school teachers on the efficacy of MTB-MLE in SLU-LES, Baguio City. SLU-LES has a total of 21 primary school teachers who served as respondents of this study in an attempt to answer the major questions regarding the efficacy of MTB-MLE among primary school teachers. A questionnaire was used in collecting the data which were analyzed using weighted mean and ANOVA. The questionnaire was validated by a statistician and it was administered to a school which does not differ from the intended respondents for further validation of the items. Findings revealed from the intended respondents that they perceive MTB-MLE as effective; however, they do not prefer the use of Mother Tongue as a medium of instruction. A research on the same topic was conducted in Ibadan, Nigeria by Dr. David O. Fakeye and although his respondents were students; the results came out that the respondents do perceive MTB-MLE to be efficacious. The results of this study also showed that years of teaching experience and the number of languages spoken by the teachers have no bearing on the preference of the respondents between MT medium and English medium gave that the respondents are in melting pot community. Comparative studies between rural and urban schools are encouraged. Future researchers should include questions that elicit reasons of the respondents on the efficacy of mother tongue as well as their preference between mother tongue medium and English.

Keywords: mother tongue, primary teachers, perception, multilingual education

Procedia PDF Downloads 276

Authors: A. Sathiya Susuman, Hamisi F. Hamisi

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Background: Many socio-economic and demographic data are age-sex attributed. However, a variety of irregularities and misstatement are noted with respect to age-related data and less to sex data because of its biological differences between the genders. Noting the misstatement/misreporting of age data regardless of its significance importance in demographics and epidemiological studies, this study aims at assessing the quality of 2012 Tanzania Population and Housing Census Results. Methods: Data for the analysis are downloaded from Tanzania National Bureau of Statistics. Age heaping and digit preference were measured using summary indices viz., Whipple’s index, Myers’ blended index, and Age-Sex Accuracy index. Results: The recorded Whipple’s index for both sexes was 154.43; male has the lowest index of about 152.65 while female has the highest index of about 156.07. For Myers’ blended index, the preferences were at digits ‘0’ and ‘5’ while avoidance were at digits ‘1’ and ‘3’ for both sexes. Finally, Age-sex index stood at 59.8 where sex ratio score was 5.82 and age ratio scores were 20.89 and 21.4 for males and female respectively. Conclusion: The evaluation of the 2012 PHC data using the demographic techniques has qualified the data inaccurate as the results of systematic heaping and digit preferences/avoidances. Thus, innovative methods in data collection along with measuring and minimizing errors using statistical techniques should be used to ensure accuracy of age data.

Keywords: age heaping, digit preference/avoidance, summary indices, Whipple’s index, Myer’s index, age-sex accuracy index

Procedia PDF Downloads 476

Authors: Villiam C. Ambong, Kevin G. Banawag, Wynne Shane B. Bugatan, Mark Alvin Jay R. Carpio, Hwan Hee Choi, Moses Kevin L. Chungalao

Abstract:

This survey research investigated the perception of primary school teachers on the efficacy of MTB-MLE in SLU-LES, Baguio City. SLU-LES has a total of 21 primary school teachers who served as the respondents of this study in an attempt to answer three major questions regarding the efficacy of MTB-MLE among primary school teachers. A questionnaire was used in collecting the data which were analyzed using weighted mean and ANOVA. The questionnaire was validated by a statistician and it was administered to a school which does not differ from the intended respondents for further validation of the items. Findings revealed from the intended respondents that they perceive MTB-MLE as effective; however, they do not prefer the use of Mother Tongue as medium of instruction. A research of the same topic was conducted in Ibadan, Nigeria by Dr. David O. Fakeye and although his respondents were students; the results came out that the respondents do perceive MTB-MLE to be efficacious. The results of this study also showed that years of teaching experience and number of languages spoken by the teachers have no bearing on the preference of the respondents between MT medium and English medium given that the respondents are in a melting pot community. Comparative studies between rural schools and urban schools are encouraged. Future researches should include questions that elicit reasons of the respondents on the efficacy of mother tongue as well as their preference between mother tongue medium and English.

Keywords: mother tongue, primary teachers, perception, multilingual education

Procedia PDF Downloads 453

Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

Procedia PDF Downloads 94

Authors: Clarence Dissanayake, Anishka A. Hettiarachchi

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Landscape architecture needs to encompass a placemaking process carefully composing and manipulating landscape elements to address perceptual needs of humans, especially aesthetic, psychological and spiritual. The objective of this qualitative investigation is to inquire the impact of elements of rock and water combination on landscape perception and related feelings, emotions, and behavior. The past empirical studies have assessed the impact of landscape elements in isolation on user preference, yet the combined effect of elements have been less considered. This research was conducted with reference to the verity of qualities of water and rock through a visual landscape quality assessment focusing on landscape qualities derived from five visual concepts (coherence, historicity imageability, naturalness, and ephemera). 'Kaludiya Pokuna' archeological site in Anuradhapura was investigated with a sample of University students (n=19, male 14, female 5, age 20-25) using a five-point Likert scale via a perception based questionnaire and a visitor employed photographic survey (VEP). Two hypothetical questions were taken into investigation concerning biophilic (naturalness) and topophilic (historicity) aspects of humans to prefer a landscape with rock and water. The findings revealed that this combination encourages both biophilic and topophilic aspects, but in varying degrees. The identified hierarchy of visual concepts based on visitor’s preference signify coherence (93%), historicity (89%), imageability (79%), naturalness (75%) and ephemera (70%) respectively. It was further revealed that this combination creates a scenery more coherent dominating information processing aspect of humans to perceive a landscape over the biophilic and topophilic aspects. Different characteristics and secondary landscape effects generated by rock and water combination were found to affect in transforming a space into a place, full filling the aesthetic and spiritual aspects of the visitors. These findings enhance a means of making places for people, resource management and historical landscape conservation. Equalization of gender based participation, taking diverse cases and increasing the sample size with more analytical photographic analysis are recommended to enhance the quality of further research.

Keywords: landscape perception, visitor’s preference, rock and water combination, visual concepts

Procedia PDF Downloads 225

Authors: Varsha V. Rohini, P. R. Shreya, B. Renukadevi

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In the web analytics the information delivery and the web usage is optimized and the analysis of data is done. The analytics is the measurement, collection and analysis of webpage data. Page statistics and user metrics are the important factor in most of the web analytics tool. This is the limitation of the existing tools. It does not provide design inputs for the optimization of information. This paper aims at providing an extension for the scope of web analytics to provide analysis and statistics of each segment of a webpage. The number of click count is calculated and the concentration of links in a web page is obtained. Its user metrics are used to help in proper design of the displayed content in a webpage by Vision Based Page Segmentation (VIPS) algorithm. When the algorithm is applied on the web page it divides the entire web page into the visual block tree. The visual block tree generated will further divide the web page into visual blocks or segments which help us to understand the usage of each segment in a page and its content. The dynamic web pages and deep web pages are used to extend the scope of web page segment analytics. Space optimization concept is used with the help of the output obtained from the Vision Based Page Segmentation (VIPS) algorithm. This technique provides us the visibility of the user interaction with the WebPages and helps us to place the important links in the appropriate segments of the webpage and effectively manage space in a page and the concentration of links.

Keywords: analytics, design optimization, visual block trees, vision based technology

Procedia PDF Downloads 266

Authors: M. Hadavi, Z. Hashemi

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Learning strategies play an important role in the development of language skills. Vocabulary learning strategies as the backbone of these strategies have become a major part of English language teaching. This study is a comparative analysis of Vocabulary Learning Strategies (VLS) use and preference among freshmen and senior EFL medical sciences students with different fields of study. 449 students (236 freshman and 213 seniors) participated in the study. 64.6% were female and 35.4% were male. The instrument utilized in this research was a questionnaire consisting of 41 items related to the students’ approach to vocabulary learning. The items were classified under eight sections as dictionary strategies, guessing strategies, study preferences, memory strategies, autonomy, note- taking strategies, selective attention, and social strategies. The participants were asked to answer each item with a 5-point Likert-style frequency scale as follows:1) I never or almost never do this, 2) I don’t usually do this, 3) I sometimes do this, 4) I usually do this, and 5)I always or almost always do this. The results indicated that freshmen students and particularly surgical technology students used more strategies compared to the seniors. Overall guessing and dictionary strategies were the most frequently used strategies among all the learners (p=0/000). The mean and standard deviation of using VLS in the students who had no previous history of participating in the private English language classes was less than the students who had attended these type of classes (p=0/000). Female students tended to use social and study preference strategies whereas male students used mostly guessing and dictionary strategies. It can be concluded that the senior students under instruction from the university have learned to rely on themselves and choose the autonomous strategies more, while freshmen students use more strategies that are related to the study preferences.

Keywords: vocabulary leaning strategies, medical sciences, students, linguistics

Procedia PDF Downloads 451

Authors: Sarah K. Hagi, Majdi Alnowaimi

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In the last decade, there were immense advancements in the medical imaging modalities. These advancements can scan a whole volume of the lung organ in high resolution images within a short time. According to this performance, the physicians can clearly identify the complicated anatomical and pathological structures of lung. Therefore, these advancements give large opportunities for more advance of all types of lung cancer treatment available and will increase the survival rate. However, lung cancer is still one of the major causes of death with around 19% of all the cancer patients. Several factors may affect survival rate. One of the serious effects is the breathing process, which can affect the accuracy of diagnosis and lung tumor treatment plan. We have therefore developed a semi automated algorithm to localize the 3D lung tumor positions across all respiratory data during respiratory motion. The algorithm can be divided into two stages. First, a lung tumor segmentation for the first phase of the 4D computed tomography (CT). Lung tumor segmentation is performed using an active contours method. Then, localize the tumor 3D position across all next phases using a 12 degrees of freedom of an affine transformation. Two data set where used in this study, a compute simulate for 4D CT using extended cardiac-torso (XCAT) phantom and 4D CT clinical data sets. The result and error calculation is presented as root mean square error (RMSE). The average error in data sets is 0.94 mm ± 0.36. Finally, evaluation and quantitative comparison of the results with a state-of-the-art registration algorithm was introduced. The results obtained from the proposed localization algorithm show a promising result to localize alung tumor in 4D CT data.

Keywords: automated algorithm , computed tomography, lung tumor, tumor localization

Procedia PDF Downloads 602