Search results for: medical image analysis

Authors: Hossein Shareh, Farhad Seifi

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The present study was to examine the role of attachment styles, gender schemas, sexual-self schemas, and body image during sexual activity in sexual function, marital satisfaction, and sexual self-esteem. The sampling method was among married women who were living in Mashhad; a snowball selected 765 people. Questionnaires and measures of adult attachment style (AAS), Bem Sex Role Inventory (BSRI), sexual self-schema (SSS), body exposure during sexual activity questionnaire (BESAQ), sexual function female inventory (FSFI), a short form of sexual self-esteem (SSEI-W-SF) and marital satisfaction (Enrich) were completed by participants. Data analysis using Pearson correlation and hierarchical regression and case analysis was performed by SPSS-19 software. The results showed that there is a significant correlation (P <0.05) between attachment and sexual function (r=0.342), marital satisfaction (r=0.351) and sexual self-esteem (r =0.292). A correlation (P <0.05) was observed between sexual schema (r=0.342) and sexual esteem (r=0.31). A meaningful correlation (P <0.05) exists between gender stereotypes and sexual function (r=0.352). There was a significant inverse correlation (P <0.05) between body image and their performance during sexual activity (r=0.41). There is no significant relationship between gender schemas, sexual schemas, body image, and marital satisfaction, and no relation was found between gender schemas, body image, and sexual self-esteem. Also, the result of the regression showed that attachment styles, gender schemas, sexual self- schemas, and body exposures during sexual activity are predictable in sexual function, and marital satisfaction can be predicted by attachment style and gender schema. Somewhat, sexual self-esteem can be expected by attachment style and gender schemas.

Keywords: attachment styles, gender and sexual schemas, body image, sexual function, marital satisfaction, sexual self-esteem

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Authors: Adnan H. M. Al-Helali, Hamza A. Ali

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The growth of censorship and pervasive monitoring on the Internet, Steganography arises as a new means of achieving secret communication. Steganography is the art and science of embedding information within electronic media used by common applications and systems. Generally, hiding information of multimedia within images will change some of their properties that may introduce few degradation or unusual characteristics. This paper presents a new image steganography approach for hiding information of multimedia (images, text, and audio) using generated Mandelbrot Fractal image as a cover. The proposed technique has been extensively tested with different images. The results show that the method is a very secure means of hiding and retrieving steganographic information. Experimental results demonstrate that an effective improvement in the values of the Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Normalized Cross Correlation (NCC), and Image Fidelity (IF) over the pervious techniques.

Keywords: fractal image, information hiding, Mandelbrot set fractal, steganography

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Authors: Ana Carolina Domingues

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This work presents the preliminary results of the above-titled doctoral research. Based on this film and on Middle East authors who discuss films made by women, it has been concluded so far, that it is part of a larger movement, which together with other productions, show the perceptions of the world of these women, who see the world otherwise, for not holding positions of power. These modes of perception revealed from the encounter of women with the cameras, educate viewers to denaturalize the impressions constructed in relation to the Middle East.

Keywords: cinema, image, middle east, women

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Authors: Tudor Barbu

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An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Second, an automatic pixel classification approach is proposed. The feature vectors are clustered using some unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: image segmentation, moment-based, texture analysis, automatic classification, validation indexes

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Authors: Boukhatem Mohammed Belkaid, Lahdir Mourad

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In this paper, we propose a new encryption system for security issues meteorological images from Meteosat Second Generation (MSG), which generates 12 images every 15 minutes. The hybrid encryption scheme is based on AES and RSA algorithms to validate the three security services are authentication, integrity and confidentiality. Privacy is ensured by AES, authenticity is ensured by the RSA algorithm. Integrity is assured by the basic function of the correlation between adjacent pixels. Our system generates a unique password every 15 minutes that will be used to encrypt each frame of the MSG meteorological basis to strengthen and ensure his safety. Several metrics have been used for various tests of our analysis. For the integrity test, we noticed the efficiencies of our system and how the imprint cryptographic changes at reception if a change affects the image in the transmission channel.

Keywords: AES, RSA, integrity, confidentiality, authentication, satellite MSG, encryption, decryption, key, correlation

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Authors: Aticha Kwaengsopha, Kevin Wongleedee

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Destination image can cause an increase and decrease in the growth rate of international tourists visiting Thailand. This paper drew upon data collected from an international tourist survey conducted in Bangkok, Thailand during January to March of 2014. The survey was structured primarily to obtain international tourists’ opinions towards the importance of destination image factors that they encountered during their trip in Thailand. A total of 200 respondents were elicited as data input for mean, SD, and t-test. The findings revealed that the overall level of importance of these factors was not very high. The findings also revealed the three most important factors as tourist experience, interesting place, and pleasing destination. In addition, the result for t-test revealed that there was not much effect from gender differences in opinions of the level concerning importance for destination image factors.

Keywords: destination image, international tourists, Thailand, revisit

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Authors: Jinseon Song, Yongwan Park

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In this paper, propose method that can user’s position that based on database is built from single camera. Previous positioning calculate distance by arrival-time of signal like GPS (Global Positioning System), RF(Radio Frequency). However, these previous method have weakness because these have large error range according to signal interference. Method for solution estimate position by camera sensor. But, signal camera is difficult to obtain relative position data and stereo camera is difficult to provide real-time position data because of a lot of image data, too. First of all, in this research we build image database at space that able to provide positioning service with single camera. Next, we judge similarity through image matching of database image and transmission image from user. Finally, we decide position of user through position of most similar database image. For verification of propose method, we experiment at real-environment like indoor and outdoor. Propose method is wide positioning range and this method can verify not only position of user but also direction.

Keywords: positioning, distance, camera, features, SURF(Speed-Up Robust Features), database, estimation

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Authors: Hsiao-Chuan Huang, King-Lung Kuo, Mei-Hsin Lo, Hsiao-Yun Chou, Yusen Lin

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The most robust and economical method for laboratory diagnosis of TB is to identify mycobacterial bacilli (AFB) under acid-fast staining despite its disadvantages of low sensitivity and labor-intensive. Though digital pathology becomes popular in medicine, an automated microscopic system for microbiology is still not available. A new AI-assisted automated microscopic system, consisting of a microscopic scanner and recognition program powered by big data and deep learning, may significantly increase the sensitivity of TB smear microscopy. Thus, the objective is to evaluate such an automatic system for the identification of AFB. A total of 5,930 smears was enrolled for this study. An intelligent microscope system (TB-Scan, Wellgen Medical, Taiwan) was used for microscopic image scanning and AFB detection. 272 AFB smears were used for transfer learning to increase the accuracy. Referee medical technicians were used as Gold Standard for result discrepancy. Results showed that, under a total of 1726 AFB smears, the automated system's accuracy, sensitivity and specificity were 95.6% (1,650/1,726), 87.7% (57/65), and 95.9% (1,593/1,661), respectively. Compared to culture, the sensitivity for human technicians was only 33.8% (38/142); however, the automated system can achieve 74.6% (106/142), which is significantly higher than human technicians, and this is the first of such an automated microscope system for TB smear testing in a controlled trial. This automated system could achieve higher TB smear sensitivity and laboratory efficiency and may complement molecular methods (eg. GeneXpert) to reduce the total cost for TB control. Furthermore, such an automated system is capable of remote access by the internet and can be deployed in the area with limited medical resources.

Keywords: TB smears, automated microscope, artificial intelligence, medical imaging

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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

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Authors: Tajinder Kaur, Anu Bala

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An image retrieval system is a computer system for browsing, searching, and retrieving images from a large database of digital images a new algorithm meant for content-based image retrieval (CBIR) is presented in this paper. The proposed method combines the color and texture features which are extracted the global and local information of the image. The local texture feature is extracted by using local binary patterns (LBP), which are evaluated by taking into consideration of local difference between the center pixel and its neighbors. For the global color feature, the color histogram (CH) is used which is calculated by RGB (red, green, and blue) spaces separately. In this paper, the combination of color and texture features are proposed for content-based image retrieval. The performance of the proposed method is tested on Corel 1000 database which is the natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and CH.

Keywords: color, texture, feature extraction, local binary patterns, image retrieval

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Authors: Lucila T. Magalong

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The hotel industry has been using their corporate image and reputation to maintain service quality, customer satisfaction, and customer loyalty and to leverage themselves against competitors and facilitate their growth strategies. With the increasing pressure to perform, hotels have even created hybrid service strategy to fight in the niche markets across pricing and level-off service parameters.

Keywords: corporate image, hotel industry, service quality, customer expectations

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Authors: Nikolaos Papadakis, Constantinos Condaxakis, Konstantinos Savvakis

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The aim of this paper is to determine the elastic properties (elastic modulus and Poisson ratio) of a composite material based on noncontacting imaging methods. More specifically, the significantly reduced cost of digital cameras has given the opportunity of the high reliability of low-cost strain measurement. The open source platform Ncorr is used in this paper which utilizes the method of digital image correlation (DIC). The use of digital image correlation in measuring strain uses random speckle preparation on the surface of the gauge area, image acquisition, and postprocessing the image correlation to obtain displacement and strain field on surface under study. This study discusses technical issues relating to the quality of results to be obtained are discussed. [0]8 fabric glass/epoxy composites specimens were prepared and tested at different orientations 0[o], 30[o], 45[o], 60[o], 90[o]. Each test was recorded with the camera at a constant frame rate and constant lighting conditions. The recorded images were processed through the use of the image processing software. The parameters of the test are reported. The strain map output which is obtained through strain measurement using Ncorr is validated by a) comparing the elastic properties with expected values from Classical laminate theory, b) through finite element analysis.

Keywords: composites, Ncorr, strain map, videoextensometry

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Zineb Nougrara

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In this paper, we propose a novel methodology for extracting a road network and its nodes from satellite images of Algeria country. This developed technique is a progress of our previous research works. It is founded on the information theory and the mathematical morphology; the information theory and the mathematical morphology are combined together to extract and link the road segments to form a road network and its nodes. We, therefore, have to define objects as sets of pixels and to study the shape of these objects and the relations that exist between them. In this approach, geometric and radiometric features of roads are integrated by a cost function and a set of selected points of a crossing road. Its performances were tested on satellite images of Algeria country.

Keywords: satellite image, road network, nodes, image analysis and processing

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Authors: Liu Mingxin

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Streets act as the organizers of each image element on the urban spatial route, and the spatial continuity of urban streets is the basis for people to perceive the overall image of the city. This paper takes the walking space of Zhengzhou city as the research object, conducts investigation and analysis through questionnaire interviews, and selects typical walking space for in-depth study. Through the analysis of questionnaire data, the investigation and analysis of the current situation of walking space, and the analysis of pedestrian psychological behavior activities, the paper summarizes the construction suggestions of urban walking space continuity from the three aspects of the composition of walking street, the bottom interface and side interface, and the service facilities of walking space. The walking space is not only the traffic space but also the comfortable experience and the continuity of the space.

Keywords: walking space, spatial continuity, walking psychology, space reconstruction

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Authors: Waraphorn Sribuakaew, Chutikarn Sriviboon, Rosjana Chandhasa

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The purpose of this research is to study the satisfaction level of graduates and factors that affect the image of Suan Sunandha Rajabhat University based on the perceptions of graduates on the graduation ceremony day. By studying the satisfaction of graduates, the image of Suan Sunandha Rajabhat University according to the graduates' perceptions and the loyalty to the university (in the aspects of intention to continue studying at a higher level, intention to recommend the university to a friend), the sample group used in this study was 1,000 graduates of Suan Sunandha Rajabhat University who participated on the 2019 graduation ceremony day. A questionnaire was utilized as a tool for data collection. By the use of computing software, the statistics used for data analysis were frequencies, percentage, mean, and standard deviation, One-Way ANOVA, and multiple regression analysis. Most of the respondents were graduates with a bachelor's degree, followed by graduates with a master's degree and PhD graduates, respectively. Major participants graduated from the Faculty of Management Sciences, followed by the Faculty of Humanities and Social Sciences and Faculty of Education, respectively. The graduates were satisfied on the ceremony day as a whole and rated each aspect at a satisfactory level. Formality, steps, and procedures were the aspects that graduates were most satisfied with, followed by graduation ceremony personnel and staff, venue, and facilities. On the perception of the graduates, the image of Suan Sunandha Rajabhat University was at a good level, while loyalty to the university was at a very high level. The intention of recommendation to others was at the highest level, followed by the intention to pursue further education at a very high level. The graduates graduating from different faculties have different levels of satisfaction on the graduation day with statistical significance at the level of 0.05. The image of Suan Sunandha Rajabhat University affected the satisfaction of graduates with statistical significance at the level of 0.01. The satisfactory level of graduates on the graduation ceremony day influenced the level of loyalty to the university with statistical significance at the level of 0.05.

Keywords: university image, loyalty to the university, intention to study higher education, intention to recommend the university to others, graduates' satisfaction

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Authors: Eunseop Yeom

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Platelets can be activated by the surrounding blood flows where a blood vessel is narrowed as a result of atherosclerosis. Numerous studies have been conducted to identify the relation between platelets activation and thrombus formation. To measure platelet adhesion, this study proposes an image analysis technique. Blood samples are delivered in the microfluidic channel, and then platelets are activated by a stenotic micro-channel with 90% severity. By applying proposed correlation mapping, which visualizes decorrelation of the streaming blood flow, the area of adhered platelets (APlatelet) was estimated without labeling platelets. In order to evaluate the performance of correlation mapping on the detection of platelet adhesion, the effect of tile size was investigated by calculating 2D correlation coefficients with binary images obtained by manual labeling and the correlation mapping method with different sizes of the square tile ranging from 3 to 50 pixels. The maximum 2D correlation coefficient is observed with the optimum tile size of 5×5 pixels. As the area of the platelet adhesion increases, the platelets plug the channel and there is only a small amount of blood flows. This image analysis could provide new insights for better understanding of the interactions between platelet aggregation and blood flows in various physiological conditions.

Keywords: platelet activation, correlation coefficient, image analysis, shear rate

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Authors: Heshalini Rajagopal, Li Sze Chow, Raveendran Paramesran

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Magnetic Resonance Imaging (MRI) is one of the most important medical imaging modality. Subjective assessment of the image quality is regarded as the gold standard to evaluate MR images. In this study, a database of 210 MR images which contains ten reference images and 200 distorted images is presented. The reference images were distorted with four types of distortions: Rician Noise, Gaussian White Noise, Gaussian Blur and DCT compression. The 210 images were assessed by ten subjects. The subjective scores were presented in Difference Mean Opinion Score (DMOS). The DMOS values were compared with four FR-IQA metrics. We have used Pearson Linear Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) to validate the DMOS values. The high correlation values of PLCC and SROCC shows that the DMOS values are close to the objective FR-IQA metrics.

Keywords: medical resonance (MR) images, difference mean opinion score (DMOS), full reference image quality assessment (FR-IQA)

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Authors: Somkait Udomhunsakul

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In this paper, a multi-focus image fusion method using Spatial Frequency Measurements (SFM) and Wavelet Packet was proposed. The proposed fusion approach, firstly, the two fused images were transformed and decomposed into sixteen subbands using Wavelet packet. Next, each subband was partitioned into sub-blocks and each block was identified the clearer regions by using the Spatial Frequency Measurement (SFM). Finally, the recovered fused image was reconstructed by performing the Inverse Wavelet Transform. From the experimental results, it was found that the proposed method outperformed the traditional SFM based methods in terms of objective and subjective assessments.

Keywords: multi-focus image fusion, wavelet packet, spatial frequency measurement

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

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Authors: Zhuang Yiwen

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The paper examines the challenges facing the Singapore healthcare system related to the tracking and traceability of medical devices. One of the major challenges identified is the lack of a standard coding system for medical devices, which makes it difficult to track them effectively. The paper suggests the use of the Unique Device Identifier (UDI) as a single standard for medical devices to improve tracking and reduce errors. The paper also explores the use of barcoding and image recognition to identify and document medical devices in nursing practices. In nursing practices, the use of barcodes for identifying medical devices is common. However, the information contained in these barcodes is often inconsistent, making it challenging to identify which segment contains the model identifier. Moreover, the use of barcodes may be improved with the use of UDI, but many subsidized accessories may still lack barcodes. The paper suggests that the readiness for UDI and barcode standardization requires standardized information, fields, and logic in electronic medical record (EMR), operating theatre (OT), and billing systems, as well as barcode scanners that can read various formats and selectively parse barcode segments. Nursing workflow and data flow also need to be taken into account. The paper also explores the use of image recognition, specifically the Tesseract OCR engine, to identify and document implants in public hospitals due to limitations in barcode scanning. The study found that the solution requires an implant information database and checking output against the database. The solution also requires customization of the algorithm, cropping out objects affecting text recognition, and applying adjustments. The solution requires additional resources and costs for a mobile/hardware device, which may pose space constraints and require maintenance of sterile criteria. The integration with EMR is also necessary, and the solution require changes in the user's workflow. The paper suggests that the long-term use of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) as a supporting terminology to improve clinical documentation and data exchange in healthcare. SNOMED CT provides a standardized way of documenting and sharing clinical information with respect to procedure, patient and device documentation, which can facilitate interoperability and data exchange. In conclusion, the paper highlights the challenges facing the Singapore healthcare system related to the tracking and traceability of medical devices. The paper suggests the use of UDI and barcode standardization to improve tracking and reduce errors. It also explores the use of image recognition to identify and document medical devices in nursing practices. The paper emphasizes the importance of standardized information, fields, and logic in EMR, OT, and billing systems, as well as barcode scanners that can read various formats and selectively parse barcode segments. These recommendations could help the Singapore healthcare system to improve tracking and traceability of medical devices and ultimately enhance patient safety.

Keywords: medical device tracking, unique device identifier, barcoding and image recognition, systematized nomenclature of medicine clinical terms

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Abdelhalim. N. Mohammed, Mohammed. Y. Esmail

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In the course of recent decades, medical imaging has been dominated by the use of costly film media for review and archival of medical investigation, however due to developments in networks technologies and common acceptance of a standard digital imaging and communication in medicine (DICOM) another approach in light of World Wide Web was produced. Web technologies successfully used in telemedicine applications, the combination of web technologies together with DICOM used to design a web-based and open source DICOM viewer. The Web server allowance to inquiry and recovery of images and the images viewed/manipulated inside a Web browser without need for any preinstalling software. The dynamic site page for medical images visualization and processing created by using JavaScript and HTML5 advancements. The XAMPP ‘apache server’ is used to create a local web server for testing and deployment of the dynamic site. The web-based viewer connected to multiples devices through local area network (LAN) to distribute the images inside healthcare facilities. The system offers a few focal points over ordinary picture archiving and communication systems (PACS): easy to introduce, maintain and independently platforms that allow images to display and manipulated efficiently, the system also user-friendly and easy to integrate with an existing system that have already been making use of web technologies. The wavelet-based image compression technique on which 2-D discrete wavelet transform used to decompose the image then wavelet coefficients are transmitted by entropy encoding after threshold to decrease transmission time, stockpiling cost and capacity. The performance of compression was estimated by using images quality metrics such as mean square error ‘MSE’, peak signal to noise ratio ‘PSNR’ and compression ratio ‘CR’ that achieved (83.86%) when ‘coif3’ wavelet filter is used.

Keywords: DICOM, discrete wavelet transform, PACS, HIS, LAN

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Authors: Claudia Horeanu

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Ever since the first horror films, Count Dracula, the image inspired mainly by the novel written by Bram Stoker, is an almost indispensable character in popular culture. In the shadow of his vampire image is a Romanian ruler, Vlad Țepeș, from Wallachia, a ruler who was also nicknamed Drăculea. The purpose of this research is to analyze the evolution of the image of Vlad Tepeș/Dracula in popular culture, identifying the reasons and themes associated with this character, and to explore how the figure of Vlad Tepeș/Dracula evolved according to social and political changes in different historical periods. It is also believed that there are elements that have remained constant in the depictions of Vlad the Impaler/Dracula.

Keywords: popular culture, dracula, vlad tepes, castlevania, vampire

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Authors: Yuan-Hsiang Chang, Pin-Chi Lin, Li-Der Jeng

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Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).

Keywords: motion detection, motion tracking, trajectory analysis, video surveillance

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Authors: Jefunnie Matahum, Yu-Chi Kuo, Chao-Ming Su, Tzong-Rong Ger

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Magnetic cell labeling is of great importance in various applications in biomedical fields such as cell separation and cell sorting. Since analytical methods for quantification of cell uptake of magnetic nanoparticles (MNPs) are already well established, image analysis on single cell level still needs more characterization. This study reports an alternative non-destructive quantification methods of single-cell uptake of positively charged MNPs. Magnetophoresis experiments were performed to calculate the number of MNPs in a single cell. Mobility of magnetic cells and the area of intracellular MNP stained by Prussian blue were quantified by image processing software. ICP-MS experiments were also performed to confirm the internalization of MNPs to cells. Initial results showed that the magnetic cells incubated at 100 µg and 50 µg MNPs/mL concentration move at 18.3 and 16.7 µm/sec, respectively. There is also an increasing trend in the number and area of intracellular MNP with increasing concentration. These results could be useful in assessing the nanoparticle uptake in a single cell level.

Keywords: magnetic nanoparticles, single cell, magnetophoresis, image analysis

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Authors: Aissa Belmeguenai, Oulaya Berrak, Khaled Mansouri

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In this work, efficiency implementation and security evaluation of the keystream generator of Achterbahn-128 for images encryption and decryption was introduced. The implementation for this simulated project is written with MATLAB.7.5. First of all, two different original images are used to validate the proposed design. The developed program is used to transform the original images data into digital image file. Finally, the proposed program is implemented to encrypt and decrypt images data. Several tests are done to prove the design performance, including visual tests and security evaluation.

Keywords: Achterbahn-128, keystream generator, stream cipher, image encryption, security analysis

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Authors: Elhussaien Elshiekh

Abstract:

A study was performed to management and compare radiation doses and image quality during Lumbar spine PA and Lumbar spine LAT, x- ray radiography using Computed Radiography (CR) and Digital Radiography (DR). Standard exposure factors such as kV, mAs and FFD used for imaging the Lumbar spine anthropomorphic phantom obtained from average exposure factors that were used with CR in five radiology centres. Lumbar spine phantom was imaged using CR and DR systems. Entrance surface air kerma (ESAK) was calculated X-ray tube output and patient exposure factor. Images were evaluated using visual grading system based on the European Guidelines on Quality Criteria for diagnostic radiographic images. The ESAK corresponding to each image was measured at the surface of the phantom. Six experienced specialists evaluated hard copies of all the images, the image score (IS) was calculated for each image by finding the average score of the Six evaluators. The IS value also was used to determine whether an image was diagnostically acceptable. The optimum recommended exposure factors founded here for Lumbar spine PA and Lumbar spine LAT, with respectively (80 kVp,25 mAs at 100 cm FFD) and (75 kVp,15 mAs at 100 cm FFD) for CR system, and (80 kVp,15 mAs at100 cm FFD) and (75 kVp,10 mAs at 100 cm FFD) for DR system. For Lumbar spine PA, the lowest ESAK value required to obtain a diagnostically acceptable image were 0.80 mGy for DR and 1.20 mGy for CR systems. Similarly for Lumbar spine LAT projection, the lowest ESAK values to obtain a diagnostically acceptable image were 0.62 mGy for DR and 0.76 mGy for CR systems. At standard kVp and mAs values, the image quality did not vary significantly between the CR and the DR system, but at higher kVp and mAs values, the DR images were found to be of better quality than CR images. In addition, the lower limit of entrance skin dose consistent with diagnostically acceptable DR images was 40% lower than that for CR images.

Keywords: image quality, dosimetry, radiation protection, optimization, digital radiography, computed radiography

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Authors: Sanjay Singh, Sushil Kumar, Rashmi Jain

Abstract:

Quantum registering is another pattern in computational hypothesis and a quantum mechanical framework has a few helpful properties like Entanglement. We plan to store data concerning the structure and substance of a basic picture in a quantum framework. Consider a variety of n qubits which we propose to use as our memory stockpiling. In recent years classical processing is switched to quantum image processing. Quantum image processing is an elegant approach to overcome the problems of its classical counter parts. Image storage, retrieval and its processing on quantum machines is an emerging area. Although quantum machines do not exist in physical reality but theoretical algorithms developed based on quantum entangled states gives new insights to process the classical images in quantum domain. Here in the present work, we give the brief overview, such that how entangled states can be useful for quantum image storage and retrieval. We discuss the properties of tripartite Greenberger-Horne-Zeilinger and W states and their usefulness to store the shapes which may consist three vertices. We also propose the techniques to store shapes having more than three vertices.

Keywords: Greenberger-Horne-Zeilinger, image storage and retrieval, quantum entanglement, W states

Procedia PDF Downloads 306

Authors: Noor Arda Adrina Daud, Mohd Hanafi Ali

Abstract:

The compensating filter is placed between the patient and X-ray tube to compensate various density and thickness of human body. The main purpose of this project is to study the effect of compensating filter on image quality in lateral projection of thoracolumbar radiography. The study was performed by an X-ray unit where different thicknesses of aluminum were used as compensating filter. Specifically the relationship between thickness of aluminum, density and noise were evaluated. Results show different thickness of aluminum compensating filter improved the image quality of lateral projection thoracolumbar radiography. The compensating filter of 8.2 mm was considered as the optimal filter to compensate the thoracolumbar junction (T12-L1), 1 mm to compensate lumbar region and 5.9 mm to compensate thorax region. The aluminum wedge compensating filter was designed resulting in an acceptable image quality.

Keywords: compensating filter, aluminum, image quality, lateral, thoracolumbar

Procedia PDF Downloads 514