Search results for: nerve segmentation

Authors: Emmanuel Kamal Aziz Saba, Gihan Abd El-Lateif Younis El-Tantawi, Mohammed Hamdy Zahran, Ibrahim Khalil Ibrahim, Mohammed Abd El-Salam Shehata, Hussein Al-Moghazy Sultan, Medhat

Abstract:

Pelvic floor electrophysiological tests are essential for assessment of patients with obstructed defecation. The present study was conducted to determine the different patterns of pelvic floor electrophysiology that are associated with obstructed defecation. The present cross sectional study included 25 patients with obstructed defecation. A control group of 20 apparently healthy subjects were included. All patients were subjected to history taking, clinical examination, proctosigmoidoscopy, lateral proctography (evacuation proctography), dynamic pelvic magnetic resonance imaging, anal manometry and electrophysiological studies. Electrophysiological studies were including pudendal nerve motor conduction study, pudendo-anal reflex, needle electromyography of external anal sphincter and puborectalis muscles, pudendal somatosensory evoked potential and tibial somatosensory evoked potential. The control group was subjected to electrophysiological studies which included pudendal nerve motor conduction study, pudendo-anal reflex, pudendal somatosensory evoked potential and tibial somatosensory evoked potential. The most common pelvic floor electrodiagnostic pattern characteristics of obstructed defecation was pudendal neuropathy, denervation and anismus of external anal sphincter and puborectalis with complete interference pattern of external anal sphincter and puborectalis at squeezing and cough and no localized defect in external anal sphincter. In conclusion, there were characteristic pelvic floor electrodiagnostic patterns associated with obstructed defecation.

Keywords: obstructed defecation, pudendal nerve terminal motor latency, pudendoanal reflex, sphincter electromyography

Procedia PDF Downloads 409

Authors: Rohit Semwal, Shivam Arora, Saurav, Sangita Roy

Abstract:

This work proposes a supervised framework with deep convolutional neural networks (CNNs) for vision-based sign language recognition of static gestures. Our approach addresses the acquisition and segmentation of correct inputs for the CNN-based classifier. Microsoft Kinect™ sensor, despite complex environmental conditions, can track hands efficiently. Skin Colour based segmentation is applied on cropped images of hands in different poses, used to depict different sign language gestures. The segmented hand images are used as an input for our classifier. The CNN classifier proposed in the paper is able to classify the input images with a high degree of accuracy. The system was trained and tested on 39 static sign language gestures, including 26 letters of the alphabet and 13 commonly used words. This paper includes a problem definition for building the proposed system, which acts as a sign language translator between deaf/mute and the rest of the society. It is then followed by a focus on reviewing existing knowledge in the area and work done by other researchers. It also describes the working principles behind different components of CNNs in brief. The architecture and system design specifications of the proposed system are discussed in the subsequent sections of the paper to give the reader a clear picture of the system in terms of the capability required. The design then gives the top-level details of how the proposed system meets the requirements.

Keywords: sign language, CNN, HCI, segmentation

Procedia PDF Downloads 121

Authors: Arjun Prakash, Vinutha H., Karthik N.

Abstract:

BACKGROUND: Carpal tunnel syndrome (CTS) is a common entrapment neuropathy with an estimated prevalence of 0.6 - 5.8% in the general adult population. It is caused by compression of the Median Nerve (MN) at the wrist as it passes through a narrow osteofibrous canal. Presently, the diagnosis is established by the clinical symptoms and physical examination and Nerve conduction study (NCS) is used to assess its severity. However, it is considered to be painful, time consuming and expensive, with a false-negative rate between 16 - 34%. Ultrasonography (USG) is now increasingly used as a diagnostic tool in CTS due to its non-invasive nature, increased accessibility and relatively low cost. Elastography is a newer modality in USG which helps to assess stiffness of tissues. However, there is limited available literature about its applications in peripheral nerves. OBJECTIVES: Our objectives were to measure the Cross-Sectional Area (CSA) and elasticity of MN at the carpal tunnel using Grey scale Ultrasonography (USG), Strain Elastography (SE) and Shear Wave Elastography (SWE). We also made an attempt to independently evaluate the role of Gray scale USG, SE and SWE in grading the severity of CTS, keeping NCS as the gold standard. MATERIALS AND METHODS: After approval from the Institutional Ethics Review Board, we conducted a comparative cross sectional study for a period of 18 months. The participants were divided into two groups. Group A consisted of 54 patients with clinically diagnosed CTS who underwent NCS, and Group B consisted of 50 controls without any clinical symptoms of CTS. All Ultrasound examinations were performed on SAMSUNG RS 80 EVO Ultrasound machine with 2 - 9 Mega Hertz linear probe. In both groups, CSA of the MN was measured on Grey scale USG, and its elasticity was measured at the carpal tunnel (in terms of Strain ratio and Shear Modulus). The variables were compared between both groups by using ‘Independent t test’, and subgroup analyses were performed using one-way analysis of variance. Receiver operating characteristic curves were used to evaluate the diagnostic performance of each variable. RESULTS: The mean CSA of the MN was 13.60 + 3.201 mm2 and 9.17 + 1.665 mm2 in Group A and Group B, respectively (p < 0.001). The mean SWE was 30.65 + 12.996 kPa and 17.33 + 2.919 kPa in Group A and Group B, respectively (p < 0.001), and the mean Strain ratio was 7.545 + 2.017 and 5.802 + 1.153 in Group A and Group B respectively (p < 0.001). CONCLUSION: The combined use of Gray scale USG, SE and SWE is extremely useful in grading the severity of CTS and can be used as a painless and cost-effective alternative to NCS. Early diagnosis and grading of CTS and effective treatment is essential to avoid permanent nerve damage and functional disability.

Keywords: carpal tunnel, ultrasound, elastography, nerve conduction study

Procedia PDF Downloads 63

Authors: A. B. Robert, Adam Pramadia, Calvin Andika

Abstract:

The main purpose of this study is to explore consumer loyalty characteristic of mobile phone category in Indonesia. Second, this research attempts to identify consumer segment and to explore their profile in each segment as the basis of marketing strategy formulation. This study used some tools of multivariate analysis such as discriminant analysis and cluster analysis. Discriminate analysis used to discriminate consumer loyal and not loyal by using particular variables. Cluster analysis used to reveal various segment in mobile phone category. In addition to having better customer understanding in each segment, this study used descriptive analysis and cross tab analysis in each segment defined by cluster analysis. This study expected several findings. First, consumer can be divided into two large group of loyal versus not loyal by set of variables. Second, this study identifies customer segment in mobile phone category. Third, exploring customer profile in each segment that has been identified. This study answer a call for additional empirical research into different product categories. Therefore, a replication research is advisable. By knowing the customer loyalty characteristic, and deep analysis of their consumption behavior and profile for each segment, this study is very advisable for high impact marketing strategy development. This study contributes body of knowledge by adding empirical study of consumer loyalty, segmentation analysis in mobile phone category by multiple brand analysis.

Keywords: customer loyalty, segmentation, marketing strategy, discriminant analysis, cluster analysis, mobile phone

Procedia PDF Downloads 567

Authors: Dimitrios Binas, Marianna Konidari, Charis Bourgioti, Lia Angela Moulopoulou, Theodore Economopoulos, George Matsopoulos

Abstract:

High grade ovarian epithelial cancer (OEC) is fatal gynecological cancer and the poor prognosis of this entity is closely related to considerable intratumoral genetic heterogeneity. By examining imaging data, it is possible to assess the heterogeneity of tumorous tissue. This study proposes a methodology for aligning, segmenting and finally visualizing information from various magnetic resonance imaging series in order to construct 3D models of heterogeneity maps from the same tumor in OEC patients. The proposed system may be used as an adjunct digital tool by health professionals for personalized medicine, as it allows for an easy visual assessment of the heterogeneity of the examined tumor.

Keywords: image segmentation, ovarian epithelial cancer, quantitative characteristics, image registration, tumor visualization

Procedia PDF Downloads 184

Authors: Kishore Kumar R., Kaustav Sengupta, Shalini Sood Sehgal, Poornima Santhanam

Abstract:

Fashion is a human expression that is constantly changing. One of the prime factors that consistently influences fashion is the change in colour preferences. The role of colour in our everyday lives is very significant. It subconsciously explains a lot about one’s mindset and mood. Analyzing the colours by extracting them from the outfit images is a critical study to examine the individual’s/consumer behaviour. Several research works have been carried out on extracting colours from images, but to the best of our knowledge, there were no studies that extract colours to specific apparel and identify colour patterns geographically. This paper proposes a framework for accurately extracting colours from T-shirt images and predicting dominant colours geographically. The proposed method consists of two stages: first, a U-Net deep learning model is adopted to segment the T-shirts from the images. Second, the colours are extracted only from the T-shirt segments. The proposed method employs the iMaterialist (Fashion) 2019 dataset for the semantic segmentation task. The proposed framework also includes a mechanism for gathering data and analyzing India’s general colour preferences. From this research, it was observed that black and grey are the dominant colour in different regions of India. The proposed method can be adapted to study fashion’s evolving colour preferences.

Keywords: colour analysis in t-shirts, convolutional neural network, encoder-decoder, k-means clustering, semantic segmentation, U-Net model

Procedia PDF Downloads 81

Authors: Jared Cheves, Amanda DeChent, Joyce Pan

Abstract:

Total knee replacements (TKAs) are one of the most common but painful surgical procedures performed in the United States. Currently, the gold standard for postoperative pain management is the utilization of opioids. However, in the wake of the opioid epidemic, the healthcare system is attempting to reduce opioid consumption by trialing innovative opioid sparing analgesic techniques such as continuous peripheral nerve blocks (CPNB) and continuous periarticular infiltration (CPAI). The alleviation of pain, particularly during the first 72 hours postoperatively, is of utmost importance due to its association with delayed recovery, impaired rehabilitation, immunosuppression, the development of chronic pain, the development of rebound pain, and decreased patient satisfaction. While both CPNB and CPAI are being used today, there is limited evidence comparing the two to the current standard of care or to each other. An extensive literature review was performed to explore the safety profiles and effectiveness of CPNB and CPAI in reducing reported pain scores and decreasing opioid consumption. The literature revealed the usage of CPNB contributed to lower pain scores and decreased opioid use when compared to opioid-only control groups. Additionally, CPAI did not improve pain scores or decrease opioid consumption when combined with a multimodal analgesic (MMA) regimen. When comparing CPNB and CPAI to each other, neither unanimously lowered pain scores to a greater degree, but the literature indicates that CPNB decreased opioid consumption more than CPAI. More research is needed to further cement the efficacy of CPNB and CPAI as standard components of MMA in TKA procedures. In addition, future research can also focus on novel catheter-free applications to reduce the complications of continuous catheter analgesics.

Keywords: total knee arthroplasty, continuous peripheral nerve blocks, continuous periarticular infiltration, opioid, multimodal analgesia

Procedia PDF Downloads 64

Authors: Jamison Duckworth, Shankarachary Ragi

Abstract:

Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

Procedia PDF Downloads 95

Authors: Lae-Jeong Park, Kyusoo Chung, Jungho Moon

Abstract:

This paper introduces an effective method of segmenting Korean text (place names in Korean) from a Korean road sign image. A Korean advanced directional road sign is composed of several types of visual information such as arrows, place names in Korean and English, and route numbers. Automatic classification of the visual information and extraction of Korean place names from the road sign images make it possible to avoid a lot of manual inputs to a database system for management of road signs nationwide. We propose a series of problem-specific heuristics that correctly segments Korean place names, which is the most crucial information, from the other information by leaving out non-text information effectively. The experimental results with a dataset of 368 road sign images show 96% of the detection rate per Korean place name and 84% per road sign image.

Keywords: segmentation, road signs, characters, classification

Procedia PDF Downloads 419

Authors: Raid Hommady, Waseem Al-Talalwah, Shorok Al Dorazi, Roger Soames

Abstract:

The iliolumbar artery is a regular branch of posterior division of the internal iliac artery. The present study investigate 82 specimens to identify the origin of iliolumbar artery. The present study targets the sciatic nerve root supply from iliolumbar artery based on its origin and course. In present study, the ililumbar artery arose from the posterior division of internal iliac artery in 52.2%. In few cases, it arose from dorsomedial aspect of the internal iliac artery in 28.8%. In few cases, the iliolumbar artery arose from the dorsal aspects of the internal iliac artery as well as from the common and external iliac artery 1.7%. Also, the iliolumbar artery arose from the sciatic artery as well as from superior and inferior gluteal arteries in 5.1%. Conversely, it found to be congenital absent in 8.5%. Therefore, the posterior trunk of the internal iliac artery is the most common origin of the iliolumbar artery. With the origin variability of the iliolumbar artery, there is a vascular supply variability of the lumbosacral trunk and sacral root of sciatic nerve. The iliolumbar artery provides vascular supply for lumbosacral trunk 57.3% in whereas the sacral root in 5.1%. As a result, surgeons should pay attention to these variations to decrease iatrogenic fault.

Keywords: iliolumbar, sciatic artery, internal iliac, external iliac, posterior division

Procedia PDF Downloads 291

Authors: Konstantinos C. Kartsakalis, Angeliki Skoura, Vasileios Megalooikonomou

Abstract:

The fuzzy composition of objects depicted in images acquired through MR imaging or the use of bio-scanners has often been a point of controversy for field experts attempting to effectively delineate between the visualized objects. Modern approaches in medical image segmentation tend to consider fuzziness as a characteristic and inherent feature of the depicted object, instead of an undesirable trait. In this paper, a novel technique for efficient image retrieval in the context of images in which segmented objects are either crisp or fuzzily bounded is presented. Moreover, the proposed method is applied in the case of multiple, even conflicting, segmentations from field experts. Experimental results demonstrate the efficiency of the suggested method in retrieving similar objects from the aforementioned categories while taking into account the fuzzy nature of the depicted data.

Keywords: fuzzy object, fuzzy image segmentation, motion descriptors, MRI imaging, object-based image retrieval

Procedia PDF Downloads 352

Authors: N. H. Harun, A. S. Abdul Nasir, M. Y. Mashor, R. Hassan

Abstract:

Leukaemia is a blood cancer disease that contributes to the increment of mortality rate in Malaysia each year. There are two main categories for leukaemia, which are acute and chronic leukaemia. The production and development of acute leukaemia cells occurs rapidly and uncontrollable. Therefore, if the identification of acute leukaemia cells could be done fast and effectively, proper treatment and medicine could be delivered. Due to the requirement of prompt and accurate diagnosis of leukaemia, the current study has proposed unsupervised pixel segmentation based on clustering algorithm in order to obtain a fully segmented abnormal white blood cell (blast) in acute leukaemia image. In order to obtain the segmented blast, the current study proposed three clustering algorithms which are k-means, fuzzy c-means and moving k-means algorithms have been applied on the saturation component image. Then, median filter and seeded region growing area extraction algorithms have been applied, to smooth the region of segmented blast and to remove the large unwanted regions from the image, respectively. Comparisons among the three clustering algorithms are made in order to measure the performance of each clustering algorithm on segmenting the blast area. Based on the good sensitivity value that has been obtained, the results indicate that moving k-means clustering algorithm has successfully produced the fully segmented blast region in acute leukaemia image. Hence, indicating that the resultant images could be helpful to haematologists for further analysis of acute leukaemia.

Keywords: acute leukaemia images, clustering algorithms, image segmentation, moving k-means

Procedia PDF Downloads 263

Authors: Malika Ourabia

Abstract:

A new and efficient method is presented for the analysis of arbitrarily shaped discontinuities. The discontinuities is characterized using a hybrid spectral/numerical technique. This structure presents an arbitrary number of ports, each one with different orientation and dimensions. This article presents a hybrid method based on multimode contour integral and mode matching techniques. The process is based on segmentation and dividing the structure into key building blocks. We use the multimode contour integral method to analyze the blocks including irregular shape discontinuities. Finally, the multimode scattering matrix of the whole structure can be found by cascading the blocks. Therefore, the new method is suitable for analysis of a wide range of waveguide problems. Therefore, the present approach can be applied easily to the analysis of any multiport junctions and cascade blocks. The accuracy of the method is validated comparing with results for several complex problems found in the literature. CPU times are also included to show the efficiency of the new method proposed.

Keywords: segmentation, s parameters, simulation, optimization

Procedia PDF Downloads 504

Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

Abstract:

Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

Procedia PDF Downloads 365

Authors: Kayode A. Olaniyi, Tola. M. Osifeko, Adeola A. Ogunleye

Abstract:

Text line segmentation is an important step in document image processing. It represents a labeling process that assigns the same label using distance metric probability to spatially aligned units. Text line detection techniques have successfully been implemented mainly in printed documents. However, processing of the handwritten texts especially unconstrained documents has remained a key problem. This is because the unconstrained hand-written text lines are often not uniformly skewed. The spaces between text lines may not be obvious, complicated by the nature of handwriting and, overlapping ascenders and/or descenders of some characters. Hence, text lines detection and segmentation represents a leading challenge in handwritten document image processing. Text line detection methods that rely on the traditional global projection profile of the text document cannot efficiently confront with the problem of variable skew angles between different text lines. Hence, the formulation of a horizontal line as a separator is often not efficient. This paper presents a technique to segment a handwritten document into distinct lines of text. The proposed algorithm starts, by partitioning the initial text image into columns, across its width into chunks of about 5% each. At each vertical strip of 5%, the histogram of horizontal runs is projected. We have worked with the assumption that text appearing in a single strip is almost parallel to each other. The algorithm developed provides a sliding window through the first vertical strip on the left side of the page. It runs through to identify the new minimum corresponding to a valley in the projection profile. Each valley would represent the starting point of the orientation line and the ending point is the minimum point on the projection profile of the next vertical strip. The derived text-lines traverse around any obstructing handwritten vertical strips of connected component by associating it to either the line above or below. A decision of associating such connected component is made by the probability obtained from a distance metric decision. The technique outperforms the global projection profile for text line segmentation and it is robust to handle skewed documents and those with lines running into each other.

Keywords: connected-component, projection-profile, segmentation, text-line

Procedia PDF Downloads 96

Authors: Mehrnoosh Omati, Mahmod Reza Sahebi

Abstract:

The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

Keywords: coupled Markov random field (MRF), environment, object-based analysis, polarimetric SAR (PolSAR) images

Procedia PDF Downloads 196

Authors: Vahid Anari, Leila Shahmohammadi

Abstract:

Diagnosing and interpreting manually from a large cohort dataset of immunohistochemically stained tissue of tumors using an optical microscope involves subjectivity and also is tedious for pathologist specialists. Moreover, digital pathology today represents more of an evolution than a revolution in pathology. In this paper, we develop and test an unsupervised algorithm that can automatically enhance the IHC image of a meningioma tumor and classify cells into positive (proliferative) and negative (normal) cells. A dataset including 150 images is used to test the scheme. In addition, a new adaptive color image enhancement method is proposed based on a vector directional filter (VDF) and statistical properties of filtering the window. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: digital pathology, cell segmentation, immunohistochemically, noise reduction

Procedia PDF Downloads 45

Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

Abstract:

In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

Procedia PDF Downloads 105

Authors: Behnaz Sohani, Sahand Shahalinezhad, Amir Rahmani, Aliyu Aliyu

Abstract:

Recently, medical imaging and specifically medical image processing is becoming one of the most dynamically developing areas of medical science. It has led to the emergence of new approaches in terms of the prevention, diagnosis, and treatment of various diseases. In the process of diagnosis of lung cancer, medical professionals rely on computed tomography (CT) scans, in which failure to correctly identify masses can lead to incorrect diagnosis or sampling of lung tissue. Identification and demarcation of masses in terms of detecting cancer within lung tissue are critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. This has been performed employing CT images based on multilevel thresholding. The proposed technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of lung cancer is obtained with 96.3% accuracy and 87.25%. The purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current feature extraction method to achieve more accurate resulting images, including further details available to machine vision systems to recognise objects in lung CT scan images.

Keywords: lung cancer detection, image segmentation, lung computed tomography (CT) images, medical image processing

Procedia PDF Downloads 60

Authors: Yun Chen, Qihang Liu, Catherine Ticehurst, Chandrama Sarker, Fazlul Karim, Dave Penton, Ashmita Sengupta

Abstract:

The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect with water. The ICESAT-2 track generates multiple VSs as it crosses the different water bodies. The difficulties are particularly pronounced in large river basins where there are many tributaries and meanders often adjacent to each other. One challenge is to split photon segments along a beam to accurately partition them to extract only the true representative water height for individual elements. As far as we can establish, there is no automated procedure to make this distinction. Earlier studies have relied on human intervention or river masks. Both approaches are unsatisfactory solutions where the number of intersections is large, and river width/extent changes over time. We describe here an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparison with river water level observations at 10 different stations on 37 different dates along the Lower Murray River, Australia. The congruence is very high and without detectable bias. In addition, we compared different outlier removal methods on the mean WSE calculation at VSs post the auto-segmentation process. All four outlier removal methods perform almost equally well with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189m) and MAE (0.130–0.142m). Overall, the auto-segmentation method developed here is an effective and efficient approach to deriving accurate mean WSE at river VSs. It provides a much better way of facilitating the application of ICESAT-2 ATL13 altimetry to rivers compared to previously reported studies. Therefore, the findings of our study will make a significant contribution towards the retrieval of hydraulic parameters, such as water surface slope along the river, water depth at cross sections, and river channel bathymetry for calculating flow velocity and discharge from remotely sensed imagery at large spatial scales.

Keywords: lidar sensor, virtual station, cross section, mean water surface elevation, beam/track segmentation

Procedia PDF Downloads 35

Authors: Safak Isik, Ozalp Vayvay

Abstract:

Strategic partnerships with suppliers play a vital role for the long-term value-based supply chain. This strategic collaboration keeps still being one of the top priority of many business organizations in order to create more additional value; benefiting mainly from supplier’s specialization, capacity and innovative power, securing supply and better managing costs and quality. However, many organizations encounter difficulties in initiating, developing and managing those partnerships and many attempts result in failures. One of the reasons for such failure is the incompatibility of members of this partnership or in other words wrong supplier selection which emphasize the significance of the selection process since it is the beginning stage. An effective selection process of strategic suppliers is critical to the success of the partnership. Although there are several research studies to select the suppliers in literature, only a few of them is related to strategic supplier selection for long-term partnership. The purpose of this study is to propose a conceptual model for the selection of strategic partnership suppliers. A two-stage approach has been used in proposed model incorporating first segmentation and second selection. In the first stage; considering the fact that not all suppliers are strategically equal and instead of a long list of potential suppliers, Kraljic’s purchasing portfolio matrix can be used for segmentation. This supplier segmentation is the process of categorizing suppliers based on a defined set of criteria in order to identify types of suppliers and determine potential suppliers for strategic partnership. In the second stage, from a pool of potential suppliers defined at first phase, a comprehensive evaluation and selection can be performed to finally define strategic suppliers considering various tangible and intangible criteria. Since a long-term relationship with strategic suppliers is anticipated, criteria should consider both current and future status of the supplier. Based on an extensive literature review; strategical, operational and organizational criteria have been determined and elaborated. The result of the selection can also be used to determine suppliers who are not ready for a partnership but to be developed for strategic partnership. Since the model is based on multiple criteria for both stages, it provides a framework for further utilization of Multi-Criteria Decision Making (MCDM) techniques. The model may also be applied to a wide range of industries and involve managerial features in business organizations.

Keywords: Kraljic’s matrix, purchasing portfolio, strategic supplier selection, supplier collaboration, supplier partnership, supplier segmentation

Procedia PDF Downloads 218

Authors: A. Bensaid, T. Mostephaoui, R. Nedjai

Abstract:

A considerable area of Algerian lands is threatened by the phenomenon of wind erosion. For a long time, wind erosion and its associated harmful effects on the natural environment have posed a serious threat, especially in the arid regions of the country. In recent years, as a result of increases in the irrational exploitation of natural resources (fodder) and extensive land clearing, wind erosion has particularly accentuated. The extent of degradation in the arid region of the Algerian Mecheria department generated a new situation characterized by the reduction of vegetation cover, the decrease of land productivity, as well as sand encroachment on urban development zones. In this study, we attempt to investigate the potential of remote sensing and geographic information systems for detecting the spatial dynamics of the ancient dune cords based on the numerical processing of LANDSAT images (5, 7, and 8) of three scenes 197/37, 198/36 and 198/37 for the year 2020. As a second step, we prospect the use of geospatial techniques to monitor the progression of sand dunes on developed (urban) lands as well as on the formation of sandy accumulations (dune, dunes fields, nebkha, barkhane, etc.). For this purpose, this study made use of the semi-automatic processing method for the dynamic segmentation of images with very high spatial resolution (SENTINEL-2 and Google Earth). This study was able to demonstrate that urban lands under current conditions are located in sand transit zones that are mobilized by the winds from the northwest and southwest directions.

Keywords: land development, GIS, segmentation, remote sensing

Procedia PDF Downloads 122

Authors: Manoj Deepak, N. Mathivanan, K. Venkatachalam

Abstract:

Nerve root stenosis is one of the main cause for back pain. There are many methods both conservative and surgical to treat this disease. It is pertinent to decompress the spine to a proper extent so as to avoid the recurrence of symptoms. But too much of an aggressive approach also has its disadvantages. We present one of the methods to effectively decompress the nerve with better results. Our study was carried out in 52 patients with foramina stenosis between 2008 to 2011.We carried out the surgical procedure of shaving off the medial part of the facet joint so as to decompress the root. We selected those patients who had symptoms of claudication for more than 2 years. They had no signs of instability and they underwent conservative treatment for a period of 2 months before the procedure. Oswersty scoring was used to record the functional level of the patient before and after the procedure. All patients were followed up for a period of minimum 2.5 years. After evaluation for a minimum of 2.5 years, 34 patients had no evidence of recurrence of symptoms with improvement in the functional level.7 patients complained of minimal pain but their functional quality had improved postop. Six patients had symptoms of lumbar canal disease which reduced with conservative treatment. 5 patients required spinal fusion surgeries in the later period. Conclusion: Thus, we can effectively conclude that our procedure is safe and effective in reducing the symptoms in those patients with neurogenic claudication.

Keywords: facetectoemy, stenosis, decompression, Lumbar Foraminal Stenosis, hemi-facetectomy

Procedia PDF Downloads 321

Authors: Deborah Zelinsky

Abstract:

The retina filters millions of incoming signals into a smaller amount of exiting optic nerve fibers that travel to different portions of the brain. Most of the signals are for eyesight (called "image-forming" signals). However, there are other faster signals that travel "elsewhere" and are not directly involved with eyesight (called "non-image-forming" signals). This article centers on the neurons of the optic nerve connecting to parts of the limbic system. Eye care providers are currently looking at parvocellular and magnocellular processing pathways without realizing that those are part of an enormous "galaxy" of all the body systems. Lenses are modifying both non-image and image-forming pathways, taking A.M. Skeffington's seminal work one step further. Almost 100 years ago, he described the Where am I (orientation), Where is It (localization), and What is It (identification) pathways. Now, among others, there is a How am I (animation) and a Who am I (inclination, motivation, imagination) pathway. Classic eye testing considers pupils and often assesses posture and motion awareness, but classical prescriptions often overlook limbic involvement in visual processing. The limbic system is composed of the hippocampus, amygdala, hypothalamus, and anterior nuclei of the thalamus. The optic nerve's limbic connections arise from the intrinsically photosensitive retinal ganglion cells (ipRGC) through the "retinohypothalamic tract" (RHT). There are two main hypothalamic nuclei with direct photic inputs. These are the suprachiasmatic nucleus and the paraventricular nucleus. Other hypothalamic nuclei connected with retinal function, including mood regulation, appetite, and glucose regulation, are the supraoptic nucleus and the arcuate nucleus. The retino-hypothalamic tract is often overlooked when we prescribe eyeglasses. Each person is different, but the lenses we choose are influencing this fast processing, which affects each patient's aiming and focusing abilities. These signals arise from the ipRGC cells that were only discovered 20+ years ago and do not address the campana retinal interneurons that were only discovered 2 years ago. As eyecare providers, we are unknowingly altering such factors as lymph flow, glucose metabolism, appetite, and sleep cycles in our patients. It is important to know what we are prescribing as the visual processing evaluations expand past the 20/20 central eyesight.

Keywords: neuromodulation, retinal processing, retinohypothalamic tract, limbic system, visual processing

Procedia PDF Downloads 50

Authors: Lamees Nasser, Yago Diez, Robert Martí, Joan Martí, Ibrahim Sadek

Abstract:

Automated 3D breast ultrasound (ABUS) screening is a novel modality in medical imaging because of its common characteristics shared with other ultrasound modalities in addition to the three orthogonal planes (i.e., axial, sagittal, and coronal) that are useful in analysis of tumors. In the literature, few automatic approaches exist for typical tasks such as segmentation or registration. In this work, we deal with two problems concerning ABUS images: nipple and rib detection. Nipple and ribs are the most visible and salient features in ABUS images. Determining the nipple position plays a key role in some applications for example evaluation of registration results or lesion follow-up. We present a nipple detection algorithm based on color and shape of the nipple, besides an automatic approach to detect the ribs. In point of fact, rib detection is considered as one of the main stages in chest wall segmentation. This approach consists of four steps. First, images are normalized in order to minimize the intensity variability for a given set of regions within the same image or a set of images. Second, the normalized images are smoothed by using anisotropic diffusion filter. Next, the ribs are detected in each slice by analyzing the eigenvalues of the 3D Hessian matrix. Finally, a breast mask and a probability map of regions detected as ribs are used to remove false positives (FP). Qualitative and quantitative evaluation obtained from a total of 22 cases is performed. For all cases, the average and standard deviation of the root mean square error (RMSE) between manually annotated points placed on the rib surface and detected points on rib borders are 15.1188 mm and 14.7184 mm respectively.

Keywords: Automated 3D Breast Ultrasound, Eigenvalues of Hessian matrix, Nipple detection, Rib detection

Procedia PDF Downloads 305

Authors: Sundip Charmode, Simmi Mehra, Sudhir Kushwaha, Shalom Philip, Pratik Amrutiya, Ranjna Jangal

Abstract:

Introduction: Quadrangular space syndrome involves compression of the axillary nerve and posterior circumflex humeral artery and its management in few cases, requires surgical decompression. The current study reviews the surgical approaches used in the decompression of neurovascular structures and presents our reflections and recommendations. Methods: Four human cadavers, in the Department of Anatomy were used for dissection of the Axillae and the Scapular region by the senior residents of the Department of Anatomy and Department of Orthopedics, who dissected quadrangular space in the eight upper limbs, using anterior and posterior surgical approaches. Observations: Posterior approach to identify the quadrangular space and secure its contents was recognized as the easier and much quicker method by both the Anatomy and Orthopedic residents, but it may result in increased postoperative morbidity. Whereas the anterior (Delto-pectoral) approach involves more skill but reduces postoperative morbidity. Conclusions: Anterior (Delto-pectoral) approach with suggested modifications can prove as an effective method in surgical decompression of quadrangular space syndrome. The authors suggest more cadaveric studies to facilitate anatomists and surgeons with the opportunities to practice and evaluate older and newer surgical approaches.

Keywords: surgical approach, anatomical approach, decompression, axillary nerve, quadrangular space

Procedia PDF Downloads 144

Authors: B. Kishore Kumar, Pogula Rakesh, T. Kishore Kumar

Abstract:

The music analysis is a part of the audio content analysis used to analyze the music by using the different features of audio signal. In music analysis, the first step is to divide the music signal to different sections based on the feature profiles of the music signal. In this paper, we present a music segmentation technique that will effectively segmentize the signal and thresholding technique to remove silence from the percussive sounds produced by percussive instruments, which uses two features of music, namely signal energy and spectral centroid. The proposed method impose thresholds on both the features which will vary depends on the music signal. Depends on the threshold, silence part is removed and the segmentation is done. The effectiveness of the proposed method is analyzed using MATLAB.

Keywords: percussive sounds, spectral centroid, spectral energy, silence removal, feature extraction

Procedia PDF Downloads 562

Authors: Suparman

Abstract:

Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model.

Keywords: piecewise regression, bayesian, reversible jump MCMC, segmentation

Procedia PDF Downloads 344

Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

Abstract:

Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

Procedia PDF Downloads 68

Authors: S. alireza Mirghasemi, Shervin Rashidinia, Mohammadsaleh Sadeghi, Mohsen Talebizadeh, Narges Rahimi Gabaran, S. Shahin Eftekhari, Sara Shahmoradi

Abstract:

Purpose: There are various pin-in-plaster methods for treating distal radius fractures. This study is meant to introduce a modified technique of pin-in-plaster. Materials and methods: Fifty-four patients with distal radius fractures were followed up for one year. Patients were excluded if they had type B fractures according to AO classification, multiple injuries or pathological fractures, and were treated more than 7 days after injury. Range of motion and functional results were evaluated. Radiographic parameters including radial inclination, tilt, and height, were measured preoperatively and postoperatively. Results: The average radial tilt was 10.6° and radial height was 10.2 mm at the sixth month postoperatively. Three cases of pin tract infection were recorded, who were treated totally with oral antibiotics. There was no case of pin loosening. Of total 73 patients underwent surgery, three cases of radial nerve irritation were recorded at the time of cast removal. All of them resolved at the 6th month follow up. No median nerve compression and carpal tunnel syndrome have found. We also had no case of tendon injury. Conclusion: Our modified technique is effective to restore anatomic congruity and maintain reduction.

Keywords: distal radius fracture, percutaneous pinning, pin-in-plaster, modified method of pin-in-plaster, operative treatment

Procedia PDF Downloads 480