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Full-Text Articles in Computer Engineering
Detection Of Various Dental Conditions On Dental Panoramic Radiography Using Faster R-Cnn, Shih Lun Chen, Tsung Yi Chen, Yi Cheng Mao, Szu Yin Lin, Ya Yun Huang, Chiung An Chen, Yuan Jin Lin, Mian Heng Chuang, Patricia Angela R. Abu
Detection Of Various Dental Conditions On Dental Panoramic Radiography Using Faster R-Cnn, Shih Lun Chen, Tsung Yi Chen, Yi Cheng Mao, Szu Yin Lin, Ya Yun Huang, Chiung An Chen, Yuan Jin Lin, Mian Heng Chuang, Patricia Angela R. Abu
Department of Information Systems & Computer Science Faculty Publications
The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists' efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% …
A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead
A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead
Mathematics, Physics, and Computer Science Faculty Articles and Research
In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …
Impact Of Image Segmentation Techniques On Celiac Disease Classification Usingscale Invariant Texture Descriptors For Standard Flexible Endoscopic Systems, Manarbek Saken, Munkhtsetseg Banzragch Yağci, Nejat Yumuşak
Impact Of Image Segmentation Techniques On Celiac Disease Classification Usingscale Invariant Texture Descriptors For Standard Flexible Endoscopic Systems, Manarbek Saken, Munkhtsetseg Banzragch Yağci, Nejat Yumuşak
Turkish Journal of Electrical Engineering and Computer Sciences
Celiac disease (CD) is quite common and is a proximal small bowel disease that develops as a permanentintolerance to gluten and other cereal proteins in cereals. It is considered as one of the most di?icult diseases to diagnose.Histopathological evidence of small bowel biopsies taken during endoscopy remains the gold standard for diagnosis.Therefore, computer-aided detection (CAD) systems in endoscopy are a newly emerging technology to enhance thediagnostic accuracy of the disease and to save time and manpower. For this reason, a hybrid machine learning methodshave been applied for the CAD of celiac disease. Firstly, a context-based optimal multilevel thresholding technique wasemployed …
Freelabel: A Publicly Available Annotation Tool Based On Freehand Traces, Philipe A. Dias, Zhou Shen, Amy Tabb, Henry P. Medeiros
Freelabel: A Publicly Available Annotation Tool Based On Freehand Traces, Philipe A. Dias, Zhou Shen, Amy Tabb, Henry P. Medeiros
Electrical and Computer Engineering Faculty Research and Publications
Large-scale annotation of image segmentation datasets is often prohibitively expensive, as it usually requires a huge number of worker hours to obtain high-quality results. Abundant and reliable data has been, however, crucial for the advances on image understanding tasks recently achieved by deep learning models. In this paper, we introduce FreeLabel, an intuitive open-source web interface that allows users to obtain high-quality segmentation masks with just a few freehand scribbles, in a matter of seconds. The efficacy of FreeLabel is quantitatively demonstrated by experimental results on the PASCAL dataset as well as on a dataset from the agricultural domain. Designed …
A Scale Space Local Binary Pattern (Sslbp) – Based Feature Extraction Framework To Detect Bones From Knee Mri Scans, Jinyeong Mun
A Scale Space Local Binary Pattern (Sslbp) – Based Feature Extraction Framework To Detect Bones From Knee Mri Scans, Jinyeong Mun
Electronic Theses and Dissertations
The medical industry is currently working on a fully autonomous surgical system, which is considered a novel modality to go beyond technical limitations of conventional surgery. In order to apply an autonomous surgical system to knees, one of the primarily responsible areas for supporting the total weight of human body, accurate segmentation of bones from knee Magnetic Resonance Imaging (MRI) scans plays a crucial role. In this paper, we propose employing the Scale Space Local Binary Pattern (SSLBP) feature extraction, a variant of local binary pattern extractions, for detecting bones from knee images. The proposed methods consist of two phases. …
Breast Boundary Segmentation In Thermography Images Based On Random Walkers, Mehrdad Moghbel, Syamsiah Mashohor, Rozi Mahmud, M.Iqbal Bin Saripan, Suzana Abd Hamid, Suraini Mohamad Sani, Saiful Nizam
Breast Boundary Segmentation In Thermography Images Based On Random Walkers, Mehrdad Moghbel, Syamsiah Mashohor, Rozi Mahmud, M.Iqbal Bin Saripan, Suzana Abd Hamid, Suraini Mohamad Sani, Saiful Nizam
Turkish Journal of Electrical Engineering and Computer Sciences
Breast and areola boundary detection and segmentation present the biggest challenge in breast segmentation from thermography images, as breast boundaries, especially in the upper quadrants of the breast, are nonexistent. Many segmentation approaches have been proposed for breast segmentation, such as active contours and snakes, circular Hough transforms, and live wires, but these methods often fail to achieve satisfactory results. With recent advances in image processing techniques, new segmentation concepts are being developed, such as random walkers, which have received high interest from the medical imaging community. In this study, 91 images acquired utilizing a FLIR A320 thermal camera are …
An Adaptive Clustering Segmentation Algorithm Based On Fcm, Jun Yang, Yun-Sheng Ke, Mao-Zheng Wang
An Adaptive Clustering Segmentation Algorithm Based On Fcm, Jun Yang, Yun-Sheng Ke, Mao-Zheng Wang
Turkish Journal of Electrical Engineering and Computer Sciences
The cluster number and the initial clustering centers must be reasonably set before the analysis of clustering in most cases. Traditional clustering segmentation algorithms have many shortcomings, such as high reliance on the specially established initial clustering center, tendency to fall into the local maximum point, and poor performance with multithreshold values. To overcome these defects, an adaptive fuzzy C-means segmentation algorithm based on a histogram (AFCMH), which synthesizes both main peaks of the histogram and optimized Otsu criterion, is proposed. First, the main peaks of the histogram are chosen by operations like histogram smoothing, merging of adjacent peaks, and …
Image Segmentation For Quantification Of Air-Water Interface In Micro-Ct Soil Images, Kranthi Kumar Potteti
Image Segmentation For Quantification Of Air-Water Interface In Micro-Ct Soil Images, Kranthi Kumar Potteti
UNLV Theses, Dissertations, Professional Papers, and Capstones
Soils are complex environments comprising various biological (roots, water, air etc) and physical constituents (minerals, aggregates, etc). Synchrotron radiation based X-ray microtomography (XMT) is widely used in extracting qualitative and quantitative information regarding spatial distribution of biological and physical soil constituents. Segmentation of these micro-CT soil images is of interest to geologists, hydrologists, civil and petroleum engineers and soil scientists. In this present work, we study and implement segmentation algorithms for microhydrology studies, specifically for soil water conductivity. Three well-known image segmentation algorithms are studied for evaluating their performance for the task. We demonstrate the problems and ways to segment …
Semi-Automatic Image Segmentation For Volumetric Visualization Of Pelvis Ct Scan-Images, Suprijanto Suprijanto, Farida I. Muchtadi, Irwan Setiawan
Semi-Automatic Image Segmentation For Volumetric Visualization Of Pelvis Ct Scan-Images, Suprijanto Suprijanto, Farida I. Muchtadi, Irwan Setiawan
Makara Journal of Technology
Semi-Automatic Image Segmentation for Volumetric Visualization of Pelvis CT Scan-Images. The current development of computerized tomography (CT) has enable us to obtain cross sectional image using multi slicing techniques in an order of few seconds. The obtained images represent several tissue structures on cross section slice being imaged. One challenge to help diagnosis using CT images is extracting an anatomic structure of interest using a method of image segmentation and volumetric visualization with the assistance of computers. In case of volumetric visualization of pelvis bones extracted from multi-slice CT images, whole images which are containing part of pelvis bone structures …
An Algorithm For Image Clustering And Compression, Meti̇n Kaya
An Algorithm For Image Clustering And Compression, Meti̇n Kaya
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a new approach to image compression based on fuzzy clustering. This new approach includes pre-filtering, and fuzzy logic image enhancing to reduce undesirable noise effects on segmentation result; separation of image into 4x4 blocks and two dimensional discrete cosine transform; obtaining of peak values of cosine membership functions by combining of performing the zig-zag method with discrete cosine transform coefficients; obtaining of membership values and cluster centroids; and finally, creation of segmented image and compression. After applying the new method on sample images at different number of clusters, better compression ratio, performing time and good validity measure …