Segmentation Of Lesions From Breast Ultrasound Images Using Deep Convolutional Neural Network

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Segmentation of Lesions from Breast Ultrasound Images Using Deep Convolutional Neural Network

Author : Niranjan Thirusangu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (127 download)

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Book Synopsis Segmentation of Lesions from Breast Ultrasound Images Using Deep Convolutional Neural Network by : Niranjan Thirusangu

Download or read book Segmentation of Lesions from Breast Ultrasound Images Using Deep Convolutional Neural Network written by Niranjan Thirusangu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: To diagnose breast cancer, currently, a radiologist uses a computer-aided diagnosis system which requires them to preselect a region of interest (ROI) as an input for analysis. Breast imaging reporting and data system (BI-RADS) is a standardized reporting process to categorize breast cancer, which is based on several features of the lesion. The BI-RADS scale is based on ultrasound images, which makes the quality of the diagnosis highly dependent on the physician's experience. To minimize human error, we propose solutions based on densely connected deep convolutional neural networks. This thesis discusses various networks based on the U-Net architecture, DenseNet, attention gates, and Mask R-CNN to do semantic segmentation of the lesions from the Breast Ultrasound (BUS) images. Firstly, regular convolution blocks are replaced by dense blocks inside the U-Net (U-DenseNet), to support the learning of intricate patterns of the BUS image which is usually noisy and contaminated with speckles. This resulted in a better performance comparing to the U-Net model, with an F-score of 0.63. Then, attention gates are used in conjunction with U-DenseNet (Attention U-DenseNet) to eliminate the requirement of an explicit localization module. This resulted in a much better improvement comparing to the U-DenseNet with an F-score of 0.75. Thirdly, the previously deduced architecture, Attention U-DenseNet is used as a backbone for the Mask R-CNN architecture, which achieves an F-score of 0.76. Finally, a per-image weighted binary cross-entropy loss function is employed, as the area of the region of interest is usually small.

Ultrasound Image Classification and Segmentation Using Deep Learning Applications

Author : Umar Farooq Mohammad
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Ultrasound Image Classification and Segmentation Using Deep Learning Applications by : Umar Farooq Mohammad

Download or read book Ultrasound Image Classification and Segmentation Using Deep Learning Applications written by Umar Farooq Mohammad and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is one of the most common diseases with a high mortality rate. Early detection and diagnosis using computer-aided methods is considered one of the most efficient ways to control the mortality rate. Different types of classical methods were applied to segment the region of interest from breast ultrasound images. In recent years, Deep learning (DL) based implementations achieved state-of-the-art results for various diseases in both accuracy and inference speed on large datasets. We propose two different supervised learning-based approaches with adaptive optimization methods to segment breast cancer tumours from ultrasound images. The first approach is to switch from Adam to Stochastic Gradient Descent (SGD) in between the training process. The second approach is to employ an adaptive learning rate technique to achieve a rapid training process with element-wise scaling in terms of learning rates. We have implemented our algorithms on four state-of-the-art architectures like AlexNet, VGG19, Resnet50, U-Net++ for the segmentation task of the cancer lesion in the breast ultrasound images and evaluate the Intersection Over Union (IOU) of the four aforementioned architectures using the following methods : 1) without any change, i.e., SGD optimizer, 2) with the substitution of Adam with SGD after three quarters of the total epochs and 3) with adaptive optimization technique. Despite superior training performances of recent DL-based applications on medical ultrasound images, most of the models lacked generalization and could not achieve higher accuracy on new datasets. To overcome the generalization problem, we introduce semi-supervised learning methods using transformers, which are designed for sequence-to-sequence prediction. Transformers have recently emerged as a viable alternative to natural global self-attention processes. However, due to a lack of low-level information, they may have limited translation abilities. To overcome this problem, we created a network that takes advantages of both transformers and UNet++ architectures. Transformers uses a tokenized picture patch as the input sequence for extracting global contexts from a Convolution Neural Network (CNN) feature map. To achieve exact localization, the decoder upsamples the encoded features, which are subsequently integrated with the high-resolution CNN feature maps. As an extension of our implementation, we have also employed the adaptive optimization approach on this architecture to enhance the capabilities of segmenting the breast cancer tumours from ultrasound images. The proposed method achieved better outcomes in comparison to the supervised learning based image segmentation algorithms.

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Author : M. Jorge Cardoso
Publisher : Springer
ISBN 13 : 3319675583
Total Pages : 399 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support by : M. Jorge Cardoso

Download or read book Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support written by M. Jorge Cardoso and published by Springer. This book was released on 2017-09-07 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Automatic Breast Ultrasound Image Segmentation: A Survey

Author : Min Xian
Publisher : Infinite Study
ISBN 13 :
Total Pages : 71 pages
Book Rating : 4./5 ( download)

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Book Synopsis Automatic Breast Ultrasound Image Segmentation: A Survey by : Min Xian

Download or read book Automatic Breast Ultrasound Image Segmentation: A Survey written by Min Xian and published by Infinite Study. This book was released on with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is one of the leading causes of cancer death among women worldwide. In clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging and essential for cancer diagnosis and treatment planning.

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Author : Le Lu
Publisher : Springer
ISBN 13 : 331942999X
Total Pages : 327 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Deep Learning and Convolutional Neural Networks for Medical Image Computing by : Le Lu

Download or read book Deep Learning and Convolutional Neural Networks for Medical Image Computing written by Le Lu and published by Springer. This book was released on 2017-07-12 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

Computer Aided Detection for Breast Lesion in Ultrasound and Mammography

Author : Richa Agarwal
Publisher :
ISBN 13 :
Total Pages : 108 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Computer Aided Detection for Breast Lesion in Ultrasound and Mammography by : Richa Agarwal

Download or read book Computer Aided Detection for Breast Lesion in Ultrasound and Mammography written by Richa Agarwal and published by . This book was released on 2020 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the field of breast cancer imaging, traditional Computer Aided Detection (CAD) systems were designed using limited computing resources and used scanned films (poor image quality), resulting in less robust application process. Currently, with the advancements in technologies, it is possible to perform 3D imaging and also acquire high quality Full-Field Digital Mammogram (FFDM). Automated Breast Ultrasound (ABUS) has been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the 3D nature of the images make the analysis difficult and tedious for radiologists. One of the goals of this thesis is to develop a framework for breast lesion segmentation in ABUS volumes. The 3D lesion volume in combination with texture and contour analysis, could provide valuable information to assist radiologists in the diagnosis.Although ABUS volumes are of great interest, x-ray mammography is still the gold standard imaging modality used for breast cancer screening due to its fast acquisition and cost-effectiveness. Moreover, with the advent of deep learning methods based on Convolutional Neural Network (CNN), the modern CAD Systems are able to learn automatically which imaging features are more relevant to perform a diagnosis, boosting the usefulness of these systems. One of the limitations of CNNs is that they require large training datasets, which are very limited in the field of medical imaging.In this thesis, the issue of limited amount of dataset is addressed using two strategies: (i) by using image patches as inputs rather than full sized image, and (ii) use the concept of transfer learning, in which the knowledge obtained by training for one task is used for another related task (also known as domain adaptation). In this regard, firstly the CNN trained on a very large dataset of natural images is adapted to classify between mass and non-mass image patches in the Screen-Film Mammogram (SFM), and secondly the newly trained CNN model is adapted to detect masses in FFDM. The prospects of using transfer learning between natural images and FFDM is also investigated. Two public datasets CBIS-DDSM and INbreast have been used for the purpose. In the final phase of research, a fully automatic mass detection framework is proposed which uses the whole mammogram as the input (instead of image patches) and provides the localisation of the lesion within this mammogram as the output. For this purpose, OPTIMAM Mammography Image Database (OMI-DB) is used. The results obtained as part of this thesis showed higher performances compared to state-of-the-art methods, indicating that the proposed methods and frameworks have the potential to be implemented within advanced CAD systems, which can be used by radiologists in the breast cancer screening.

A Guide to Convolutional Neural Networks for Computer Vision

Author : Salman Khan
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1681732823
Total Pages : 284 pages
Book Rating : 4.6/5 (817 download)

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Book Synopsis A Guide to Convolutional Neural Networks for Computer Vision by : Salman Khan

Download or read book A Guide to Convolutional Neural Networks for Computer Vision written by Salman Khan and published by Morgan & Claypool Publishers. This book was released on 2018-02-13 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

Deep Learning for Medical Image Analysis

Author : S. Kevin Zhou
Publisher : Academic Press
ISBN 13 : 0323858880
Total Pages : 544 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Deep Learning for Medical Image Analysis by : S. Kevin Zhou

Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-12-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Artificial Intelligence in Healthcare

Author : Lalit Garg
Publisher : Springer Nature
ISBN 13 : 9811662657
Total Pages : 157 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Artificial Intelligence in Healthcare by : Lalit Garg

Download or read book Artificial Intelligence in Healthcare written by Lalit Garg and published by Springer Nature. This book was released on 2021-10-29 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights the analytics and optimization issues in healthcare systems, proposes new approaches, and presents applications of innovative approaches in real facilities. In the past few decades, there has been an exponential rise in the application of swarm intelligence techniques for solving complex and intricate problems arising in healthcare. The versatility of these techniques has made them a favorite among scientists and researchers working in diverse areas. The primary objective of this book is to bring forward thorough, in-depth, and well-focused developments of hybrid variants of swarm intelligence algorithms and their applications in healthcare systems.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Author : Anne L. Martel
Publisher : Springer
ISBN 13 : 9783030597245
Total Pages : 819 pages
Book Rating : 4.5/5 (972 download)

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Book Synopsis Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 by : Anne L. Martel

Download or read book Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 written by Anne L. Martel and published by Springer. This book was released on 2020-10-03 with total page 819 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

Segmentation of Ultrasound Images Using Deep Neural Networks

Author : Shayantonee Dhar Tupor
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Segmentation of Ultrasound Images Using Deep Neural Networks by : Shayantonee Dhar Tupor

Download or read book Segmentation of Ultrasound Images Using Deep Neural Networks written by Shayantonee Dhar Tupor and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: During brachytherapy sessions, medical specialists record ultrasound images of prostate cancer patients and segment these images manually. In the process of analyzing patient records, it is a fundamental task to localize the catheters (needles) in the recorded ultrasound images. Due to the signi cant amount of noise in ultrasound images, localizing multiple catheter positions in ultrasound images is more challenging than similar image segmentation tasks for MRI and CT images. The manual segmentation process is very time-consuming and relies on experienced clinicians. Hence, a tool for the automatic localization of catheters in ultrasound images is highly desirable. In the medical eld, deep learning has gained popularity due to its ability to produce highly accurate detection tools. In order to automate the segmentation of ultrasound images in general and the detection of catheters in particular, we utilize a deep neural network-based architecture named U-net. A 5-fold cross-validation method is applied to evaluate the performance of the U-net based model on the limited dataset. After training a deep neural network, we further improve the resulting detection model by incorporating domain information provided by a medical expert. i The second step of our approach is to generate 3D detections from a combination of the 2D detections, using sequences of 2D images for each patient. Through these 3D detections, we obtain an improvement in accuracy compared to the 2D model.

Deep Learning in Medical Image Analysis

Author : Gobert Lee
Publisher : Springer Nature
ISBN 13 : 3030331288
Total Pages : 184 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Deep Learning in Medical Image Analysis by : Gobert Lee

Download or read book Deep Learning in Medical Image Analysis written by Gobert Lee and published by Springer Nature. This book was released on 2020-02-06 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

Deep Learning Systems for Automated Lesion Detection, Segmentation, and Classification in Mammography

Author : Dina Abdelhafiz
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Deep Learning Systems for Automated Lesion Detection, Segmentation, and Classification in Mammography by : Dina Abdelhafiz

Download or read book Deep Learning Systems for Automated Lesion Detection, Segmentation, and Classification in Mammography written by Dina Abdelhafiz and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is the second leading cause of cancer deaths among women in the USA. Mammography is the preferred screening tool for breast cancer and accounts for the greatest contribution to the early detection of breast cancer. The detection of breast masses in mammogram (MG) images using deep learning (DL) systems is a challenging task due to the varying sizes, shapes, and textures of masses. In this thesis, we propose a novel DL network called residual attention UNet (RAU-Net), the network pays attention to small lesions, and shows superior performance compared to the other state-of-the-arts DL models in detecting and segmenting masses, especially for heterogeneously dense and dense MG images. The proposed RAU-Net model achieves a mean dice coefficient index of 0.98 and mean intersection over union of 0.94. We propose a DL residual network for classification of MG images into benign and malignant that achieved accuracy of 0.95, and AUC of 0.98. We also propose a one-shot multi-input Siamese network that learns features from previous and current year MG images of the same patient to give a better assessment for current year MG images. The detection of mass tumors in dense tissues and, more generally, in dense breasts is often considered more challenging due to the similar visual aspects of normal and abnormal dense tissues. In this thesis, we present a training algorithm that we used to train various kinds of U-Net networks such as RCNN-UNet, AU-Net, RAU-Net,and UNet++ to generate density attention masks that automatically pays attention and gives more weight to tumors in dense regions of MG images. To train and test our models, we collected and pre-processed MG images that come with different resolutions from public repositories and MG images from UCONN health center. In conclusion, we proposed DL systems for lesion detection, segmentation, and classification in mammography that can aid radiologists and serve as a second eye for them.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Author : Anne L. Martel
Publisher : Springer Nature
ISBN 13 : 3030597199
Total Pages : 867 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 by : Anne L. Martel

Download or read book Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 written by Anne L. Martel and published by Springer Nature. This book was released on 2020-10-02 with total page 867 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

Artificial Intelligence in Medical Imaging

Author : Erik R. Ranschaert
Publisher : Springer
ISBN 13 : 3319948784
Total Pages : 373 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Artificial Intelligence in Medical Imaging by : Erik R. Ranschaert

Download or read book Artificial Intelligence in Medical Imaging written by Erik R. Ranschaert and published by Springer. This book was released on 2019-01-29 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

A Fully Automatic Segmentation Method for Breast Ultrasound Images

Author : Juan Shan
Publisher :
ISBN 13 :
Total Pages : 110 pages
Book Rating : 4.:/5 (725 download)

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Book Synopsis A Fully Automatic Segmentation Method for Breast Ultrasound Images by : Juan Shan

Download or read book A Fully Automatic Segmentation Method for Breast Ultrasound Images written by Juan Shan and published by . This book was released on 2011 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is the second leading cause of death of women worldwide. Accurate lesion boundary detection is important for breast cancer diagnosis. Since many crucial features for discriminating benign and malignant lesions are based on the contour, shape, and texture of the lesion, an accurate segmentation method is essential for a successful diagnosis. Ultrasound is an effective screening tool and primarily useful for differentiating benign and malignant lesions. However, due to inherent speckle noise and low contrast of breast ultrasound imaging, automatic lesion segmentation is still a challenging task. This research focuses on developing a novel, effective, and fully automatic lesion segmentation method for breast ultrasound images. By incorporating empirical domain knowledge of breast structure, a region of interest is generated. Then, a novel enhancement algorithm (using a novel phase feature) and a newly developed neutrosophic clustering method are developed to detect the precise lesion boundary. Neutrosophy is a recently introduced branch of philosophy that deals with paradoxes, contradictions, antitheses, and antinomies. When neutrosophy is used to segment images with vague boundaries, its unique ability to deal with uncertainty is brought to bear. In this work, we apply neutrosophy to breast ultrasound image segmentation and propose a new clustering method named neutrosophic l-means. We compare the proposed method with traditional fuzzy c-means clustering and three other well-developed segmentation methods for breast ultrasound images, using the same database. Both accuracy and time complexity are analyzed. The proposed method achieves the best accuracy (TP rate is 94.36%, FP rate is 8.08%, and similarity rate is 87.39%) with a fairly rapid processing speed (about 20 seconds). Sensitivity analysis shows the robustness of the proposed method as well. Cases with multiple-lesions and severe shadowing effect (shadow areas having similar intensity values of the lesion and tightly connected with the lesion) are not included in this study.

Biomedical Data Mining for Information Retrieval

Author : Sujata Dash
Publisher : John Wiley & Sons
ISBN 13 : 111971124X
Total Pages : 450 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Biomedical Data Mining for Information Retrieval by : Sujata Dash

Download or read book Biomedical Data Mining for Information Retrieval written by Sujata Dash and published by John Wiley & Sons. This book was released on 2021-08-24 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.