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A Benchmark For Breast Ultrasound Image Segmentation Busis
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Book Synopsis A Benchmark for Breast Ultrasound Image Segmentation (BUSIS) by : Min Xian
Download or read book A Benchmark for Breast Ultrasound Image Segmentation (BUSIS) written by Min Xian and published by Infinite Study. This book was released on with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Computer-Aided Diagnosis (CAD) systems. Many BUS segmentation approaches have been proposed in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which result in discrepancy in performance comparison.
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.
Book Synopsis A Survey Of Ultrasonography Breast Cancer Image Segmentation Techniques by : Jwan Najeeb Saeed
Download or read book A Survey Of Ultrasonography Breast Cancer Image Segmentation Techniques written by Jwan Najeeb Saeed and published by Infinite Study. This book was released on with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis.
Book Synopsis A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images by : YaozhongLuo
Download or read book A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images written by YaozhongLuo and published by Infinite Study. This book was released on with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality.
Book Synopsis An Adaptive Region Growing based on Neutrosophic Set in Ultrasound Domain for Image Segmentation by : XUE JIANG
Download or read book An Adaptive Region Growing based on Neutrosophic Set in Ultrasound Domain for Image Segmentation written by XUE JIANG and published by Infinite Study. This book was released on with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast tumor segmentation in ultrasound is important for breast ultrasound (BUS) quantitative analysis and clinical diagnosis. Even this topic has been studied for a long time, it is still a challenging task to segment tumor in BUS accurately arising from difficulties of speckle noise and tissue background inconsistence. To overcome these difficulties, we formulate breast tumor segmentation as a classification problem in the neutrosophic set (NS) domain which has been previously studied for removing speckle noise and enhancing contrast in BUS images. The similarity set score and homogeneity value for each pixel have been calculated in the NS domain to characterize each pixel of BUS image. Based on that, the seed regions are selected by an adaptive Otsu-based thresholding method and morphology operations, then an adaptive region growing approach is developed for obtaining candidate tumor regions in NS domain.
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.
Book Synopsis Automated breast cancer detection and classification using ultrasound images: A survey by : H.D.Cheng
Download or read book Automated breast cancer detection and classification using ultrasound images: A survey written by H.D.Cheng and published by Infinite Study. This book was released on with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast.
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.
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.
Book Synopsis Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 by : Hayit Greenspan
Download or read book Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 written by Hayit Greenspan and published by Springer Nature. This book was released on 2023-09-30 with total page 838 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.
Book Synopsis Automatic Segmentation of Breast Ultrasound Images for Finding Tumor Regions by Using a Distance Regularized Level Set Evolution Combined with Texture Feature-based Initialization and Post-processing by : 徐詠璿
Download or read book Automatic Segmentation of Breast Ultrasound Images for Finding Tumor Regions by Using a Distance Regularized Level Set Evolution Combined with Texture Feature-based Initialization and Post-processing written by 徐詠璿 and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computer-aided Detection of Breast Cancer Using Ultrasound Images by : Yanhui Guo
Download or read book Computer-aided Detection of Breast Cancer Using Ultrasound Images written by Yanhui Guo and published by . This book was released on 2010 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ultrasound imaging suffers from severe speckle noise. We propose a novel approach for speckle reduction using 2D homogeneity and directional average filters to remove speckle noise. We transform speckle noise into additive noise using a logarithm transformation. Texture information is employed to describe the speckle characteristics of the image. The homogeneity value is defined using texture information value, and the ultrasound image is transformed into a homogeneity domain from the gray domain. If the homogeneity value is high, the region is homogenous and has less speckle noise. Otherwise, the region is nonhomogenous, and speckle noise occurs. The threshold value is employed to distinguish homogenous regions from regions with speckle noise obtained from a 2D homogeneity histogram according to the maximal entropy principle. A new directional filtering is convoluted to remove noise from pixels in a nonhomogenous region. The filtering processing iterates until the breast ultrasound image is homogenous enough. Experiments show the proposed method improves denoising and edge-preserving capability. We present a novel enhancement algorithm based on fuzzy logic to enhance the fine details of ultrasound image features, while avoiding noise amplification and overenhancement. We take into account both the fuzzy nature of an ultrasound and feature regions on images, which are significant in diagnosis. The maximal entropy principle utilizes the gray-level information to map the image into fuzzy domain. Edge and textural information is extracted in fuzzy domain to describe the features of lesions. The contrast ratio is computed and modified by the local information. Finally, the defuzzification operation transforms the enhanced ultrasound images back to the spatial domain. Experimental results confirm a high enhancement performance including fine details of lesions, without over- or under-enhancement. Identifying object boundaries in ultrasound images is a difficult task. We present a novel automatic segmentation algorithm based on characteristics of breast tissue and eliminating particle swarm optimization (EPSO) clustering analysis, thus transforming the segmentation problem into clustering analysis. Mammary gland characteristics in ultrasound images are utilized, and a step-down threshold technique is employed to locate the mammary gland area. Experimental results demonstrate that the proposed approach increases clustering speed and segments the mass from tissue background with high accuracy.
Book Synopsis Advanced Intelligent Virtual Reality Technologies by : Kazumi Nakamatsu
Download or read book Advanced Intelligent Virtual Reality Technologies written by Kazumi Nakamatsu and published by Springer Nature. This book was released on 2023-01-19 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers a collection of selected works and new research results of scholars and graduate students presented at the 6th International Conference on Artificial Intelligence and Virtual Reality (AIVR 2022) via the Internet, during July 22-24 2022, hosted and organized by Sojo University in conjunction with other three universities and Beijing Huaxia Rongzhi Blockchain Technology Institute. The focus of the book is interdisciplinary in nature and includes research on all aspects of artificial intelligence and virtual reality, from fundamental development to the applied system. The book covers topics such as system techniques, performance, and implementation; content creation and modelling; cognitive aspects, perception, user behaviour; AI technologies; interactions, interactive and responsive environments; AI/VR applications and case studies.
Book Synopsis Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 by : Linwei Wang
Download or read book Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 written by Linwei Wang and published by Springer Nature. This book was released on 2022-09-15 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.
Book Synopsis Biomarker Detection Algorithms and Tools for Medical Imaging or Omic Data by : Fengfeng Zhou
Download or read book Biomarker Detection Algorithms and Tools for Medical Imaging or Omic Data written by Fengfeng Zhou and published by Frontiers Media SA. This book was released on 2022-07-13 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Explainable AI and Susceptibility to Adversarial Attacks in Classification and Segmentation of Breast Ultrasound Images by : Hamza Rasaee
Download or read book Explainable AI and Susceptibility to Adversarial Attacks in Classification and Segmentation of Breast Ultrasound Images written by Hamza Rasaee and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ultrasound is a non-invasive imaging modality that can be conveniently used to classify suspicious breast nodules and potentially detect the onset of breast cancer. Recently, Convolutional Neural Networks (CNN) techniques have shown promising results in classifying ultrasound images of the breast into benign or malignant. However, CNN inference acts as a black-box model, and as such, its decision-making is not interpretable. Therefore, increasing effort has been dedicated to explaining this process, most notably through Gradient-weighted Class Activation Mapping (Grad-CAM) and other techniques that provide visual explanations into inner workings of CNNs. In addition to interpretation, these methods provide clinically important information, such as identifying the location for biopsy or treatment. In this work, we analyze how adversarial assaults that are practically undetectable may be devised to alter these importance maps dramatically. Furthermore, we will show that this change in the importance maps can come with or without altering the classification result, rendering them even harder to detect. As such, care must be taken when using these importance maps to shed light on the inner workings of deep learning. Finally, we utilize Multi-Task Learning (MTL) and propose a new network based on deep residual networks to improve the classification accuracies. Our sensitivity and specificity values are comparable to the state of the art results.
Book Synopsis Computer Vision by : Md Atiqur Rahman Ahad
Download or read book Computer Vision written by Md Atiqur Rahman Ahad and published by CRC Press. This book was released on 2024-07-30 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications. This book highlights various core challenges as well as solutions by leading researchers in the field. It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI. Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and providing a range of perspectives from top researchers around the world. A substantial compilation of ideas and state-of-the-art solutions, it will be of great benefit to students, researchers, and industry practitioners.
