A Survey Of Ultrasonography Breast Cancer Image Segmentation Techniques

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A Survey Of Ultrasonography Breast Cancer Image Segmentation Techniques

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Author : Jwan Najeeb Saeed
language : en
Publisher: Infinite Study
Release Date :

A Survey Of Ultrasonography Breast Cancer Image Segmentation Techniques written by Jwan Najeeb Saeed and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.

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.

Automatic Breast Ultrasound Image Segmentation A Survey

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Author : Min Xian
language : en
Publisher: Infinite Study
Release Date :

Automatic Breast Ultrasound Image Segmentation A Survey written by Min Xian and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

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.

Automated Breast Cancer Detection And Classification Using Ultrasound Images A Survey

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Author : H.D.Cheng
language : en
Publisher: Infinite Study
Release Date :

Automated Breast Cancer Detection And Classification Using Ultrasound Images A Survey written by H.D.Cheng and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

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.

A Benchmark For Breast Ultrasound Image Segmentation Busis

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Author : Min Xian
language : en
Publisher: Infinite Study
Release Date :

A Benchmark For Breast Ultrasound Image Segmentation Busis written by Min Xian and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

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.

A Novel Segmentation Approach Combining Region And Edge Based Information For Ultrasound Images

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Author : YaozhongLuo
language : en
Publisher: Infinite Study
Release Date :

A Novel Segmentation Approach Combining Region And Edge Based Information For Ultrasound Images written by YaozhongLuo and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

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.

A Fully Automatic Segmentation Method For Breast Ultrasound Images

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Author : Juan Shan
language : en
Publisher:
Release Date : 2011

A Fully Automatic Segmentation Method For Breast Ultrasound Images written by Juan Shan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.

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.

Innovations In Biomedical Engineering

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Author : Marek Gzik
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Innovations In Biomedical Engineering written by Marek Gzik and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.

This book presents the latest developments in the field of biomedical engineering and includes practical solutions and strictly scientific considerations. The development of new methods of treatment, advanced diagnostics or personalized rehabilitation requires close cooperation of experts from many fields, including, among others, medicine, biotechnology and finally biomedical engineering. The latter, combining many fields of science, such as computer science, materials science, biomechanics, electronics not only enables the development and production of modern medical equipment, but also participates in the development of new directions and methods of treatment. The presented monograph is a collection of scientific papers on the use of engineering methods in medicine. The topics of the work include both practical solutions and strictly scientific considerations expanding knowledge about the functioning of the human body. We believe that the presented works will have an impact on the development of the field of science, which is biomedical engineering, constituting a contribution to the discussion on the directions of development of cooperation between doctors, physiotherapists and engineers. We would also like to thank all the people who contributed to the creation of this monograph—both the authors of all the works and those involved in technical works.

Ultrasound Image Classification And Segmentation Using Deep Learning Applications

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Author : Umar Farooq Mohammad
language : en
Publisher:
Release Date : 2022

Ultrasound Image Classification And Segmentation Using Deep Learning Applications written by Umar Farooq Mohammad and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

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.

An Adaptive Region Growing Based On Neutrosophic Set In Ultrasound Domain For Image Segmentation

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Author : XUE JIANG
language : en
Publisher: Infinite Study
Release Date :

An Adaptive Region Growing Based On Neutrosophic Set In Ultrasound Domain For Image Segmentation written by XUE JIANG and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.

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.

Performance Analysis Of Em Mpm And K Means Clustering In 3d Ultrasound Breast Image Segmentation

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Author : Huanyi Yang
language : en
Publisher:
Release Date : 2013

Performance Analysis Of Em Mpm And K Means Clustering In 3d Ultrasound Breast Image Segmentation written by Huanyi Yang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Bayesian statistical decision theory categories.

Mammographic density is an important risk factor for breast cancer, detecting and screening at an early stage could help save lives. To analyze breast density distribution, a good segmentation algorithm is needed. In this thesis, we compared two popularly used segmentation algorithms, EM-MPM and K-means Clustering. We applied them on twenty cases of synthetic phantom ultrasound tomography (UST), and nine cases of clinical mammogram and UST images. From the synthetic phantom segmentation comparison we found that EM-MPM performs better than K-means Clustering on segmentation accuracy, because the segmentation result fits the ground truth data very well (with superior Tanimoto Coefficient and Parenchyma Percentage). The EM-MPM is able to use a Bayesian prior assumption, which takes advantage of the 3D structure and finds a better localized segmentation. EM-MPM performs significantly better for the highly dense tissue scattered within low density tissue and for volumes with low contrast between high and low density tissues. For the clinical mammogram, image segmentation comparison shows again that EM-MPM outperforms K-means Clustering since it identifies the dense tissue more clearly and accurately than K-means. The superior EM-MPM results shown in this study presents a promising future application to the density proportion and potential cancer risk evaluation.