ICAR Research Data Repository for Knowledge Management
Segmentation of Ultrasound Breast Images
Shodhganga@INFLIBNET
View Archive Info| Field | Value | |
| Title |
Segmentation of Ultrasound Breast Images
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| Contributor |
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| Subject |
algorithms
Markov Random Field segmentation Thresholding Ultrasound |
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| Description |
Breast cancer is the leading cause of death amongst women worldwide. However early detection can reduce the mortality rate substantially. B-mode ultrasound (US) imaging modality is one of the reliable tools used to detect and diagnose the breast cancer. In fact it can detect the cancer at an early stage where effective treatment is possible. Computer aided diagnosis system helps radiologists in the newlinedetection and diagnosis of the region of interest accurately. Image segmentation is one of the vital steps used in automated computer aided diagnosis system. Indeed accuracy of the overall diagnosis is newlinedepends on the detection and demarcation of region of interest. However US image characteristics such as varying echogenicity,heterogeneous texture patterns, irregular shape, fuzzy tumor newlineboundary makes segmentation challenging. Moreover poor quality images due to inherent artifact such as speckle, attenuation makes segmentation more challenging. Many methods are suggested in the literature for speckle removal before implementation of region extraction algorithms. However in order to reduce the complexity of the segmentation, we suggested to omit speckle removal step newlinedeliberately from the proposed segmentation algorithm. Therefore we newlineuse original ultrasound images directly as input to the segmentation process.During the literature survey and problem formulation we observed that US breast images have extreme random gray level distribution. This phenomenon is the major hurdle in achievement of accurate segmentation. We found that unsupervised learning (clustering) has a great potential in solving such problems. Here we proposed total six algorithms based on clustering for segmentation. Initially we proposed thresholding based clustering on texture feature images. In this method texture has been analyzed by using selected texture parameters proposed by Haralick. — |
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| Date |
2017-08-14T08:00:17Z
2017-08-14T08:00:17Z 27/07/2011 30/12/2014 30/12/2014 |
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| Type |
Ph.D.
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| Identifier |
http://hdl.handle.net/10603/166342
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| Language |
English
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| Relation |
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| Rights |
university
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| Format |
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— DVD |
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| Coverage |
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| Publisher |
Mumbai
Narsee Monjee Institute of Management Studies Department of Electronic and Telecommunication Engineering |
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| Source |
University
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