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| Abstract: | Breast cancer detection is an important social requisite as it is the leading cause of death due to cancer among women. The mortality rate of breast cancer is second among all cancers. The cause for breast cancer is not known to date and early detection & treatment are the only means to reduce breast cancer related deaths. Mammography is the main radiological tool that is employed for identifying breast cancer at the earliest stage. Computer aided techniques have great relevance in detection of abnormalities from mammographic images, as often the features associated with various abnormalities are difficult to detect and might be missed by even trained radiologists. In addition, when screening mammography is employed, a large number of mammographic images need to be checked for signs of abnormality, justifying the use of computer aided diagnosis. Three problems are addressed in this thesis: delineation of the pectoral muscle region by properly identifying the pectoral muscle boundary, detection of architectural distortion and enhancement of microcalcification features in the mammographic images. Two novel methods were developed for identifying the pectoral muscle boundary from mediolateral oblique view mammograms that employed multiscale decomposition and local segmentation. The breast area is extracted after this step following the removal of the Pectoral muscle region. The breast abnormalities are searched for in this region. Architectural distortion is the most commonly missed abnormality in mammograms. A novel method for detecting architectural distortion is proposed in this thesis that employs geometrical features obtained from selected edge structures in the mammographic image. These features are used to train a feedforward neural network classifier initialized using metaheuristic algorithms for better classification. Microcalcification is another breast cancer symptom which is ii said to be the most commonly occurring. However the visibility of the microcalcification structures is often poor, especially when they are located in dense parenchymal tissues. Therefore an algorithm is proposed to enhance such features, employing the singularities, viz. zero-crossings and modulus maxima of coefficients obtained after computing the contourlet transform of the mammographic image. Contourlet transform is employed for the directional information it provides. |
| URI: | http://dyuthi.cusat.ac.in/purl/5149 |
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| Dyuthi-T2183.pdf | (10.07Mb) |
| Abstract: | A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases |
| Description: | Biomedical Signal Processing and Control 8 (2013) 667– 674 |
| URI: | http://dyuthi.cusat.ac.in/purl/4228 |
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| Spectral cluste ... ication from brain MRI.pdf | (1.561Mb) |
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