Now showing items 1-4 of 4
| Abstract: | Breast cancer is the most common non - skin malignancy in women and a leading cause of female morality. A potentially important strategy for reducing this menace is the detection at an early stage . The invention of non-invasive and non-ionizing microwave technique, to reveal the internal structure of biological objects was a break through in the field of medical diagnostics. Electrical properties of biological tissues and their interaction with electromagmetic waves have direct impact on human life. This thesis focuses on theoretical and experimental investigations of active microwave imaging techniques for breast cancer detection. |
| Description: | Department of Electronics, Cochin University of Science and Technology |
| URI: | http://dyuthi.cusat.ac.in/purl/2124 |
| Files | Size |
|---|---|
| Dyuthi-T0394.pdf | (13.57Mb) |
| 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 |
| Files | Size |
|---|---|
| Dyuthi-T2183.pdf | (10.07Mb) |
| Abstract: | The paper summarizes the design and implementation of a quadratic edge detection filter, based on Volterra series, for enhancing calcifications in mammograms. The proposed filter can account for much of the polynomial nonlinearities inherent in the input mammogram image and can replace the conventional edge detectors like Laplacian, gaussian etc. The filter gives rise to improved visualization and early detection of microcalcifications, which if left undetected, can lead to breast cancer. The performance of the filter is analyzed and found superior to conventional spatial edge detectors |
| Description: | Data Science & Engineering (ICDSE), 2012 International Conference on |
| URI: | http://dyuthi.cusat.ac.in/purl/4486 |
| Files | Size |
|---|---|
| Enhancement of ... based Quadratic Filter.pdf | (311.2Kb) |
| Abstract: | In this paper, a novel fast method for modeling mammograms by deterministic fractal coding approach to detect the presence of microcalcifications, which are early signs of breast cancer, is presented. The modeled mammogram obtained using fractal encoding method is visually similar to the original image containing microcalcifications, and therefore, when it is taken out from the original mammogram, the presence of microcalcifications can be enhanced. The limitation of fractal image modeling is the tremendous time required for encoding. In the present work, instead of searching for a matching domain in the entire domain pool of the image, three methods based on mean and variance, dynamic range of the image blocks, and mass center features are used. This reduced the encoding time by a factor of 3, 89, and 13, respectively, in the three methods with respect to the conventional fractal image coding method with quad tree partitioning. The mammograms obtained from The Mammographic Image Analysis Society database (ground truth available) gave a total detection score of 87.6%, 87.6%, 90.5%, and 87.6%, for the conventional and the proposed three methods, respectively. |
| Description: | Journal of Digital Imaging, Vol 23, No 5 (October), 2010: pp 538-546 |
| URI: | http://dyuthi.cusat.ac.in/purl/4573 |
| Files | Size |
|---|---|
| A New Fast Frac ... ications in Mammograms.pdf | (229.2Kb) |
Now showing items 1-4 of 4
Dyuthi Digital Repository Copyright © 2007-2011 Cochin University of Science and Technology. Items in Dyuthi are protected by copyright, with all rights reserved, unless otherwise indicated.