Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/16541
Title: Mammogram image analysis - a soft computing approach
Researcher: Lochanambal K P
Guide(s): Karnan M
Keywords: Soft computing
Breast Carcinoma
Upload Date: 28-Feb-2014
University: Mother Teresa Womens University
Completed Date: 25/06/2013
Abstract: In this thesis CAD system is designed to diagnose breast cancer through mammograms, using image processing techniques along with swarm intelligence tools and clustering method, such as Particle Swarm Optimization Algorithm (PSO), Artificial Bee Colony Optimization (ABC), Fuzzy Spatial Dependency C-Means (FSDCM) Clustering Algorithm and Artificial Neural Network. The detection of micro calcifications is performed in two phases: preprocessing and segmentation in the first phase and feature extraction, selection and classification in the second phase. 161 pairs of digitized mammograms obtained from the Mammography Image Analysis Society (MIAS) database is used to design the proposed diagnosing system. Initially, the film artifacts and labels are eliminated from the mammogram images and median filter is applied to eliminate the high frequency components from the image. Then the mammogram images are normalized. The suspicious region or micro calcifications is segmented using Fuzzy Spatial Dependency CMeans (FSDCM) Clustering Algorithm (FSDCM ), Particle Swarm Optimization Algorithm (PSO) and Markov Random Field (MRF) hybrid with Artificial Bee Colony Optimization (ABC) algorithm for mammogram images. The MRF and ABC based image segmentation method is a process seeking the optimal labeling of the pixels. The textural features can be extracted from the segmented mammogram image to classify the micro calcifications into benign, malignant or normal. The Surrounding Region Dependency Matrix textural analysis method is used to extract the fourteen Haralick features from the segmented image. The features are selected from the extracted set of features using Genetic Algorithm (GA), PSO and ABC.
Pagination: 182p.
URI: http://hdl.handle.net/10603/16541
Appears in Departments:Department of Computer Science

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01_title.pdfAttached File5.56 MBAdobe PDFView/Open
02_certificate.pdf9.44 kBAdobe PDFView/Open
03_abstract.pdf10.66 kBAdobe PDFView/Open
04_declaration.pdf9.35 kBAdobe PDFView/Open
05_acknowledgement.pdf9.44 kBAdobe PDFView/Open
06_contents.pdf23.2 kBAdobe PDFView/Open
07_list_of_tables.pdf13.29 kBAdobe PDFView/Open
08_list_of_figures.pdf39.56 kBAdobe PDFView/Open
09_abbreviations.pdf231.47 kBAdobe PDFView/Open
10_chapter1.pdf1.08 MBAdobe PDFView/Open
11_chapter2.pdf344.46 kBAdobe PDFView/Open
12_chapter3.pdf4.79 MBAdobe PDFView/Open
13_chapter4.pdf2.59 MBAdobe PDFView/Open
14_chapter5.pdf198.95 kBAdobe PDFView/Open
15_chapter6.pdf381.77 kBAdobe PDFView/Open
16_conclusion.pdf10.32 kBAdobe PDFView/Open
17_bibliography.pdf55.21 kBAdobe PDFView/Open


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