Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/263289
Title: Computer Aided Analysis of Mammograms for Breast Diseases
Researcher: Pawar Meenakshi Mukund
Guide(s): Talbar S N
Keywords: Electronics and telecommunication
University: Swami Ramanand Teerth Marathwada University
Completed Date: 11/11/2018
Abstract: Breast cancer is the life-threatening disease that invades into surrounding tissues or newlineextends to distant body parts. It is the most prevalent cancer amongst women across newlineworldwide. The regular screening of breast is essential to investigate cancer risk in its newlineearly stage. The early detection of breast cancer is necessary to initiate radiation therapy newlineto stop or control further growth of it. Mammography is considered as a gold standard newlinefor screening of breast, which can detect lump or tumor before actually it is felt. The newlinemammogram images are X-ray images, which are captured using low dose X-ray or very newlinelimited count of X-ray photon. Therefore, mammogram images are poor contrast images, newlineweak edges and it also consists of random fluctuations, so that interpretation of newlinemammogram is one of the difficult tasks for radiologist. The study shows that 10-30% newlinebreast cancers are misinterpreted by radiologist. Therefore, Computer Aided diagnosis newlineand detection (CAD) system act as a second reader for radiologist, which performs newlineautomatic segmentation and detection of breast cancer. The contrast enhancement of newlinemammogram is an important preprocessing stage for CAD system to better visualize newlinecontents of mammogram. In this thesis, a novel image fusion using Local entropy newlinemaximization technique for mammogram contrast enhancement is proposed. The newlineproposed contrast enhancement technique enhances contrast and improves the edge newlinecontents. To overcome the problem of false positives detected in automatic breast tumor newlinesegmentation, this thesis proposes the false positive reduction as a posterior step of CAD newlinesystem. Since, the segmented suspicious region may not be abnormal called as false newlinepositive region, which consumes radiologist time and sometimes may results into newlineunnecessary biopsies. Therefore, in the proposed system, the initial suspicious region is newlinesegmented using self-organizing map (SOM) network, then the detection of false positives newlineand subsequently removal or reduction of false positives are achieved using feature newlineextraction an
Pagination: 145p
URI: http://hdl.handle.net/10603/263289
Appears in Departments:Faculty of Engineering

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01_title.pdfAttached File225.16 kBAdobe PDFView/Open
02_certificate.pdf200.49 kBAdobe PDFView/Open
03_abstract.pdf301.61 kBAdobe PDFView/Open
04_declaration.pdf407.25 kBAdobe PDFView/Open
05_acknowledgements.pdf304.38 kBAdobe PDFView/Open
06_contents.pdf379.8 kBAdobe PDFView/Open
07_list_of_tables.pdf303.3 kBAdobe PDFView/Open
08_list_of_figures.pdf374.59 kBAdobe PDFView/Open
09_abbreviations.pdf202.01 kBAdobe PDFView/Open
10_chapter 1.pdf1.12 MBAdobe PDFView/Open
11_chapter 2.pdf634.4 kBAdobe PDFView/Open
12_chapter 3.pdf1.83 MBAdobe PDFView/Open
13_chapter 4.pdf2.13 MBAdobe PDFView/Open
14_chapter 5.pdf1.83 MBAdobe PDFView/Open
15_conclusion.pdf343.01 kBAdobe PDFView/Open
16_summery.pdf247.72 kBAdobe PDFView/Open
17_bibliography.pdf345.23 kBAdobe PDFView/Open


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