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Title: Detection of microcalcification in mammograms using soft segmentation techniques
Researcher: Subash chandra bose J
Guide(s): Shankarkumar K R
Keywords: Information and communication engineering
Soft segmentation techniques
Wavelet transform
Upload Date: 1-Oct-2014
University: Anna University
Completed Date: 01/10/2013
Abstract: One of the most efficient methods for breast cancer early detection is mammography A new method for detection and classification of micro calcifications is presented here It can be done in four stages first the preprocessing stage deals with noise removal and normalized image Second stage Fuzzy c Means clustering is used for segmentation and pectoral muscle extraction using area calculation and finally micro calcifications detection Third stage consists of two dimensional discrete wavelet transforms are extracted from the detection of micro calcifications Then nine statistical features are calculated from the LL band of the wavelet transform Finally the extracted features are fed as input to the Artificial Neural Network and are classified into normal or abnormal images The given classification approach is applied to a database of 322 dense mammographic images originating from the MIAS database The results are analyzed using MATLAB
Pagination: xvi, 150p.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File99.88 kBAdobe PDFView/Open
02_certificates.pdf3.29 MBAdobe PDFView/Open
03_abstract.pdf14.69 kBAdobe PDFView/Open
04_acknowledgement.pdf6.1 kBAdobe PDFView/Open
05_contents.pdf22.42 kBAdobe PDFView/Open
06_chapter1.pdf1 MBAdobe PDFView/Open
07_chapter2.pdf73.03 kBAdobe PDFView/Open
08_chapter3.pdf1.66 MBAdobe PDFView/Open
09_chapter4.pdf881.52 kBAdobe PDFView/Open
10_chapter5.pdf91.14 kBAdobe PDFView/Open
11_chapter6.pdf18.23 kBAdobe PDFView/Open
12_appendix.pdf324.47 kBAdobe PDFView/Open
13_references.pdf66.19 kBAdobe PDFView/Open
14_publications.pdf9.51 kBAdobe PDFView/Open
15_vitae.pdf5.75 kBAdobe PDFView/Open

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