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Title: Digital mammogram segmentation and tumor detection using artificial neural network
Researcher: Ireaneus Anna Rejani, Y.
Guide(s): Tamarai Selvi, S.
Keywords: Mammogram, segmentation, Discrete wavelet transform, fractal dimension analysis, Breast cancer
Upload Date: 28-Nov-2013
University: Anna University
Completed Date: 
Abstract: Breast cancer is one of the leading causes of cancer related death among women. Women over 40 years of age and those who have family history are recommended to take mammograms regularly for screening. Manual examination of mammograms may result in misdiagnosis due to human errors caused by visual fatigue. The main aim of this research is to improve the accuracy of tumor detection from mammograms. In this research, four approaches have been designed and implemented for tumor detection. The general structure of all these approaches of tumor detection consists of enhancement, segmentation, feature extraction and classification stages. The proposed approach1 for tumor detection relies on contour searching method that uses an iterative procedure. The approach 2 of tumor detection in mammogram follows the scheme of enhancement techniques using wavelet decomposition and morphological operations. In the approach 3, the digitized mammogram is complemented using image negative and dimensionally reduced using Discrete Wavelet Transform (DWT). In the approach 4, smoothing filtering is used to remove noise from the complemented image and DWT to reduce the dimension of the image. The roughness value is measured using Fractal dimension analysis. Adaptive Thresholding with Fast Segmentation is used for segmentation process, Back Propagation Neural network (BPN), Radial Basis Function network (RBF) and Support Vector Machine (SVM) classifiers for classification. Mini mammographic data base from Mammographic Image Analysis Society (MIAS) has been used for the implementation of these approaches. From the analysis of the obtained results it is found that BPN provides more accurate results comparing to RBF and SVM. The performance accuracy is increased in the approach 4. This approach provides a sensitivity of 99.1% which is higher comparing to the other approaches and the existing approaches. The result shows that the proposed approach 4 is efficient. newline newline newline
Pagination: xvi, 117
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File50.65 kBAdobe PDFView/Open
02_certificates.pdf847.72 kBAdobe PDFView/Open
03_abstract.pdf16.1 kBAdobe PDFView/Open
04_acknowledgement.pdf15.56 kBAdobe PDFView/Open
05_contents.pdf54.01 kBAdobe PDFView/Open
06_chapter 1.pdf70.93 kBAdobe PDFView/Open
07_chapter 2.pdf59.86 kBAdobe PDFView/Open
08_chapter 3.pdf64.65 kBAdobe PDFView/Open
09_chapter 4.pdf143.69 kBAdobe PDFView/Open
10_chapter 5.pdf52.18 kBAdobe PDFView/Open
11_chapter 6.pdf66.55 kBAdobe PDFView/Open
13_chapter 8.pdf70.33 kBAdobe PDFView/Open
14_chapter 9.pdf132.81 kBAdobe PDFView/Open
15_chapter 10.pdf29.42 kBAdobe PDFView/Open
16_references.pdf48.35 kBAdobe PDFView/Open
17_publications.pdf18.47 kBAdobe PDFView/Open
18_vitae.pdf11.52 kBAdobe PDFView/Open

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