Volume & Issue no: Volume 3, Issue 4, July - August 2014
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Title: |
CAD Based System for Automatic Detection & Classification of Suspicious Lesions in Mammograms |
Author Name: |
Veena, Jayakrishna V |
Abstract: |
Abstract
It is very difficult to detect the breast cancer in its earlier
stage as it shows symptoms gradually. So it is very important
to keep track of any abnormality present in the breast. Breast
cancer is the leading cause of death due to cancer among the
women. Computer Aided Diagnosis (CAD) is an important
tool in assisting doctors in the early detection of cancers.
Earlier diagnosis of breast cancer is of great importance as
far as modern medical treatments are concerned.
Mammography is currently used for the reliable and early
detection of cancers in breast. This paper presents, a novel
algorithm for CAD based breast cancer analysis. The
proposed system develops a systematic scheme to classify the
breast cancer into normal, benign and malignant type. Here,
AI based Training and Classification method using Back
Propagation Neural Network classifier is proposed. These
classification techniques find even the minute area of
suspicion which can be either cancerous or fatty and this area
is used for classification. Adaptive thresholding and multiresolution
analysis are used for lesion detection. Multiresolution
analysis provides a framework for interpreting the
image information. The proposed system uses wavelets
transform, coarse segmentation, fine segmentation and area
Analysis. Wavelet transform is used for multi-resolution
analysis. Wavelet based method is used for coarse
segmentation and window based method is used for fine
segmentation. For obtaining the rough localization of
suspicious lesions, coarse segmentation is used. A window
based adaptive thresholding is done on the resultant image to
get the fine segmentation. Fine segmentation is used for
accurately spotting and detecting even the fine lesions. Area
analysis of white pixel is performed in fine segmented image.
The proposed method is tested with images in the Mini
Mammography database of the Mammographic Image
Analysis Society (MIAS).
Keywords: CAD, neural network classifier, multi-resolution
analysis, wavelet transform, coarse segmentation, fine
segmentation, mass classification |
Cite this article: |
Veena, Jayakrishna V , "
CAD Based System for Automatic Detection & Classification of Suspicious Lesions in Mammograms" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 3, Issue 4, July - August 2014 , pp.
338-345 , ISSN 2278-6856.
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