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Breast Cancer Diagnosis using Digital Image Segmentation Techniques
Background/Objectives: The objective of this research is to work out on the computer-aided detection of Breast cancer. This research work mainly target to build up a structure of methods by using image processing and classification approach for the recognition of abnormalities in Mammograms. Methods/Statistical Analysis: It is observed that the breast images are analyzed after decomposition. However, the image become smaller and crucial information may be lost by virtue of image decomposition and when region of interest is applied only on the specific segment of image as above said some information get lost. Findings: Further, the high pass decomposed image using wavelet transform over enhance the intensity variation that may be falsely detected and cancer characteristics leading to erroneous analysis. These limitations could be overcome by denoising the given input image using the wavelet transform and analysis made on inverse transformed image. The texture features should also be considered while analyzing an image for cancer detection. A back propagation neural network is trained using the mammogram images in different categories and tested using the sample as well as unknown images13 feature neurons are used for N/W training and testing as well. Application/Improvements: The N/W is trained for normal images as well as abnormal cases. The classification accuracy has been observed to the tune of 89%.
BPN, Image Segmentation, NN Classifier, Statistical Features, Texture Features.
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