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A Detection of Breast Cancer Using Digital Image Processing Techniques


     

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Breast cancer is one of the most common cancer among women in India. Early detection of microcalcification cells is very important stage for the further treatment. Calcification is the deposit of calcium in breast tissues. It is helpful to classify benign or malignant. Early detection of cancer depends on the quality of images and the ability of radiologists to read the mammogram images.

In this proposed work, the mammogram images are initially preprocessed using different methods. In this, noise in the background will be removed using median filter, artifacts will be removed using thresholding method and contrast enhancement will be done using contrast limited adaptive histogram equalization techniques.

Then the region of interest will be determined using segmentation by Otsu’s thresholding algorithm. Features of the mammogram images will be extracted using wavelet transform and to determine the information from the images Support Vector Machine classifier will be used.


Keywords

Mammogram Images, Otsu’s Thresholding, Support Vector Machine, Wavelet Transform
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  • A Detection of Breast Cancer Using Digital Image Processing Techniques

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Abstract


Breast cancer is one of the most common cancer among women in India. Early detection of microcalcification cells is very important stage for the further treatment. Calcification is the deposit of calcium in breast tissues. It is helpful to classify benign or malignant. Early detection of cancer depends on the quality of images and the ability of radiologists to read the mammogram images.

In this proposed work, the mammogram images are initially preprocessed using different methods. In this, noise in the background will be removed using median filter, artifacts will be removed using thresholding method and contrast enhancement will be done using contrast limited adaptive histogram equalization techniques.

Then the region of interest will be determined using segmentation by Otsu’s thresholding algorithm. Features of the mammogram images will be extracted using wavelet transform and to determine the information from the images Support Vector Machine classifier will be used.


Keywords


Mammogram Images, Otsu’s Thresholding, Support Vector Machine, Wavelet Transform