Breast cancer in women has been the most often diagnosed cancer. Digital mammogram becomes the most effective imaging method to detect breast cancer in early stage. In breast cancer screening, radiologists typically miss about 10-30% of tumors due to the speculated margins of lesions. Mammogram is a low contrast image whose quality needs to be improved for better interpretation. The performance of the validation of pre-processing methods for mammogram image enhancement is done by performance metrics such as peak signal to noise ratio (PSNR) and mean square error (MSE). Good filtering technique having higher PSNR and low MSE value. Experimental results on the digital database for screening mammography images shown that the non-local mean filter is better for mammogram image enhancement. Further we proposed mammogram images enhancement by entropy improvement method by considering nonlocal filtered images. These methodologies could add to the effective discovery of masses and micro calcifications in mammograms.

Accurately extracting the features of interest from a blurred image is one of the difficult tasks in image segmentation. This paper uses the blind deconvolution, deblurring algorithm to find original features of interest, and then uses the improved Chan -Vese snake model to get the accurate features. The presented algorithm is tested on the MRI images of brain and results are found to be satisfactorily.

No abstract