Filtering method for pre-processing mammogram images for breast cancer detection

Abstract

Breast cancer is a stand-out surrounded by the most widely perceived diseases and has a high rate of mortality around the world, significantly risking the health of the females. Among existing all modalities of medical scans, mammography is the most preferred modality for preliminary examination of breast cancer. To assist radiologist, a computer-aided diagnosis (CAD) is enhancing and important medical systems for mammographic lesion analysis. CAD is necessary to provide doctors, to improve detection quality of breast cancer. In mammogram images, micro-calcifications is one of the imperative sign for breast cancer detection. Mammographic medical scan may present unwanted noise and CAD systems are very sensitive to noise. So, pre-processing of medical images for any medical image analysis application like brain tumor detection, breast cancer detection, and interstitial lung disease classification is considered as an important step. The segmentation or classification accuracy is mainly depends upon the significant improved pre-processing process. Thus, in this work, different types of filtering techniques used for noise reduction in medical image processing are analyzed. The qualitative and quantitative results are examined on mini-MIAS mammogram image database. The effectiveness of filtering techniques is compared based on the different quantitative parameters and visual qualities of examined output.