Removal of noise on mammogram breast images using filtering methods

Detecting Breast Cancer (BC) at an early stage can reduce the rate of mortality. A mammogram is a preliminary examination of breast cancer with an optimistic and accurate method, for the same test will be used to know the behavior of the tumor in all stages of cancer. Unpolished Images need to process for a clear image to analyze, and enhanced images can use to view the tissue, muscle, and tumor. Image processing methods are not only used to remove the noisy pixels but also help to extract the Region of Interest (RoI) from the image. The size and structure of the tumor will be extracted using statistical analysis. This study was experimented on mammogram images by analyzing the types of noise on the image and filtering methods applied for a specific class of noise data. The different filtering approaches like Adaptive, Gaussian, Threshold, Mean, Mean-Median, Bilateral, Wiener, and Tri-State Filters were used. Mammogram images are grayscale images. The objective of this research experiment is noise data will reduce the detection factors of the tumor with True-Ve and False+Ve results. Fault results will impact the accuracy to identify the RoI. Image Preprocessing will help to improve the quality of the images and help in the treatment plan and expert advice in the decision of the disease stage. Results: The Dataset used images from CBIS-DDSM (The Curated Breast Imaging Subset of DDSM-[Digital Database for Screening Mammography]). The obtained Peak-signal-to-Noise Ratio (PSNR) achieved is 80% minimum.

Automated summary

Detecting breast cancer at an early stage is crucial for reducing mortality rates. Mammography, as a preliminary examination, utilizes image processing and filtering methods to enhance the accuracy of tumor detection.