CHARACTERIZATION OF BREAST LESIONS BY PROCESSING DIGITAL BREAST IMAGES

This Rendering to the World Health Organization, women in both developed and developing nations are most likely to develop breast cancer. This illness causes breast cells to grow and multiply out of control. According to research institutes and international organizations, there are various screening methods available based on age, and breast cancer can be cured if detected in time. The Breast Imaging Reporting and Data System (BIRADS) is a standardized system that is commonly used in these techniques to report results and findings. Results are sorted by BIRADS into six categories, numbered 0 through 6. Furthermore, mammography is the most widely utilized screening technique.

This study suggests using mammography data processing to identify breast lesions. Adaptive filters are used for image cropping and contrast enhancement during the pre-processing phase. The pectoral muscle is then segmented using segmentation techniques that consider morphological and area growth factors. The lesion is then divided into sections at the muscle and breast levels using the Discrete Wavelet Transform (DWT), which finds any micro calcifications. Furthermore, to distinguish between dense lesions and other kinds of lesions, an area cultivation approach combined with multiple thresholding techniques is employed. Lastly, the obtained segmentation is used to extract textural and morphological features.

When expert-segmented and automatically segmented images were compared, the Sorensen Decade similarity index was 0.73, indicating the effectiveness of the suggested method. Considering that the lesion area on a mammogram can only be roughly delineated by hand or automatically, this is a promising outcome.