Discoursing Novel Procedure for Segmentation of Mammograms

Mammography plays a significant role in the early detection of breast cancers since it can demonstrate changes in the breast, years before a patient or physician can feel them. The research work conducted in the research paper highlights the process of segmentation of mammogram images intending to detect the presence of tumors in the breast at early stages so that such tumors can be timely cured and further damage could be prevented. The flowchart developed in the research paper defines a systematic approach adopted to perform segmentation on mammograms. This includes the use of techniques like Green Channel Complement, CLAHE (Contrast Limited Adaptive Histogram Equalization), Morphological operations, and FCM (Fuzzy C-Means). Mammogram images from the MIAS (Mammographic Image Analysis Society) database have been used for performing segmentation. The research paper features a detailed algorithm that discusses the detailed adopted approach. The GUI (Graphical User Interface) has been constructed with multiple windows to show the output received at each step after appropriate processing.Numerical readings have been obtained for the parameters like sensitivity, specificity, accuracy, positive predictive value, negative predictive value, false-negative rate, false-positive rate, etc. The obtained readings of different parameters prove the authenticity of conducted work. Segmentation enables the scrutinizing of any region within an image. The conducted research work can prove helpful in enhancing the mammogram image and focusing on the segmented image which indicates the presence of microcalcifications. The effectively conducted segmentation enables the radiologist to classify the tumor as benign (non-cancerous) or malignant (cancerous). Based on the obtained result the further treatment of the patient can be decided upon. The findings of the research paper are restricted to the segmentation phase of the mammogram images.