Detection And Segmentation Of Mass Region In Mammogram Image - Ting, Shyue Siong (2014)
Breast cancer is the most common form of cancer and continues to be a significant public health problem amongst women around the world. Early detection is the most promising way to decrease the number of patient suffering from this disease. Currently, mammography is an effective diagnostic technique for early detection of breast cancer. Numerous studies have been carried out to develop Computer-Aided Diagnosis (CAD) system to help radiologists. Unfortunately, none of the proposed techniques provide good results in both mass detection and segmentation. Thus, in this study, an automated system to detect and segment the true mass regions of any size, shape and margin in mammogram image is proposed. Initially, the Mean Median Intersection Point (MMIP) algorithm is proposed to obtain a point in the intensity histogram to be set as threshold value to segment the breast region and distinguish it from the background in the mammogram image. Then, a modified contrast enhancement technique, namely Multi-Stage Contrast Enhancement (MSCE) technique is proposed to increase the contrast between the mass and breast tissue regions. Finally, an automated seed based region growing process for mass detection and segmentation is proposed. In the proposed technique, the seed point that produces the smallest smoothness descriptor value is set as the initial mass centre for the region growing process. In order to produce results which mimic true mass region, the enhanced mammogram image is multiplied with the Gaussian function centred at a seed point location.