Tumor segmentation by tolerance near set approach in mammography and lesion classification with neural network

The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, a new algorithm to detect suspicious lesions in mammograms is developed using tolerance near set approach. Near set theory provides a method to establish resemblance between objects contained in a disjoint set. Objects that have, in some degree, affinities are considered perceptually near each other. The probe functions are defined in terms of digital images such as: gray level, entropy, color, texture, etc. Objects in visual field are always presented with respect to the selected probe functions. Moreover, it is the probe functions that are used to measure characteristics of visual objects and similarities among perceptual objects, making it possible to determine if two objects are associated with the same pattern. The algorithm has been verified on mammograms from the CICRI (Central India Cancer Research Institute, Nagpur, India) and Mias database. Results of segmentation are compared with Otsu method of segmentation.. Once the features are computed for each region of interest (ROI), they are used as inputs to a supervised Back Propagation Neural Network. Results indicate that Tolerance Near sets segmentation method performs better than otsu method in terms of classification accuracy.