Feature extraction from MR images for detection of brain and breast tumors through mathematical modeling

Abstract

Segmentation of medical images is a critical step to distinguish different regions, especially tumor. The presence of severe intensity inhomogeneity in Magnetic Resonance Imaging (MRI) and Mammogram images makes it difficult to assess and detect the boundaries of tumor. Tumor detection in medical images measures and identifies the structure of the tumor. This challenging task of extracting tumor i.e., region of interest is achieved by proposing a novel fuzzy selective model. The proposed model is mainly based on selective segmentation to detect the tumor in medical images. This model is based on the local Gaussian distribution as a fitting term with fuzzy logic to handle uncertainty and irregular borders in medical images. Due to the usage of fuzzy membership functions, it avoids local minima and converges to the tumor boundary rapidly and efficiently. In addition, to obtain more information of a local intensity, the idea of multi-scale modeling is incorporated to achieve significant results. The proposed segmentation model is applied to diverse MRI and Mammogram datasets containing both the intensity inhomogeneity and noisy images, this data is collected from local hospitals and the ground truths are created by the experts. The extensive simulation results show that the proposed segmentation method significantly outperforms existing state-of-the-art techniques using both the qualitative and quantitative assessment. Loss functions play very important role in CNN architectures, and is well studied research area. In the second part of the paper, we will be introducing a new loss function, which is introduced in the first part of the paper. Deep active contour architectures based Chan-Vese loss function works well in images with homogeneity and may not work well in images with intensity inhomogeneity. We proposed an architecture, whose loss function will be based on this new proposed model. The proposed model is used as a loss function in a newly introduced CNN architecture, which has produced improved results. The results of the new architecture are compared with other existing methods used for segmentation of medical images.