Efficient three-dimensional super-diffusive model for benign brain tumor segmentation

Abstract

Brain is most complex and central part of the human body. Millions of cells are present in the brain. Brain tumor is extra unwanted cell in the human brain. It is mainly categorized into benign and malignant. Benign tumor cells have very similar characteristics with its surrounding cells. Its accurate detection and segmentation is very challenging task. Image segmentation methods have major contribution in detection and segmentation of these tumor cells. Segmentation methods are either boundary based or region based. These methods use traditional integral-order calculus. It has been observed that these approaches are unable to detect low variational region such as benign tumor. In the present manuscript, fractional diffusion-based benign brain tumor detection and segmentation method is being proposed. It has been observed that the proposed method is able to detect and segment benign brain tumor region more accurately. Higher accuracy has been obtained due to fractional-order derivative. Frequency domain derivative definition has been used in the proposed method due to simplicity and low computational cost. A hardware model of the proposed work has been also presented in the current manuscript. The results obtained have been compared with existing boundary-based and region-based tumor detection and segmentation methods. It has been found that the proposed method is having higher accuracy in benign brain tumor detection and segmentation with low computational cost.