An affinity propagated clustering aided computerized Inherent Seeded Region Growing and Deep learned Marching Cubes Algorithm (ISRG‐DMCA) based three dimensional image reconstruction approach

Abstract In this manuscript, the novel three dimensional (3D) image reconstruction approach based on affinity propagated clustering aided computerized Inherent Seeded Region Growing and Deep learned Marching Cubes Algorithm (ISRG‐DMCA) is proposed. The major purpose of this manuscript is to divide the brain tumor based on Shapelets. Here, the information about the shape/depth that can be obtained in every two dimensional (2D) image on the image stack is handled to acquire a 3D reconstruction, which provides high accurate 3D view of tumor Region of Interest (ROI). Then, the 3D model is rendered with the help of the proposed Deep learned Marching Cubes Algorithm (Deep MCA) at 3D reconstruction technique. The performance of the proposed method is executed in MATLAB. The simulation results show that the proposed ISRG‐DMCA algorithm attains a higher detection rate 14.117%, 5.435%, higher accuracy rate 9.556%, 26.41% and lower execution time 66.667%, 75%, compared with the existing methods, like Improved Marching Cubes Algorithm (IMCA), Improved CNN‐CRF method, respectively. In the proposed ISRG‐DMCA method, the volume of the tumor has a length of 2.56 mm. Finally, the simulation outcomes demonstrate that the proposed ISRG‐DMCA method can be able to find the optimal solutions efficiently and accurately.