An Effective and Efficient Approach to 3D Reconstruction and Quantification of Brain Tumor on Magnetic Resonance Images

Three dimensional (3D) reconstruction of the tumor from medical images is an important operation in the medical field as it helps the radiologist in the diagnosis, surgical planning and biological research. Thus in this paper, we propose an effective and efficient approach to 3D reconstruction of brain tumor and estimation of its volume from a set of two dimensional (2D) cross sectional magnetic resonance (MR) images of the brain. In the first step, MR images are preprocessed to improve the quality of the image. Next, abnormal slices are identified based on histogram analysis and tumor on those slices is segmented using modified fuzzy c-means (MFCM) clustering algorithm. Next, the proposed enhanced shape based interpolation technique is applied to estimate the missing slices accurately and efficiently. Then, the surface mesh of the tumor is reconstructed by applying the marching cubes (MC) algorithm on a set of abnormal slices. The large number of triangles generated by the MC algorithm was reduced by our proposed mesh simplification algorithm to accelerate the rendering phase. Finally, rendering was performed by applying Phong lighting and shading model on the reconstructed mesh to add realism to the 3D model of the tumor. The volume of the tumor was also computed to assist the radiologist in estimating the stage of the cancer. All experiments were carried out on MR image datasets of brain tumor patients and satisfactory results were achieved. Thus, our proposed method can be incorporated into the computer aided diagnosis (CAD) system to assist the radiologist in finding the tumor location, volume and 3D information.