3D Tumor Segmentation in MR images using Tumor Characteristics

3D segmentation is an important pre-processing phase that dictates the performance of the possible post processing applications in 3D biomedical image analysis. 3D reconstruction of the segmented tumor from 2D MRI plays an essential role in 3D encapsulation of the segmented tumor. Fuzzy C-mean clustering is widely used in 2D segmentation of the brain tumor. In this thesis 2D tumor projections in each slice of the MRI are segmented using FCM. 2D slices extracted through FCM are used to generate the 3D reconstruction of the tumor. However, the possible low number of slices limits the quality of such 3D reconstruction. This thesis proposes to minimize this problem by generating predicted slices (P) between intra slices (I) and generating bi-predictive slices (B) between predictive slices and intra slices by using bilinear interpolation. 3D reconstruction of the brain tumor is performed by using the real slices and the predicted slices. Limitations of the FCM clustering in deciding which cluster is to be segmented is overcome by employing 2D/3D characteristics such as intensity, circularity and 3D Compactness in T2 -weighted MR Images for reliable segmentation of the 3D brain tumors. The proposed approach leads to detect the 3D tumor automatically with improved accuracy compared to alternative methods in the literature. Keywords: 3D Segmentation; MRI; FCM clustering; Labeling; Thresholding; 3D reconstruction; 3D compactness; Circularity; Tumor; Brain.