Enhanced 3D segmentation techniques for reconstructed 3D medical volumes: Robust and Accurate Intelligent System

Medical images play an important role in treating a large number of ailments as they are integral and even indispensable to the diagnosis process of such ailments. Medical images come from different acquisition systems (such as PET, CT, MRI) and, in many situations, automated processing of these images can greatly aid physicians and make their jobs easier. In medical imaging and its applications, 2D segmentation (with its different approaches such as FCM, k-means, MRFM and NN) is the first step which is used to extract ROI. This helps in extracting ROI in each slice (2D medical image) separately regardless of its relation to the next and the previous slices. In this paper, a 3D model of FCM segmentation techniques is proposed to enhance the segmentation process and take in mind the overall 3D-Volume as one testing data.