3D Medical Volume Segmentation Using Hybrid Mult iresolution Statistical Approaches

3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physicalentities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enablesthe preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets havebeen deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medicalvolume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extractionwhich can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carriedout to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI).Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but su ffers a long computation timefor its calculations.