Abstract
3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out 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 suffers a long computation time for its calculations.
Highlights
Volume segmentation allocates the voxels in 3D images into partitions or 3D regions that represent meaningful physical entities
The proposed approach has been tested on NEMA IEC body phantom [41] (DATA SET 1) and real chest images from a Computerized Tomography (CT) scanner [29] (DATA SET 2)
The most accurate results have been achieved with applying Hidden Markov Models (HMMs) directly on the spatial domain without any transformation
Summary
Volume segmentation allocates the voxels in 3D images into partitions or 3D regions that represent meaningful physical entities. Voxels’ classification into regions is performed according to a certain region to which the voxels belong, and some shared, predefined properties. Those voxels comprise an isolated or segmented Object Of Interest (OOI) from the input volume. There are many existing techniques used for medical image segmentation, including Multiresolution Analysis (MRA), statistical methods, and thresholding- and clustering-based techniques. Medical images can be segmented using thresholding approaches by partitioning their intensities. When images contain different structures with contrasting intensities, thresholding provides a simple but effective means for obtaining segmentation. The thresholds are generated based on visual assessment of the resulting segmentation [3, 4]
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