Abstract

The acquisition of Positron Emission Tomography (PET) images for tumor and lesion detection has emerged as one of the most powerful tools for medical image analysis in recent years. Works on patch-based sparse representation selected the most relevant elements from a large group of candidates using segmentation, but failed to separate myelinated WM from unmyelinated WM compromising multi-modality image information. In this study, a novel technique to obtain multimodality aspect of tumor and lesion detection in PET images through Automated Multimodal Spectral Cluster based Segmentation (AMSCS) is proposed, aiming at improving the tumor detection accuracy. The Spectral Contours with Constrained Threshold (SCCT) technique in AMSCS is carried out to various spectral features of the PET image without any deformation, improving the true positive rate. The SCCT technique utilize user defined seed point in the region of interest in PET images and generate spectral contours (i.e.,) shape, size, location and intensity. A Multi-Spectral Contour Cluster (MSCC) mechanism is introduced that organizes the spectral contour features of shape, size, location and intensity into multi-spectral clusters for quicker segmentation of PET Image regions of interest. Experimental analysis is conducted using Primary Tumor Data Set from UCI repository PET Images on parametric such as, Multi-spectral cluster size, ROI segmentation time, tumor and lesion detection time, tumor detection accuracy.

Keywords:

Constrained threshold, multimodality, positron emission tomography, region of interest, spectral contours,

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Competing interests

The authors have no competing interests.

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