Probability Random Index Based Clustering for Segmentation of Pet-Ct Images

Abstract

Medical image segmentation is a vital process in medical diagnosis and evaluation of tumor response to therapy. Current segmentation methods works only on single modality image like positron emission tomography has low resolution and gives only functional information; Computed Tomography has low contrast and provides structural information. This paper focus on segmentation of multimodality PET-CT image. In recent days PET-CT is advanced multimodal imaging equipment, which gives both functional and anatomical information in a single image. Probability random index is a new methodology adopted to segment the portion of an image, which is most essential for determining the actual intricacies involved in the portion of a body. The clustering is another methodology used to group similar pixel locations into a single group based on unpredictable random values of an image. The probability based clustering has been incorporated to overcome the drawbacks of existing methods of segmentation like over segmentation and under segmentation. The over segmentation has been eliminated by incorporating random values generated from the features of dataset of images. Similarly under segmentation has been eliminated by removing barriers of lack of collecting similarly values from clustering. Thus, the proposed method eliminates both over segmentation and under segmentation drawbacks of the existing methods. The proposed probability random index based clustering has yielded good results in comparision with other contemporary methods, which shall be observed from the section, results and analysis. The proposed probability random indexed clustering has yielded a good result of 88.41% on benchmark dataset.