Evaluation of Statistical Region Merging Segmentation for heart and spleen contour delineation in CT images

Author: Sami Almutairi

Almutairi, Sami, 2017 Evaluation of Statistical Region Merging Segmentation for heart and spleen contour delineation in CT images, Flinders University, School of Computer Science, Engineering and Mathematics

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Abstract

Segmentation is the key step towards extracting information from medical images for further analysis. This study investigates the accuracy of organ segmentation from CT images using the Statistical Region Merging (SRM) segmentation technique. The experiments were conducted using data set of 12 consecutive CT slices for heart and 8 CT slices for spleen. The data sets were previously manually segmented by an expert in human anatomy and these sets constituted the ground truth to judge the accuracy of an automatic segmentation using SRM. The results of SRM segmentation technique depend on a parameter (Q) which value determines granularity of the outcome. A wide range of Q values was investigated in relation to the accuracy of segmented heart and spleen. The accuracy was measured using the Dice and Jaccard indexes. The results show that the SRM segmentation technique can potentially provide high accuracy segmentation of 80% and above for both heart and spleen. However, neither heart nor spleen can be segmented at this level of precision as a single component and an additional post-processing is needed to merge eligible regions into one piece corresponding to the whole organ.

Keywords: Statistical Region Merging Segmentation
Subject: Engineering thesis

Thesis type: Masters
Completed: 2017
School: School of Computer Science, Engineering and Mathematics
Supervisor: Dr. Mariusz Bajger