A Competitive Study of Graph Reduction Methods for Min S-T Cut Image Segmentation

Abstract

When applied to the segmentation of 3D medical images, graph-cut segmentation algorithms require an extreme amount of memory and time resources in order to represent the image graph and to perform the necessary processing on the graph. These requirements actually exclude the graph-cut based approaches from their practical application. Hence, there is a need to develop the dedicated graph size reduction methods. In this paper, several techniques for the graph size reduction are proposed. These apply the idea of superpixels. In particular, two methods for superpixel creation are introduced. The results of applying the proposed methods to the segmentation of CT datasets using min-cut/max-flow algorithm are presented, compared and discussed.

Automated Detection of Lesions in Patients with Traumatic Brain Injury using Brain CT Images: Concept Note and Proposed Method

“…Rapid advancements in medical imaging technology have resulted in accurate and early diagnosis of many diseases conditions, better management planning, and improved outcomes in neurosurgical practice. 7 Several authors have developed algorithms for automated analysis and segmentation of CT images to interpret the findings as an adjunct to manual image reading. 7 8 9 10 11 12 The present article presents a conceptual analysis to explore the feasibility of automated image analysis and segmentation to interpret the CT images in patients with TBI.…”

“…7 Several authors have developed algorithms for automated analysis and segmentation of CT images to interpret the findings as an adjunct to manual image reading. 7 8 9 10 11 12 The present article presents a conceptual analysis to explore the feasibility of automated image analysis and segmentation to interpret the CT images in patients with TBI.…”