Yang, X., Ye, Xujiong and Slabaugh, G. (2015) Multilabel region classification and semantic linking for colon segmentation in CT colonography. IEEE Transactions on Biomedical Engineering, 62 (3). pp. 948-959. ISSN 0018-9294
Full content URL: https://doi.org/10.1109/TBME.2014.2374355
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Item Type: | Article |
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Item Status: | Live Archive |
Abstract
Accurate and automatic colon segmentation from CT images is a crucial step of many clinical applications in CT colonography, including computer-aided detection (CAD) of colon polyps, 3-D virtual flythrough of the colon, and prone/supine registration. However, the existence of adjacent air-filled organs such as the lung, stomach, and small intestine, and the collapse of the colon due to poor insufflation, render accurate segmentation of the colon a difficult problem. Extra-colonic components can be categorized into two types based on their 3-D connection to the colon: detached and attached extracolonic components (DEC and AEC, respectively). In this paper, we propose graph inference methods to remove extracolonic components to achieve a high quality segmentation. We first decompose each 3-D air-filled object into a set of 3-D regions. A classifier trained with region-level features can be used to identify the colon regions from noncolon regions. After removing obvious DEC, we remove the remaining DEC by modeling the global anatomic structure with an a priori topological constraint and solving a graph inference problem using semantic information provided by a multiclass classifier. Finally, we remove AEC by modeling regions within each 3-D object with a hierarchical conditional random field, solved by graph cut. Experimental results demonstrate that our method outperforms a purely discriminative learning method in detecting true colon regions, while decreasing extra-colonic components in challenging clinical data that includes collapsed cases.
Additional Information: | Publication history on PDF: date of current version February 16, 2015 |
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Keywords: | Classification (of information), Graphic methods, Image segmentation, Semantics, Topology, Automatic colon segmentation, Computer-aided detection, Conditional random field, CT colonography, Discriminative learning, graph inference, High-quality segmentation, Topological constraints, Computerized tomography, Article, classifier, colon, computed tomographic colonography, computed tomography scanner, computer assisted radiography, discrimination learning, human, image processing, bmjgoldcheck, NotOAChecked |
Subjects: | G Mathematical and Computer Sciences > G400 Computer Science H Engineering > H673 Bioengineering B Subjects allied to Medicine > B821 Radiography, diagnostic |
Divisions: | College of Science > School of Computer Science |
Related URLs: | |
ID Code: | 16978 |
Deposited On: | 27 Mar 2015 09:23 |
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