NDLI: Fully Automated Segmentation of Cerebral Ventricles from 3-D SPGR MR Images using Fuzzy Representative Line

Content Provider	Springer Nature Link
Author	Kobashi, Syoji Kondo, Katsuya Hata, Yutaka
Copyright Year	2006
Abstract	Generating surface shaded display images and measuring the volumes of cerebral ventricles using 3-D SPGR MR images will help to diagnose many types of cerebral diseases with quantitatively and qualitatively. However, manual segmentation of cerebral ventricles is time-consuming and is subject to inter- and intra-operator variation. This article proposes a fully automated method for segmenting cerebrospinal fluid (CSF) and cerebral ventricles from MR images. Our method segments the cerebral ventricles by using a representative line (RL), which can represent the abstract of the shape and position of the cerebral ventricles. The RL is found by fuzzy If-Then rules that can implement physicians’ knowledge on the cerebral ventricles. The proposed method was applied to MR volumes of 20 normal subjects, 20 Alzheimer disease (AD) and 20 normal pressure hydrocephalus (NPH) patients. The segmentation error ratio of the lateral ventricles was 1.98% in comparison with the volumes of manually delineated region by a physician. Using the proposed method, we found that patients of NPH significantly increased the ratio of volume of the lateral ventricles to the total CSF volume in comparison with that of AD (significance level < 0.001)
Starting Page	1181
Ending Page	1191
Page Count	11
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	10
Issue Number	12
e-ISSN	14337479
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-01-28
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Medical image segmentation Fuzzy image processing Cerebral ventricles Magnetic resonance image Control Engineering Numerical and Computational Methods in Engineering Mathematical Logic and Foundations Computing Methodologies Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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