DocumentCode
2486416
Title
Development of comparative reading system using 1D SOM for brain dock examinations
Author
Sato, Kazuhito ; Kadowaki, Sakura ; Madokoro, Hirokazu ; Inugami, Atsushi
Author_Institution
Fac. of Syst. Sci. & Technol., Akita Prefectural Univ., Yurihonjo, Japan
fYear
2010
fDate
18-23 July 2010
Firstpage
1
Lastpage
7
Abstract
We propose an objective segmentation method for Magnetic Resonance (MR) images of the brain using self-mapping characteristics of one-dimensional Self-Organizing Maps (SOM). The proposed method requires no operators to specify the representative points, but can segment tissues (such as cerebrospinal fluid, gray matter and white matter) needed for diagnosis of brain atrophy. Doing clinical image experiments, we demonstrate the effectiveness of our method. As a result, we can obtain segmentation results that agree with anatomical structures such as continuities and boundaries of brain tissues. In addition, we propose a Computer-Aided Diagnosis (CAD) system for brain dock examinations based on the use case analysis of diagnostic reading, and construct a prototype system for reducing loads to diagnosticians that occur in quantitative analyses of the extent of brain atrophy. Through field tests of 193 examples of brain dock medical examinees at Akita Kumiai General Hospital, we also present the prospect of efficient support of diagnostic reading in the clinical field because the aging situation of brain atrophy is readily quantifiable irrespective of diagnosticians´ expertise.
Keywords
CAD; biological tissues; biomedical MRI; brain; image segmentation; medical image processing; self-organising feature maps; 1D SOM; Akita Kumiai General Hospital; brain atrophy; brain dock examinations; cerebrospinal fluid; clinical image experiments; comparative reading system; computer-aided diagnosis; diagnostic reading; magnetic resonance images; one-dimensional self-organizing maps; self-mapping characteristics; Diseases; Image segmentation; Magnetic resonance imaging;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks (IJCNN), The 2010 International Joint Conference on
Conference_Location
Barcelona
ISSN
1098-7576
Print_ISBN
978-1-4244-6916-1
Type
conf
DOI
10.1109/IJCNN.2010.5596292
Filename
5596292
Link To Document