Interpretation of MR images using self-organizing maps and knowledge-based expert systems

被引：24

作者：

Gueler, Inan ^{[1
]}

Demirhan, Ayse ^{[1
]}

Karakis, Rukiye ^{[1
]}

机构：

[1] Gazi Univ, Fac Technol, Dept Elect & Comp Technol, TR-06500 Ankara, Turkey

来源：

DIGITAL SIGNAL PROCESSING | 2009年 / 19卷 / 04期

关键词：

MR images; Image segmentation; Self-organizing maps; Knowledge-based expert systems; NEURAL NETWORKS; SEGMENTATION; CLASSIFICATION;

D O I：

10.1016/j.dsp.2008.08.002

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A new image segmentation system is presented to automatically segment and label brain magnetic resonance (MR) images to show normal and abnormal brain tissues using self-organizing maps (SOM) and knowledge-based expert systems. Elements of a feature vector are formed by image intensities. first-order features, texture features extracted from gray-level co-occurrence matrix and multiscale features. This feature vector is used as an input to the SOM. SOM is used to over segment images and a knowledge-based expert system is used to join and label the segments. Spatial distributions of segments extracted from the SOM are also considered as well as gray level properties. Segments are labeled as background, skull, white matter, gray matter, cerebrospinal fluid (CSF) and suspicious regions. (c) 2008 Elsevier Inc. All rights reserved.

引用

页码：668 / 677

页数：10

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