Segmentation of medical images by using wavelet transform and incremental self-organizing map

被引：0

作者：

Dokur, Zumray ^{[1
]}

Iscan, Zafer ^{[1
]}

Olmez, Tamer ^{[1
]}

机构：

[1] Istanbul Tech Univ, Elect & Commun Engn, TR-34469 Istanbul, Turkey

来源：

MICAI 2006: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2006年 / 4293卷

关键词：

segmentation of medical images; artificial neural networks; self-organizing map; wavelet transform;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a novel method that uses incremental self-organizing map (ISOM) network and wavelet transform together for the segmentation of magnetic resonance (MR), computer tomography (CT) and ultrasound (US) images. In order to show the validity of the proposed scheme, ISOM has been compared with Kohonen's SOM. Two-dimensional continuous wavelet transform (2D-CWT) is used to form the feature vectors of medical images. According to the selected two feature extraction methods, features are formed by the intensity of the pixel of interest or mean value of intensities at one neighborhood of the pixel at each sub-band. The first feature extraction method is used for MR and CT head images. The second method is used for US prostate image.

引用

页码：800 / +

页数：3

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