SEGMENTATION OF MEDICAL IMAGES USING GEO-THEORETIC DISTANCE MATRIX IN FUZZY CLUSTERING

被引：2

作者：

Pham, Tuan D. ^{[1
]}

Eisenblaetter, Uwe ^{[1
]}

Golledge, Jonathan ^{[2
]}

Baune, Bernhard T. ^{[3
]}

Berger, Klaus ^{[4
]}

机构：

[1] Univ New South Wales, ADFA Sch Informat Technol & Elect Engn, Canberra, ACT 2600, Australia

[2] Vasular Biolog Univ, Calgary, AB 4811, Canada

[3] Psychiat Neurosci, Dept Phychiat, Townsville, Qld 4811, Australia

[4] Univ Munster, Inst Epidemiol & Social Med, D-48129 Munster, Germany

来源：

2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6 | 2009年

关键词：

Medical image segmentation; CT imaging; MRI; fuzzy c-means; semi-variance;

D O I：

10.1109/ICIP.2009.5413877

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Investigation on novel methods for extracting objects of interest in medical images has been an important and challenging area of research in image analysis. In particular, medical images are highly spatially correlated and subject to fuzzy distribution of pixels, we present in this paper a new algorithm for medical image segmentation with special reference to abdominal aortic aneurysm and degraded human brain imaging. Development of the new algorithm is based on the implementation of the theoretic distance matrix with spatial semi-variances.

引用

页码：3369 / +

页数：2

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