A new approach to unsupervised Markov random field-based segmentation of MR images

被引：0
|
作者：
Morrison, MW
Dingle, AA
Attikiouzel, Y
机构：
来源：
ISSPA 96 - FOURTH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, PROCEEDINGS, VOLS 1 AND 2 | 1996年
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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 describes a new approach to the unsupervised segmentation of images. A Markov random field model is used for prior label field modelling. Unlike conventional stochastic model-based approaches, each image class is not characterised by a parametric model. The algorithm compares the data in local windows around each pixel with the global distribution of data in each class using appropriate distance metrics. A novel method for determining the number of image classes is presented.
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页码：357 / 360
页数：4
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