Tissue characterization from X-ray images

被引:24
作者
Bocchi, L
Coppini, G
DeDominicis, R
Valli, G
机构
[1] UNIV FLORENCE, DEPT ELECT ENGN, I-50139 FLORENCE, ITALY
[2] CNR, INST CLIN PHYSIOL, I-56100 PISA, ITALY
[3] UNIV FLORENCE, DEPT CLIN PATHOPHYSIOL, FLORENCE, ITALY
关键词
X-ray images; computer vision; texture analysis; co-occurrence matrices; neural networks; Kohonen self-organizing map; feed-forward networks;
D O I
10.1016/S1350-4533(96)00078-1
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The study of the fine-scale structure of biological tissues is crucial for diagnosing a wide number of different diseases. In X-ray images, line structures usually induce a correlation among image gray levels and are commnonly perceived as textures. In this paper, rue report on a Computer Vision approach to the characterization of biological tissues as imaged by standard Xray techniques. In particular, using features derived from co-occurrence matrices, we have assessed spatial gray-level dependence of bone tissue and lung parenchyma images. A hybrid neural network was adopted to distinguish pathological tissues from normal ones and to classify different pathologies. (C) 1997 IPEM. Published by Elsevier Science Ltd.
引用
收藏
页码:336 / 342
页数:7
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