| Autor: |
Brunner G; Computational Biomedicine Lab, Depts. of Computer Science, Elec. & Comp. Engineering, and Biomedical Engineering, Univ. of Houston, Houston, TX, USA. gbrunner@uh.edu, Kurkure U, Chittajallu DR, Yalamanchili RP, Kakadiaris IA |
| Jazyk: |
angličtina |
| Zdroj: |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention [Med Image Comput Comput Assist Interv] 2008; Vol. 11 (Pt 1), pp. 144-52. |
| DOI: |
10.1007/978-3-540-85988-8_18 |
| Abstrakt: |
There is growing evidence that calcified arterial deposits play a crucial role in the pathogenesis of cardiovascular disease. This paper investigates the challenging problem of unsupervised calcified lesion classification. We propose an algorithm, US-CALC (UnSupervised Calcified Arterial Lesion Classification), that discriminates arterial lesions from non-arterial lesions. The proposed method first mines the characteristics of calcified lesions using a novel optimization criterion and then identifies a subset of lesion features which is optimal for classification. Second, a two stage clustering is deployed to discriminate between arterial and non-arterial lesions. A histogram intersection distance measure is incorporated to determine cluster proximity. The clustering hierarchies are carefully validated and the final clusters are determined by a new intracluster compactness measure. Experimental results indicate an average accuracy of approximately 80% on a database of electron beam CT heart scans. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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