期刊
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
卷 55, 期 1-3, 页码 15-23出版社
SPRINGER
DOI: 10.1007/s11265-008-0214-6
关键词
Renal cell carcinoma; Subtype classification; Computer-aided diagnosis; Tissue image quantification; Feature extraction for classification; Wavelet; Co-occurrence; Morphological processing
资金
- National Institutes of Health [R01CA108468, P20GM072069, U54CA119338]
- Georgia Cancer Coalition
- Hewlett Packard, and Microsoft Research
We present a new image quantification and classification method for improved pathological diagnosis of human renal cell carcinoma. This method combines different feature extraction methodologies, and is designed to provide consistent clinical results even in the presence of tissue structural heterogeneities and data acquisition variations. The methodologies used for feature extraction include image morphological analysis, wavelet analysis and texture analysis, which are combined to develop a robust classification system based on a simple Bayesian classifier. We have achieved classification accuracies of about 90% with this heterogeneous dataset. The misclassified images are significantly different from the rest of images in their class and therefore cannot be attributed to weakness in the classification system.
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