Automatic fibrosis quantification by using a k-NN classificator

Presents an automatic algorithm to measure fibrosis in muscle sections of mdx mice, a mutant species used as a model of the Duchenne dystrophy. The algorithm described herein automatically segments three different tissues: muscle cell tissue (MT), pure collagen fiber deposit (CD) and cellular infiltrates surrounded by loose collagen deposit (CI), by using a statistical classifier based on the k-nearest neighbour (k-NN) decision rule in the RGB color space. The algorithm is trained by selecting a number of correctly classified pixels from each class. The k-NN rule classifies other pixels in the class that is most represented among the k nearest training samples in the RGB space, which is efficiently implemented with a fast k-distance transform algorithm. All extracted areas are quantified in absolute (/spl mu/m/sup 2/) and relative (%) values. For validation of this method, the different tissues were manually segmented and their quantifications statistically compared with those obtained automatically. Statistical analysis showed interoperator variability in manual segmentation. Automatic quantifications of the same areas did not differ significantly from their mean manual evaluations. In conclusion, this method produce fast, reliable and reproducible results.