Fuzzy Segmentation Driven by Modified ABC Algorithm Using Cartilage Features Completed by Spatial Aggregation: Modeling of Early Cartilage Loss

In a clinical practice of the orthopedics, the articular cartilage assessment is one of the major clinical procedures serving as a predictor of the future cartilage loss development. The early stage of the cartilage osteoarthritis is badly observable from the native MR records due to weak contrast between the physiological cartilage and the osteoarthritic spots. Therefore, the cartilage regional modeling would reliably differentiate the physiological cartilage from the early cartilage deterioration, and can serve as an effective clinical tool. In a comparison with the conventional segmentation methods based on the hard thresholding, the soft fuzzy thresholding based on the histogram separation into segmentation classes via the fuzzy triangular functions represents a sensitive regional segmentation even in the non-contrast environment. We have proposed the soft segmentation where the fuzzy sets are driven by the ABC genetic algorithm to optimal fuzzy class’s distribution regarding the knee tissues characteristics. Consequently, the spatial aggregation is employed to taking advantage the spatial dependences which allows for modification the original fuzzy membership function. This procedure ensures the correct pixel’s classification especially when the noise pixels are present. Such multiregional segmentation makes a mathematical model well separating the physiological cartilage from the early osteoarthritic spots which are highlighted in the model.