Appearance representation and feature extraction of anatomy or anatomical features is a key step for segmentation and classification tasks. We focus on an advanced appearance model in which an object is decomposed into pyramidal complementary channels, and each channel is represented by a part-based model. We apply it to landmark detection and pathology classification on the problem of lumbar spinal stenosis. The performance is evaluated on 200 routine clinical data with varied pathologies. Experimental results show an improvement on both tasks in comparison with other appearance models. We achieve a robust landmark detection performance with average point to boundary distances lower than 2 pixels, and image-level anatomical classification with accuracies around 85%.
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