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%.
CITATION STYLE
Zhang, Q., Bhalerao, A., Parsons, C., Helm, E., & Hutchinson, C. (2016). Wavelet appearance pyramids for landmark detection and pathology classification: Application to lumbar spinal stenosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9901 LNCS, pp. 274–282). Springer Verlag. https://doi.org/10.1007/978-3-319-46723-8_32
Mendeley helps you to discover research relevant for your work.