Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Fractal Analysis of Image Textures for Indexing and Retrieval by Content
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
ACM Transactions on Knowledge Discovery from Data (TKDD)
Artificial Intelligence in Medicine
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This paper proposes a new feature extraction method: the Fast Fractal Stack, or FFS. The extraction algorithm consists in decomposing the input grayscale image into a stack of binary images from which the fractal dimension values are computed, resulting in a compact and highly descriptive set of features. We evaluated FFS for the task of classification of interstitial lung diseases in computed tomography (CT) scans, applied on a database of 248 CT images from 67 patients. The proposed approach performs well, improving the classification accuracy when compared to other feature extraction algorithms. Additionally, the FFS extraction algorithm is efficient, with a computational cost linear with respect to input image size.