Multilayer descriptors for medical image classification
In this paper, we propose a new method for improving the performance of 2D descriptors by building an n-layer image using different preprocessing approaches from which multilayer descriptors are extracted and used as feature vectors for training a Support Vector Machine. The different preprocessing approaches are used to build different n-layer images (n=3, n=5, etc.). We test both color and gray-level images, two well-known texture descriptors (Local Phase Quantization and Local Binary Pattern), and three of their variants suited for n-layer images (Volume Local Phase Quantization, Local Phase Quantization Three-Orthogonal-Planes, and Volume Local Binary Patterns). Our results show that multilayers and texture descriptors can be combined to outperform the standard single-layer approaches. Experiments on 10 datasets demonstrate the generalizability of the proposed descriptors. Most of these datasets are medical, but in each case the images are very different. Two datasets are completely unrelated to medicine and are included to demonstrate the discriminative power of the proposed descriptors across very different image recognition tasks. A MATLAB version of the complete system developed in this paper will be made available at https://www.dei.unipd.it/node/2357.
Information Technology and Cybersecurity
Ensemble, Local binary patterns, Local phase quantization, Multilayer descriptors, Support vector machine, Texture descriptors
Lumini, Alessandra, Loris Nanni, and Sheryl Brahnam. "Multilayer descriptors for medical image classification." Computers in Biology and Medicine 72 (2016): 239-247.
Computers in Biology and Medicine