MR-brain image meticulous classification based on support vector machine
Wufan Chen · Journal of Circuits and Systems · 2010
This paper proposes a meticulous classification of MR-brain images using support vector machine (SVM). We use both texture and shape feature to express images, and then apply statistical association rule miner (StARMiner) algorithm to compute weight coefficient of each feature. A classifier based on SVM is trained, the parameters of which are optimized via many experiments. The result of glancing classification could achieve 92.10%. Meticulous classification can be applied in special body part retrieval system for retrieving more accurate images and reducing computational load.