Fuzzy-Based Medical X-ray Image Classification

In this paper a novel fuzzy scheme for medical x-ray image classification is presented. In this method, any image is partitioned in to 25 overlapping subimages and then shape-texture features are extracted from shape and directional information extracted from any subimage. In the classification stage, we apply a fuzzy membership to any subimage with respect to Euclidean distance between feature vector of any subimage and average of feature vectors of training subimages. At last, the summation of fuzzy memberships in any test image is obtained and its maximum can be used to classify the test image. The proposed method is evaluated for image classification on 2215 radiographic images from IRMA dataset with 195 training samples and 2020 test samples. Classification accuracy rate obtained by fuzzy classifier is much higher than.