Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives
This work addresses improving diagnostic decisions and education in medical imaging through better image retrieval, but it is incremental as it builds on existing Gabor-Radon methods.
The paper tackled content-based image retrieval in medical imaging by using a two-stage approach with Gabor filters on Radon-transformed images and a multi-class SVM for classification, achieving efficient retrieval on the IRMA dataset with over 14,000 images.
Content-based image retrieval (CBIR) of medical images in large datasets to identify similar images when a query image is given can be very useful in improving the diagnostic decision of the clinical experts and as well in educational scenarios. In this paper, we used two stage classification and retrieval approach to retrieve similar images. First, the Gabor filters are applied to Radon-transformed images to extract features and to train a multi-class SVM. Then based on the classification results and using an extracted Gabor barcode, similar images are retrieved. The proposed method was tested on IRMA dataset which contains more than 14,000 images. Experimental results show the efficiency of our approach in retrieving similar images compared to other Gabor-Radon-oriented methods.