This paper presents a detailed study about a biomedical image retrieval framework by extracting Phase Congruency (PC) features from L*a*b* triplets of images (query, target) and representing them in fuzzy feature space. These features correspond to an edge-corner map of the given image. The resulting map is then processed by Scale Invariant Feature Transform (SIFT) to derive keypoints, that are invariant to affine transformations. The ensuing features were vector quantized to build a codebook of keypoints. The codebook was produced using a Spherical Self-Organizing Map (SOM) built with a geodesic data structure termed as GeoSOM. Then keypoints of the query image are mapped with the codebook and their occurrences are counted to formulate a histogram termed as Phase Congruency-based Bag of Keypoints (PC-BoK). This histogram is generated offline for target images and a similarity measure was performed with the query image to yield the nearest match based on a global fuzzy membership function. Exhaustive experiments of the proposed framework named as BIRS (Biomedical Image Retrieval System) were performed on a diverse medical image collection (NBIA, MESSIDOR, DRIVE). Finally, performance of BIRS demonstrates the advantage of the proposed image representation approach in terms of Precision (P)–Recall (R) parameters. Furthermore relative comparison of the proposed scheme with existing feature descriptors depicts improved P–R values. The proposed feature extraction and representation scheme was also robust against quantization errors. 
An effective biomedical image retrieval framework in a fuzzy feature space employing Phase Congruency and GeoSOM