An Improved and Efficient Content-Based Medical Image Retrieval Technique vs State-of-Art Texture Feature Extraction Techniques

Abstract

Areas in the health sector such as X-rays, Dermatology, High-resolution Computed Tomography – HRCT, Endoscopy, Radiology, Cardiology and Magnetic Resonance Imaging – MRI heavily depend on medical images for their activities hence it has become complex for managing and accessing these images from their repositories. As a matter of concern content-based medical image retrieval-CBMIR has been the system proposed by many researchers for handling access to similar medical image(s) as the input image, yet there is a caveat that has to be addressed with respect to which technique best suits CBMIR system with respect to a given performance metric. Medical images are mostly of grayscale and therefore color feature extraction techniques may not work effectively on them. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric, this work seeks to comparatively evaluate the performance of the following state-of-the-art texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and a Proposed Technique using the metrics; Precision, Recall, F1-score, Mean Squared Error (MSE), Accuracy and Time. The results showed that the proposed technique is best suited for systems focusing on precision with an average precision score of 100% over 10.5k of raw medical images (dataset) using an appreciable minimum time with time complexity of 0(n).