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Hybrid Oriented FAST Rotated BRIEF and Non-Rotational-Invariant Uniform Local Binary Patterns for CBMIR

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Abstract

In recent decades, Content-Based Image Retrieval (CBIR) is an emerging topic, which assists the clinicians in performing fast diagnosis by conducting quantitative assessment of various modalities. The major difficulty in CBIR system is the semantic space among low-level visual detail and high-level semantic detail. To solve these issues, ORB-NULBP approach is implemented for retrieving medical images from Contrast Enhanced Magnetic Resonance Images (CE-MRI) database. Further, the image classification and the similarity between CE-MRI database and query image are performed using random forest classifier and chi-square distance measure to retrieve the relevant medical images. In the experimental section, ORB-NULBP approach attained 98.89% of retrieval accuracy, 99.55% of specificity, and 99.02% of sensitivity on the CE-MRI database, which are significant compared to the Gaussian Naïve Bayes method. The proposed ORB-NULBP approach has achieved better retrieval performance with limited computational time and complexity. As the future extension, the extracted feature vectors of ORB-NULBP approach are organized as indices for faster medical image retrieval.

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This study was not funded by any organization.

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  1. Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India

    Faiyaz Ahmad & Tanvir Ahmad

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  1. Faiyaz Ahmad
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  2. Tanvir Ahmad
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Ahmad, F., Ahmad, T. Hybrid Oriented FAST Rotated BRIEF and Non-Rotational-Invariant Uniform Local Binary Patterns for CBMIR. J. Inst. Eng. India Ser. B 103, 1949–1959 (2022). https://doi.org/10.1007/s40031-022-00787-7

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  • DOI: https://doi.org/10.1007/s40031-022-00787-7

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