Computational approach for content‐based image retrieval of K‐similar images from brain MR image database

• Published in: Expert systems Vol. 39; no. 7
• Main Authors: Sampathila, Niranjana, Pavithra, Martis, Roshan Joy
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.08.2022
• Subjects: Algorithms; Brain; CBMIR; Computer vision; Digital computers; Digital imaging; Distribution functions; Histograms; Image classification; Image databases; Image management; Image retrieval; K nearest neighbour; Magnetic resonance imaging; Mathematical analysis; medical image; Medical imaging; MRI; query image
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.12652

Content‐based medical image retrieval (CBMIR) is a mechanism to handle a huge quantity of image data generated in various medical imaging modalities. In recent years, due to the evolution of computer vision and digital imaging modalities, a large number of medical images are generated. Consequently, the task of retrieving medical images from a large image database becomes more tedious due to variation in the size and shape of the images. Hence, it is necessary to design an appropriate system for medical image retrieval. In this paper a methodology for CBMIR using features of an image such as colour, shape, and texture is proposed to represent and retrieve the images from a large database that are relevant to a given query image. This methodology is evaluated for the application of retrieving the brain MRI images of different planes (coronal, sagittal, and transverse) from a dataset of normal and demented subjects. The features are determined in terms of Grey level co‐occurrence based Haralik's features and histogram based cumulative distribution function (CDF). The image retrieval mechanism is designed using the K‐Nearest Neighbour algorithm by finding the minimum distance between query and database images. The performance parameters such as precision and recall are calculated. The average accuracy of 95.5% are obtained. The results provided ensures the capability to use it as assistive framework for radiologists in radiology image retrieval and classification.