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

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,...

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Vydáno v:Expert systems Ročník 39; číslo 7
Hlavní autoři: Sampathila, Niranjana, Pavithra, Martis, Roshan Joy
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.08.2022
Témata:
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
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Shrnutí: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.
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ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.12652