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.

中文翻译：

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基于内容的脑MR图像数据库中K相似图像检索的计算方法

基于内容的医学图像检索 (CBMIR) 是一种处理各种医学成像模式中生成的大量图像数据的机制。近年来，由于计算机视觉和数字成像方式的发展，产生了大量的医学图像。因此，由于图像大小和形状的变化，从大型图像数据库中检索医学图像的任务变得更加繁琐。因此，有必要设计一个合适的医学图像检索系统。在本文中，提出了一种使用图像特征（如颜色、形状和纹理）的 CBIR 方法来表示和检索与给定查询图像相关的大型数据库中的图像。该方法用于检索不同平面（冠状面、冠状面、矢状和横向）来自正常和痴呆受试者的数据集。这些特征是根据基于灰度共现的 Haralik 特征和基于直方图的累积分布函数 (CDF) 确定的。图像检索机制是使用 K-Nearest Neighbor 算法通过查找查询图像和数据库图像之间的最小距离来设计的。计算精度和召回率等性能参数。获得了 95.5% 的平均准确率。所提供的结果确保了将其用作放射科医师在放射学图像检索和分类中的辅助框架的能力。图像检索机制是使用 K-Nearest Neighbor 算法通过查找查询图像和数据库图像之间的最小距离来设计的。计算精度和召回率等性能参数。获得了 95.5% 的平均准确率。所提供的结果确保了将其用作放射科医师在放射学图像检索和分类中的辅助框架的能力。图像检索机制是使用 K-Nearest Neighbor 算法通过查找查询图像和数据库图像之间的最小距离来设计的。计算精度和召回率等性能参数。获得了 95.5% 的平均准确率。所提供的结果确保了将其用作放射科医师在放射学图像检索和分类中的辅助框架的能力。