Distance measures for medical image retrieval

Abstract

AbstractImaging has occupied a huge role in the management of patients, whether hospitalized or not. Depending on the patient's clinical problem, a variety of imaging modalities were available for use. Radiology is the branch of medical science dealing with medical imaging. It may use X‐ray machines or other such radiation devices. It also uses techniques that do not involve radiation, such as magnetic resonance imaging (MRI) and ultrasound (US). Commonly used imaging modalities include plain radiography, computed tomography (CT), MRI, US, and nuclear imaging techniques. Each of these modalities has strengths and limitations which dictates its use in diagnosis. The usage of modality for a particular problem must be reviewed with emphasis on method of generating an image with costs, strengths and weaknesses, and associated risks. The reason for image retrieval is due to increase in acquisition of images. Physicians and radiologists feel better while using retrieval techniques for faster remedy in surgery and medicine due to the following reasons: giving details to the patients, searching the present and past records from the larger databases, and giving solutions to them in a faster and more accurate way. Similarity measures are one of the techniques that help us in retrieval of medical images. Similarity measures also termed as distance metrics, which plays an important role in CBIR and CBMIR. They calculate the visual similarities between the query image and images in the database which were ranked by their similarities with the query image. Different similarity measures have different effects in an image retrieval system significantly. So, it is important to find the best distance metrics for CBIR system. In this article, various distance methods were used and then they are compared for effective medical image retrieval. A double‐step approach is followed for effective retrieval. This article describes some easily computable distance measures for medical image retrieval using measures such as probability, mean, standard deviation, skew, energy, and entropy. The distance measures used are Euclidean, Manhattan, Mahalanobis, Canberra, Bray‐Curtis, squared chord, and Squared chi‐squared. Two kind of decision rules precision and accuracy were used for measuring retrieval. A dataset is created using various imaging modalities like CT, MRI, and US images. From the final results, it is very clear that each distance metric with each measures shows different results in retrieval of medical images. It is found that the distance metrics with all the measures shows different precision and recall value calculated from their retrieved medical images. The best retrieval results for Euclidean distance metric is only with probability measure showing 75% of precision and 30% of recall when comparing with other measures. The best retrieval results for Manhattan distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for Mahalanobis distance metric is only with probability as a measure giving 75% of precision and 30% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for Canberra distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for Bray‐Curtis distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for squared‐chord distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for squared chi‐chord distance metric is only with mean as a measure showing 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. These results indicate that these easily computable similarity distance measures have a wide variety of medical image retrieval applications. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 9–21, 2013