Distance measures for medical image retrieval

Imaging 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 re