Abstract
Local Tetra Pattern (LTrP) is an image retrieval and indexing algorithm for content based image retrieval (CBIR) which made a significant improvement in the precision and recall rates of the retrieved images. Enhanced LTrP for Image Retrieval (ELIR) proposes a novel method of image retrieval by adding additional features to LTrP together with the features of coarseness, contrast, directionality and busyness. The experimental results show that precision and recall of image retrieval improved from that of using LTrP alone.
Highlights
Physicians and researchers take data from these images for studying a case. This lead to a new field of research called Medical Image Retrieval (MIR)
In this paper we propose a novel method of image retrieval by constructing a feature vector using Local Tetra Pattern (LTrP), perceptual textural features and more features generated using shift operation on LTrP
The precision improved from 28.95% to 38.62% and the recall improved from 47.79% to 50.42%
Summary
Medical imaging technology has seen great advancement in recent years. The use of CT, MRI, X-ray imaging help diagnose diseases and to provide adequate treatment to patients. It is necessary to acquire, analyze, classify and store these images. Physicians and researchers take data from these images for studying a case. This lead to a new field of research called Medical Image Retrieval (MIR). This is similar to earlier existing image retrieval methods. Retrieval in MIR must be more accurate as it is a real time application
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