A FRAMEWORK FOR MRI IMAGE RETRIEVAL USING CURVELET TRANSFORM AND EUCLIDEAN DISTANCE

Rajakumar Rajakumar

doi:10.3844/jcssp.2013.285.290

Abstract

Content Based Image Retrieval (CBIR) system supports users to retrieve relevant medical images based on their features. Current content based image retrieval systems are incapable of providing exact results to the users. To address this problem a consistent feature extraction method is required for content based medical image retrieval system to extract similar features from the images. In this study discrete curvelet based feature extraction technique is proposed to retrieve similar images and the similarity distance is calculated by using Euclidean distance. The proposed method gives better precision and recall rate. Experimental results on a database of 200 MRI images.

Highlights

To address this problem a consistent feature extraction method is required for content based medical image retrieval system to extract similar features from the images
In this study discrete curvelet based feature extraction technique is proposed to retrieve similar images and the similarity distance is calculated by using Euclidean distance
We present the performance of MRI image retrieval using discrete curvelet transform and euclidean distance method

Summary

Introduction

In the text-based image retrieval are many techniques for capturing medical images from system, the images are physically annotated by text patients to assist with diagnostic tasks. These include descriptors and used by a database system to perform Computed Tomography (CT), Magnetic Resonance image retrieval. There The majority of these techniques produce digital images, are inconsistencies between user textual queries and image which are archived and handled through Picture annotations. To alleviate these problems, the image Archiving and Communication Systems (PACS).

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