Abstract
In this study, we propose a content-based medical image retrieval framework based on binary association rules to augment the results of medical image diagnosis, for supporting clinical decision making. Specifically, this work is employed on scanned Magnetic Resonance brain Images (MRI) and the proposed Content Based Image Retrieval (CBIR) process is for enhancing relevancy rate of retrieved images. The pertinent features of a query brain image are extracted by applying third order moment invariant functions, which are then examined with the selected feature indexes of large medical image database for appropriate image retrieval. Binary association rules are incorporated here for organizing and marking the significant features of database images, regarding a specific criterion. Trigonometric function distance similarity measurement algorithm is applied to improve the accuracy rate of results. Moreover, the performances of classification and retrieval methods are determined in terms of precision and recall rates. Experimental results reveal the efficacy of the adduced methodology as compared to the related works.
Highlights
In current decade, the enormous growth of digital images related to clinical diagnosis has led to the development of efficient image retrieval systems
We propose a content-based medical image retrieval framework based on binary association rules to augment the results of medical image diagnosis, for supporting clinical decision making
This work is employed on scanned Magnetic Resonance brain Images (MRI) and the proposed Content Based Image Retrieval (CBIR) process is for enhancing relevancy rate of retrieved images
Summary
The enormous growth of digital images related to clinical diagnosis has led to the development of efficient image retrieval systems. We engage moment invariant feature extraction methods in our work since it involves in shape discrimination based on some unique features of brain images Depending on those features, the feature vector is evaluated and given as the index for further classification of brain images under normal, benign and malignant classes. The AR subsumes in supporting better decision making on medical image diagnosis In this method of tumor detection, each training image is combined with a set of keywords, which are the representative terms preferred by the specialists for accurate results.
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