Local Structure-Based Region-of-Interest Retrieval in Brain MR Images | IEEE Journals & Magazine | IEEE Xplore

Local Structure-Based Region-of-Interest Retrieval in Brain MR Images


Abstract:

The aging population and the growing amount of medical data have increased the need for automated tools in the neurology departments. Although the researchers have been d...Show More

Abstract:

The aging population and the growing amount of medical data have increased the need for automated tools in the neurology departments. Although the researchers have been developing computerized methods to help the medical expert, these efforts have primarily emphasized to improve the effectiveness in single patient data, such as computing a brain lesion size. However, patient-to-patient comparison that should help improve diagnosis and therapy has not received much attention. To this effect, this paper introduces a fast and robust region-of-interest retrieval method for brain MR images. We make the following various contributions to the domains of brain MR image analysis, and search and retrieval system: 1) we show the potential and robustness of local structure information in the search and retrieval of brain MR images; 2) we provide analysis of two complementary features, local binary patterns (LBPs) and Kanade-Lucas-Tomasi feature points, and their comparison with a baseline method; 3) we show that incorporating spatial context in the features substantially improves accuracy; and 4) we automatically extract dominant LBPs and demonstrate their effectiveness relative to the conventional LBP approach. Comprehensive experiments on real and simulated datasets revealed that dominant LBPs with spatial context is robust to geometric deformations and intensity variations, and have high accuracy and speed even in pathological cases. The proposed method can not only aid the medical expert in disease diagnosis, or be used in scout (localizer) scans for optimization of acquisition parameters, but also supports low-power handheld devices.
Page(s): 897 - 903
Date of Publication: 08 January 2010

ISSN Information:

PubMed ID: 20064763

I. Introduction

The advances in the medical imaging technology allow for in vivo visualization and analysis of human body with unprecedented accuracy and resolution. A diagnosis by a specialist often requires a visit to a radiology department to obtain various images that highlight the suspected pathology. Despite the high resolution of the acquired images, image-based diagnosis often utilizes a considerable amount of qualitative measures. To improve the diagnosis and efficiency, the research in medical image analysis has focused on the computation of quantitative measures by automating some of the error-prone and time-consuming tasks, such as segmentation of a structure.

References

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