Open Access Research

Novel coarse-to-fine dual scale technique for tuberculosis cavity detection in chest radiographs

Tao Xu1, Irene Cheng2, Richard Long3 and Mrinal Mandal1*

Author Affiliations

1 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2V4, Canada

2 Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada

3 Division of Pulmonary Medicine, Department of Medicine, University of Alberta, Edmonton, AB, T6G 2V2, Canada

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EURASIP Journal on Image and Video Processing 2013, 2013:3  doi:10.1186/1687-5281-2013-3

Published: 8 January 2013

Abstract

Although many lung disease diagnostic procedures can benefit from computer-aided detection (CAD), current CAD systems are mainly designed for lung nodule detection. In this article, we focus on tuberculosis (TB) cavity detection because of its highly infectious nature. Infectious TB, such as adult-type pulmonary TB (APTB) and HIV-related TB, continues to be a public health problem of global proportion, especially in the developing countries. Cavities in the upper lung zone provide a useful cue to radiologists for potential infectious TB. However, the superimposed anatomical structures in the lung field hinder effective identification of these cavities. In order to address the deficiency of existing computer-aided TB cavity detection methods, we propose an efficient coarse-to-fine dual scale technique for cavity detection in chest radiographs. Gaussian-based matching, local binary pattern, and gradient orientation features are applied at the coarse scale, while circularity, gradient inverse coefficient of variation and KullbackÔÇôLeibler divergence measures are applied at the fine scale. Experimental results demonstrate that the proposed technique outperforms other existing techniques with respect to true cavity detection rate and segmentation accuracy.

Keywords:
Classification; Segmentation; Computer-aided detection (CAD); Tuberculosis (TB)