Customized hybrid level sets for automatic lung segmentation in chest x-ray images

S. Kamalakannan; S. Antani; R. Long; G. Thoma

doi:10.1117/12.2001531

13 March 2013 Customized hybrid level sets for automatic lung segmentation in chest x-ray images

S. Kamalakannan, S. Antani, R. Long, G. Thoma

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 866939 (2013) https://doi.org/10.1117/12.2001531
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

A chest x-ray screening system for pulmonary pathologies such as tuberculosis (TB) is of paramount importance due to the increasing mortality rate of patients with undiagnosed TB, especially in densely-populated developing countries. As a first step toward developing such screening systems, this paper presents a novel computer vision module that automatically segments the lungs from posteroanterior digital chest x-ray images. The segmentation task is non-trivial, due to poor image contrast and occlusion of the lung region by ribs, clavicle, heart, and by non-TB abnormalities associated with pulmonary diseases. In the proposed procedure, we first compute a lung shape model by employing a level set based technique for registration up to a homography. Next, we use this computed mean lung shape to initialize the level set that is based on a best fit measure obtained in a heuristically estimated search space for the projective transform parameters. Once the level set is initialized, a suite of customized lower level image features and higher level shape features up to a homography evolve the level set function at a lower resolution in order to achieve a coarse segmentation of the lungs. Finally, a fine segmentation step is performed by adding additional shape variation constraints and evolving the level set in a higher resolution. We processed the standard Japanese Society of Radiological Technology (JSRT) dataset, comprised of 247 images, using this scheme. The promising results (92% accuracy) demonstrate the viability and efficacy of the proposed approach.

Citation Download Citation

S. Kamalakannan, S. Antani, R. Long, and G. Thoma "Customized hybrid level sets for automatic lung segmentation in chest x-ray images", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866939 (13 March 2013); https://doi.org/10.1117/12.2001531

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