13 March 2013 Customized hybrid level sets for automatic lung segmentation in chest x-ray images
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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
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.
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S. Kamalakannan, S. Kamalakannan, S. Antani, S. Antani, R. Long, R. Long, G. Thoma, 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); doi: 10.1117/12.2001531; https://doi.org/10.1117/12.2001531

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