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A Combined Approach for Lung Boundary Segmentation of Chest X-Ray Images.

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NIH Intramural Research Festival, Bethesda MD, November 6-8, 2013.
Abstract: 

In collaboration with the Indiana University School of Medicine, AMPATH, the largest AIDS treatment program in the third world), and Moi University in Eldoret, the National Library of Medicine is developing a digital chest x-ray screening software with a focus on detecting tuberculosis. The software is intended for deployment on low-cost transport vehicles in remote areas in Kenya to help screen populations using mobile x-ray scanners. Our software consists of various image-processing algorithms. In this poster we are presenting a lung boundary detection method using a combination of techniques based on graph-cut and elastic registration to achieve state-of-the-art performance. More specifically, the method consists of a fast image retrieval method for identifying training images (with masks) most similar to the patient CXR using Bhattacharyya shape similarity measure and image correlation, creating the initial patient-specific anatomical atlas using elastic registration using cubic B-splines, building refined lung boundaries using a graph cut optimization approach with a customized energy function. We validated the accuracy of the approach with extensive experiments on public and NLM x-ray datasets. The average accuracy of the system is above 95% on the public dataset (Jaccard similarity coefficient) and comparable with the state-of-the-art.

Karargyris A, Candemir S, Jaeger S, Xue Z, Antani SK, Thoma GR. A Combined Approach for Lung Boundary Segmentation of Chest X-Ray Images. NIH Intramural Research Festival, Bethesda MD, November 6-8, 2013.