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Computer-aided TB Screening on Chest X-rays

banner image collage showing 4 pictures: portable x-ray device, mobile x-ray truck, chest-xray, and map of Kenya
Project information

We are collaborating with AMPATH (Academic Model Providing Access to Healthcare), an organization supported by USAID that runs the largest AIDS treatment program in the world. This project uses LHNCBC’s imaging research and system development to fulfill NIH global health policy objectives. Our objective is to leverage in-house expertise in image processing to screen HIV-positive patients in rural Kenya for evidence of pulmonary tuberculosis (TB) in chest x-rays. Since chest radiography is important to the detection of TB and other pulmonary infections prevalent in HIV-positive patients, we have provided AMPATH with lightweight digital x-ray units readily transportable in rural areas. Their staff will take chest x-rays (CXR) of the population and screen them for the presence of disease. These x-ray units are already on site in the Moi University Hospital in Eldoret, Kenya and are being readied for deployment. The team completed design of vehicles to transport the x-ray units – one vehicle is being outfitted as a mobile x-ray truck.

Since the lack of sufficient radiological services in the area suggests the utility of automation to perform the screening, our in-house research effort focuses on developing software to automatically screen for disease in the CXR images. Our researchers are developing algorithms to automatically segment the lungs, detect and remove ribs, heart, aorta and other structures and then detect texture features characteristic of pulmonary disease,. At present, the algorithms distinguish between 2 cases: abnormal vs. normal. After receiving IRB exemption, we obtained chest x-ray images to use as test and training sets: 400 from Montgomery County’s TB Control Program, 850 from a source in India, and 8,200 from Indiana University. We also acquired an open-source Japanese set containing about 250 x-ray images. We are in the process of receiving a few hundred images from China.

Using these x-rays for training and testing, we have developed algorithms for detecting lungs and ribs focusing on region-based features such as log Gabor wavelets that exploit the orientation of anatomical structures. For lung segmentation, we have developed algorithms using region-based level sets and a novel graph-cut segmentation method, yielding an accuracy of about 95%. A robust identification of lung shape plays a role in detecting TB in CXR since many abnormalities (e.g., pleural effusion) exhibit deformation in lung shape. After extracting the lung fields, the algorithm measures various geometric features that discriminate between normal and effused cases. Ongoing work is in identifying the most successful geometric features.

In parallel, we are developing a binary support vector machine (SVM) classifier that uses several features extracted from the x-rays as input, such as histograms of intensity, gradient magnitude and orientation, shape and curvature. Based on these input features, the SVM returns a confidence value, allowing an operator to inspect cases in which the classifier is uncertain. This initial classifier, showing an accuracy of about 80%, serves as our starting point for ongoing optimization.

Publications/Tools: 
KC S, Xue Z, Antani SK, Thoma GR. NLM at ImageCLEF 2015: Biomedical Multipanel Figure Separation. Editors: Cappellato, L., Ferro, N., Jones, G., and San Juan, E., CLEF 2015 Labs and Workshops, Notebook Papers. CEUR WorkshopProceedings (CEUR-WS.org), ISSN 1613-0073, http://ceur-ws.org/ Vol-1391/.
Jaeger S, Candemir S, Antani SK, Wang Y, Lu P, Thoma GR. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg. 2014 Dec;4(6):475-7. doi: 10.3978/j.issn.2223-4292.2014.11.20.
KC S, Candemir S, Jaeger S, Folio L, Karargyris A, Antani SK, Thoma GR. Rotation detection in chest radiographs based on generalized line histogram of rib-orientations. IEEE 27th International Symposium on Computer-Based Medical Systems, New York, NY, USA, May 27-29, 2014: page 138-142.
Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan FM, Xue Z, Palaniappan K, Singh RK, Antani SK, Thoma GR, Wang Y, Lu P, McDonald CJ. Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging. 2014 Feb;33(2):233-45. doi: 10.1109/TMI.2013.2284099. Epub 2013 Oct 1.
Candemir S, Jaeger S, Palaniappan K, Musco J, Singh RK, Xue Z, Karargyris A, Antani SK, Thoma GR, McDonald CJ. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 2014 Feb;33(2):577-90. doi: 10.1109/TMI.2013.2290491. Epub 2013 Nov 13.
Xue Z, Jaeger S, Karargyris A, Candemir S, Antani SK, Long LR, Thoma GR, McDonald CJ. A System for Automated Screening for Tuberculosis using Digital Chest X-rays for Resources-Constrained Regions. NIH Intramural Research Festival, Bethesda MD, November 6-8, 2013.
Karargyris A, Folio L, Siegelman J, Callaghan FM, Candemir S, Xue Z, Lu P-X, Wang Y, Antani SK, Thoma GR, Jaeger S. Comparing the Performance of Man and Machine for TB Screening in Chest Radiographs. NIH Intramural Research Festival, Bethesda MD, November 6-8, 2013.
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.
Jaeger S, Karargyris A, Candemir S, Siegelman J, Folio L, Antani S, Thoma G. Automatic screening for tuberculosis in chest radiographs: a survey. Quant Imaging Med Surg. 2013 Apr;3(2):89-99. doi: 10.3978/j.issn.2223-4292.2013.04.03.
Candemir S, Jaeger S, Palaniappan K, Antani SK, Thoma GR. Graph Cut Based Automatic Lung Boundary Detection in Chest Radiographs. 1st Annual IEEE Healthcare Innovation Conference of the IEEE EMBS Houston, Texas USA, 7 - 9 November, 2012.

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