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International Journal of Computer Assisted Radiology and Surgery

Published in:

01-01-2016 | Original Article

Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays

Authors: Alexandros Karargyris, Jenifer Siegelman, Dimitris Tzortzis, Stefan Jaeger, Sema Candemir, Zhiyun Xue, K. C. Santosh, Szilárd Vajda, Sameer Antani, Les Folio, George R. Thoma

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 1/2016

Abstract

Purpose

To improve detection of pulmonary and pleural abnormalities caused by pneumonia or tuberculosis (TB) in digital chest X-rays (CXRs).

Methods

A method was developed and tested by combining shape and texture features to classify CXRs into two categories: TB and non-TB cases. Based on observation that radiologist interpretation is typically comparative: between left and right lung fields, the algorithm uses shape features to describe the overall geometrical characteristics of the lung fields and texture features to represent image characteristics inside them.

Results

Our algorithm was evaluated on two different datasets containing tuberculosis and pneumonia cases.

Conclusions

Using our proposed algorithm, we were able to increase the overall performance, measured as area under the (ROC) curve (AUC) by 2.4 % over our previous work.

Available only for authorised users

Firefly was developed by University of Missouri, and it is an online annotating toolbox.

http://www.who.int/tb/publications/global_report/gtbr14_executive_summary.pdf?ua=1. Viewed in Dec 2014

Centers for Disease Control and Prevention—Testing for TB Infection. http://www.cdc.gov/tb/topic/testing/default.htm. Viewed in Dec 2014

Van Cleeff M, Kivihya-Ndugga L, Meme H, Odhiambo J, Klatser P (2005) The role and performance of chest X-ray for the diagnosis of tuberculosis: a cost-effectiveness analysis in Nairobi, Kenya. BMC Infect Dis 5:111. doi:10.1186/1471-2334-5-111 PubMedCentralCrossRefPubMed

Jaeger S, Karargyris A, Candemir S, Siegelman J, Folio L, Antani S, Thoma G (2013) Automatic screening for tuberculosis in chest radio-graphs: a survey. Quant Imaging Med Surg 3(2):89–99PubMedCentralPubMed

Ginneken B, Hogeweg L, Prokop M (2009) Computer-aided diagnosis in chest radiography: beyond nodules. Eur J Radiol 72(2):226–230CrossRefPubMed

Karargyris A, Antani S, Thoma G (2011) Segmenting anatomy in chest X-rays for tuberculosis screening. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, pp 7779–7782, Aug 30, 2011–Sept 3, 2011. doi:10.1109/IEMBS.2011.6091917

Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, Karargyris A, Antani S, Thoma G, McDonald CJ (2014) Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging 33(2):577–590CrossRefPubMed

Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan F, Xue Z, Palaniappan K, Singh RK, Antani S, Thoma G, Wang Y-X, Pu-Xuan L, McDonald CJ (2014) Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging 33(2):233–245CrossRefPubMed

Santosh KC, Candemir S, Jaeger S, Karargyris A, Antani S, Folio L, Thoma G (2015) Automatically detecting rotation in chest radiographs using principal rib-orientation measure for quality control. Int J Pattern Recognit Artif Intell. doi:10.1142/S0218001415570013

10.

Radon Transform. http://mathworld.wolfram.com/RadonTransform.html. Viewed in Jan 2015

11.

Bhattacharyya distance. http://en.wikipedia.org/wiki/Bhattacharyya_distance. Viewed in Jan 2015

12.

Beard D (2009) Firefly—web-based interactive tool for the visualization and validation of image processing algorithms. M.S. thesis, Univ. Missouri, Columbia

13.

Folio LR (2012) Chest imaging: an algorithmic approach to learning. Springer, BerlinCrossRef

14.

The MathWorks, Inc., Matlab—measure properties of image regions. http://www.mathworks.com/help/images/ref/regionprops.html

15.

http://www.jsrt.or.jp/jsrt-db/eng.php. Viewed in Dec 2014

Title: Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays
Authors: Alexandros Karargyris
Jenifer Siegelman
Dimitris Tzortzis
Stefan Jaeger
Sema Candemir
Zhiyun Xue
K. C. Santosh
Szilárd Vajda
Sameer Antani
Les Folio
George R. Thoma
Publication date: 01-01-2016
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 1/2016
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-015-1242-x

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays

Abstract

Purpose

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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