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

被引：78

作者：

Karargyris, Alexandros ^{[1
]}

Siegelman, Jenifer ^{[2
,3
]}

Tzortzis, Dimitris ^{[4
]}

Jaeger, Stefan ^{[1
]}

Candemir, Sema ^{[1
]}

Xue, Zhiyun ^{[1
]}

Santosh, K. C. ^{[1
]}

Vajda, Szilard ^{[1
]}

Antani, Sameer ^{[1
]}

Folio, Les ^{[5
]}

Thoma, George R. ^{[1
]}

机构：

[1] Natl Lib Med, Lister Hill Natl Ctr Biomed Commun, Commun Engn Branch, NIH, Bldg 10, Bethesda, MD 20892 USA

[2] Brigham & Womens Hosp, Dept Radiol, Div Emergency Radiol, 75 Francis St, Boston, MA 02115 USA

[3] Harvard Univ, Sch Med, Ctr Evidence Based Imaging, Boston, MA USA

[4] Gen Hosp Athens KAT, Ugeianet Diagnost Ctr, Athens, Greece

[5] NIH, Ctr Clin, Dept Radiol, Bethesda, MD 20892 USA

来源：

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY | 2016年 / 11卷 / 01期

关键词：

Tuberculosis; Screen; Software; Remote; Telemedicine; TUBERCULOSIS; RADIOGRAPHS;

D O I：

10.1007/s11548-015-1242-x

中图分类号：

R318 [生物医学工程];

学科分类号：

100103 [病原生物学];

摘要：

To improve detection of pulmonary and pleural abnormalities caused by pneumonia or tuberculosis (TB) in digital chest X-rays (CXRs). 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. Our algorithm was evaluated on two different datasets containing tuberculosis and pneumonia cases. 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.

引用

页码：99 / 106

页数：8

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