A Hybrid Knowledge-Guided Detection Technique for Screening of Infectious Pulmonary Tuberculosis From Chest Radiographs

作者：Shen Rui; Cheng Irene; Basu Anup

来源：IEEE Transactions on Biomedical Engineering, 2010, 57(11): 2646-2656.

DOI：10.1109/TBME.2010.2057509

• Classification
• computer-aided detection (CAD)
• image segmentation
• tuberculosis (TB) detection

Tuberculosis (TB) is a deadly infectious disease and the presence of cavities in the upper lung zones is a strong indicator that the disease has developed into a highly infectious state. Currently, the detection of TB cavities is mainly conducted by clinicians observing chest radiographs. Diagnoses performed by radiologists are labor intensive and very often there is insufficient health care personnel available, especially in remote communities. After assessing existing approaches, we propose an automated segmentation technique, which takes a hybrid knowledge-based Bayesian classification approach to detect TB cavities automatically. We apply gradient inverse coefficient of variation and circularity measures to classify detected features and confirm true TB cavities. By comparing with nonhybrid approaches and the classical active contour techniques for feature extraction in medical images, experimental results demonstrate that our approach achieves high accuracy with a low false positive rate in detecting TB cavities.

• 出版日期2010-11