Toward the detection of abnormal chest radiographs the way radiologists do it

Mohammad Alzubaidi; Ameet Patel; Sethuraman Panchanathan; John A. Black Jr.

doi:10.1117/12.878256

9 March 2011 Toward the detection of abnormal chest radiographs the way radiologists do it

Mohammad Alzubaidi, Ameet Patel, Sethuraman Panchanathan, John A. Black Jr.

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796337 (2011) https://doi.org/10.1117/12.878256
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Computer Aided Detection (CADe) and Computer Aided Diagnosis (CADx) are relatively recent areas of research that attempt to employ feature extraction, pattern recognition, and machine learning algorithms to aid radiologists in detecting and diagnosing abnormalities in medical images. However, these computational methods are based on the assumption that there are distinct classes of abnormalities, and that each class has some distinguishing features that set it apart from other classes. However, abnormalities in chest radiographs tend to be very heterogeneous. The literature suggests that thoracic (chest) radiologists develop their ability to detect abnormalities by developing a sense of what is normal, so that anything that is abnormal attracts their attention. This paper discusses an approach to CADe that is based on a technique called anomaly detection (which aims to detect outliers in data sets) for the purpose of detecting atypical regions in chest radiographs. However, in order to apply anomaly detection to chest radiographs, it is necessary to develop a basis for extracting features from corresponding anatomical locations in different chest radiographs. This paper proposes a method for doing this, and describes how it can be used to support CADe.

Citation Download Citation

Mohammad Alzubaidi, Ameet Patel, Sethuraman Panchanathan, and John A. Black Jr. "Toward the detection of abnormal chest radiographs the way radiologists do it", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796337 (9 March 2011); https://doi.org/10.1117/12.878256

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