TY - GEN
T1 - Toward the detection of abnormal chest radiographs the way radiologists do it
AU - Alzubaidi, Mohammad
AU - Patel, Ameet
AU - Panchanathan, Sethuraman
AU - Black, John A.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Computer Aided Detection
KW - Computer Aided Diagnosis
KW - chest x-rays
KW - machine learning
KW - radiology training
UR - http://www.scopus.com/inward/record.url?scp=79955751816&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79955751816&partnerID=8YFLogxK
U2 - 10.1117/12.878256
DO - 10.1117/12.878256
M3 - Conference contribution
AN - SCOPUS:79955751816
SN - 9780819485052
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2011
T2 - Medical Imaging 2011: Computer-Aided Diagnosis
Y2 - 15 February 2011 through 17 February 2011
ER -