The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography.
In this project, patients with a solitary pulmonary nodule, were imaged using high resolution computed tomography. Quantitative measures of texture were extracted from these images using co-occurrence matrices. These matrices were formed with different combinations of gray level quantization, distance between pixels and angles. The derived measures were input to a linear discriminant classifier to predict the classification (benign or malignant) of each nodule. Using a relative quantization scheme with eight levels, four features yielded an area under the ROC curve (Az) of 0.992; 93.8% (30/32) of cases were correctly classified when training and testing on the same cases; while 90.6% (29/32) were correctly classified when jackknifing was used.
McNitt-Gray
M F
MF
Department of Radiological Sciences, B3-227U Center for Health Sciences, UCLA School of Medicine, Los Angeles, CA 90095-1721, USA. mmcnittgray@mednet.ucla.edu
Wyckoff
N
N
Sayre
J W
JW
Goldin
J G
JG
Aberle
D R
DR
D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D006801:Humans; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D012984:Software; D003074:Solitary Pulmonary Nodule / Q000145:classification / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*