Fractal texture analysis in computer-aided diagnosis of solitary pulmonary nodules.
OBJECTIVE
The authors investigated the use of fractal texture characterization to improve the accuracy of solitary pulmonary nodule computer-aided diagnosis (CAD) systems.


METHODS
Thirty chest radiographs were acquired from patients who had no pulmonary nodules. Thirty regions were selected that were considered remotely suspicious-looking for nodules. Artificial nodules of multiple shapes, sizes, and orientations were added at subtle levels of contrast to 30 non-suspicious-looking regions of the radiographs. Fractal dimensions of the 60 "nodule candidates" were calculated to quantify the texture of each region. Four radiologists also interpreted the images.


RESULTS
The fractal dimension of each possible nodule provided statistically significant (P < .05) differentiation between regions that contained an artificial nodule and those that did not. The area under the receiver operating characteristic curve for the fractal analysis was significantly better (P < .05) than that for the radiologists.


CONCLUSIONS
Fractal texture characterization provides useful information for the classification of potential solitary pulmonary nodules with CAD algorithms.
Vittitoe
N F
NF
Department of Biomedical, Engineering, Duke University, Durham, NC 27710, USA.
Baker
J A
JA
Floyd
C E
CE
D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017709:Fractals; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D011859:Radiography; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*