FRACTAL TEXTURE ANALYSIS IN COMPUTER-AIDED DIAGNOSIS OF SOLITARY PULMONARY NODULES

Rationale and Objectives. 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-suspic ious-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 fr actal dimension of each possible nodule provided statistically signifi cant (P < .05) differentiation between regions that contained an artif icial nodule and those that did not. The area under the receiver opera ting characteristic curve for the fractal analysis was significantly b etter (P < .05) than that for the radiologists. Conclusion. Fractal te xture characterization provides useful information for the classificat ion of potential solitary pulmonary nodules with CAD algorithms.