AI Article Synopsis
- The study aims to enhance computer-aided diagnosis (CAD) systems for detecting solitary pulmonary nodules by using fractal texture characterization.
- Thirty chest radiographs were analyzed, where artificial nodules were added to non-suspicious areas, and the fractal dimensions of these regions were calculated.
- The results showed that fractal dimensions could significantly differentiate between regions with and without artificial nodules, outperforming the interpretations of radiologists in accuracy.
Article Abstract
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-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.
Conclusion: Fractal texture characterization provides useful information for the classification of potential solitary pulmonary nodules with CAD algorithms.
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| http://dx.doi.org/10.1016/s1076-6332(97)80005-0 | DOI Listing |
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