[Show abstract][Hide abstract] ABSTRACT: The purpose of this study is to investigate if fractal texture analysis can assist in the diagnostic interpretation of perfusion lung scans. Forty-five perfusion scans were acquired from patients with clinical suspicion of acute pulmonary embolism (PE) who underwent pulmonary angiography for final diagnosis. Fractal texture analysis was performed on 270 regions of interest (ROIs) extracted from the posterior view of the lung scans. Specifically, there were 94 normally perfused ROIs and 176 abnormal ROIs representing various lung diseases including PE and obstructive pulmonary disease (OPD). The average fractal dimension (FD) of normal ROIs was statistically significantly higher than that of abnormal ROIs. Furthermore, the FDs of abnormal ROIs with PE were significantly lower than the FDs of ROIs with OPD present.
Computers and Biomedical Research 07/2000; 33(3):161-71. DOI:10.1006/cbmr.2000.1542
[Show abstract][Hide abstract] ABSTRACT: Previously, the authors presented an algorithm that identifies lung regions in a digitized posteroanterior chest radiograph (DCR) by labeling each pixel as either lung or nonlung. In this manuscript, the inherent flexibility of this algorithm is demonstrated as the algorithm is generalized to identify multiple anatomical regions in a DCR. Specifically, each pixel is classified as belonging to one of six anatomical region types: lung, subdiaphragm, heart, mediastinum, body, or background. The algorithm determines the optimal set of pixel classifications, xOPT, for a given set of DCR pixel gray level values y via a probabilistic approach that defines xOPT as the particular segmentation that maximizes the conditional distribution P(x/y). A spatially varying Markov random field (MRF) model is used that incorporates spatial and textural information of each possible region type. MRF modeling provides the form of P(x/y), and Iterated Conditional Modes is used to converge to the distribution maximum of P(x/y) thus obtaining the optimal segmentation for a given DCR. Results show the algorithm being able to correctly classify 90.0% +/- 3.4% of the pixels in a DCR.
Medical Physics 09/1999; 26(8):1670-7. DOI:10.1118/1.598673 · 2.64 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This study was performed to determine physical characteristics of areas on chest radiographs that are suspicious but not definitive for the presence of a pulmonary nodule and the characteristics of areas that contain an obvious nodule.
Two groups of patients were identified: those who had an area at plain radiography that was suspicious for a pulmonary nodule and underwent fluoroscopy for further evaluation (138 patients, 142 areas) and those who had an obvious nodule at plain radiography who underwent computed tomography for further evaluation (72 patients, 97 areas). The measured characteristics of the region of interest included size, circularity, compactness, contrast, and location.
A comparison of the data show that while there was some difference between these groups of patients with regard to location of the nodules, there were essentially no differences with regard to size, circularity, compactness, and contrast of the regions of interest.
Size, circularity, compactness, contrast, and location are not sufficient to distinguish pulmonary nodules from other suspicious regions on the chest radiograph.
[Show abstract][Hide abstract] ABSTRACT: The authors present an algorithm utilizing Markov random field modeling for identifying lung regions in a digitized chest radiograph (DCR). Let x represent the classifications of each pixel in a DCR as either lung or nonlung. We model x as a realization of a spatially varying Markov random field. This model is developed utilizing spatial and textural information extracted from samples of lung and nonlung region-types in a training set of DCRs. With this model, the technique of Iterated Conditional Modes is used to determine the optimal classification of each pixel in a DCR. The algorithm's ability to identify lung regions is evaluated on a testing set of DCRs. The algorithm performs well yielding a sensitivity of 90.7% +/- 4.4%, a specificity of 97.2% +/- 2.0%, and an accuracy of 94.8% +/- 1.6%. In an attempt to gain insight into the meaning and level of the algorithm's performance numbers, the results are compared to those of some easily implemented classification algorithms.
Medical Physics 07/1998; 25(6):976-85. DOI:10.1118/1.598405 · 2.64 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The authors investigated the use of fractal texture characterization to improve the accuracy of solitary pulmonary nodule computer-aided diagnosis (CAD) systems.
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.
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.
Fractal texture characterization provides useful information for the classification of potential solitary pulmonary nodules with CAD algorithms.
[Show abstract][Hide abstract] ABSTRACT: We present a computer-aided diagnostic technique for identifying nodular interstitial lung disease on chest radiographs. The fractal dimension was used as a numerical measure of image texture on digital chest radiographs to distinguish patients with normal lung from those with a diffuse nodular interstitial abnormality.
Twenty digitized chest radiographs were classified as normal (n = 10) or as containing diffuse nodular abnormality (n = 10) on the basis of readings assigned according to the classification of the International Labour Organization. Regions of interest (ROIs) measuring 1.28 cm2 were selected from the intercostal spaces of these radiographs. The fractal dimension of these ROIs was estimated by power spectrum analysis. The cases were not subtle.
The fractal dimension provided statistically significant discrimination between normal parenchyma and nodular interstitial lung disease. The area under the receiver operating characteristic curve was 0.90 (+/- 0.02). One operating point provides sensitivity of 88% with a specificity of 80%.
The fractal dimension can provide a measure of lung parenchymal texture and shows promise as an element of computer-aided diagnosis, characterization, and follow-up of interstitial lung disease.
American Journal of Roentgenology 12/1996; 167(5):1185-7. DOI:10.2214/ajr.167.5.8911177 · 2.73 Impact Factor