Total lung capacity (TLC) is a very important parameter in the study of pulmonary function. In the pulmonary function laboratory, it is normally obtained using plethysmography or helium dilution techniques. Several authors have developed methods of calculating the TLC using postero-anterior (PA) and lateral chest radiographs. These methods have not been often used in clinical practice. In the present work, we have developed and automated computer-based method for the calculation of TLC, by determining the pulmonary contours from digital PA and lateral radiographs of the thorax. The automatic tracing of the pulmonary borders is carried out using: (1) a group of reference lines is determined in each radiograph; (2) a family of rectangular regions of interest (ROIs) defined, which include the pulmonary borders, and in each of them the pulmonary border is identified using edge enhancement and thresholding techniques; (3) removing outlaying points from the preliminary boundary set; and (4) the pulmonary border is corrected and completed by means of interpolation, extrapolation, and arc fitting. The TLC is calculated using a computerized form of the radiographic ellipses method of Barnhard. The pulmonary borders were automatically traced in a total of 65 normal radiographs (65 PA and 65 lateral views of the same patients). Three radiologists carried out a subjective evaluation of the automatic tracing of the pulmonary borders, with a finding of no error or only one minor error in 67.7% of the PA evaluations, and in 75.9% of the laterals. Comparing the automatically traced borders with borders traced manually by an expert radiologists, we obtained a precision of 0.990 +/- 0.001 for the PA view, and 0.985 +/- 0.002 for the lateral. The values of TLC obtained by the automatic calculation described here showed a high correlation (r = 0.98) with those obtained by applying the manual Barnhard method.
The authors present a new algorithm to enhance the edges and contrast of chest and breast radiographs while minimally amplifying image noise. The algorithm consists of a linear combination of an original image and two smoothed images obtained from it by using different masks and parameters, followed by the application of nonlinear contrast stretching. The result is an image which retains the high median frequency local variations (edge and contrast-enhancing).
We have developed a method for the quantification of breast texture by using different algorithms to classify mammograms into the four patterns described by Wolfe (N1, P1, P2 and Dy). The computerized scheme employs craniocaudal views of conventional screen-film mammograms, which are digitized by a laser scanner. We used discriminant analysis to select among different feature-extraction techniques, including Fourier transform, local-contrast analysis, and grey-level distribution and quantification. The method has been evaluated on 117 clinical mammograms previously classified by five radiologists as to mammographic breast parenchymal patterns (MBPPS). The results show differences in agreement among radiologists and computer classification, depending on the Wolfe pattern: excellent for Dy (kappa = 0.77), good for P2 (kappa = 0.52) and N1 (kappa = 0.52) and poor for P1 (kappa = 0.22). Our quantitative texture measure as calculated from digital mammograms may be valuable to radiologists in their assessment of MBPP and therefore useful in establishing an index of risk for developing breast carcinoma.
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