Showing papers by "Stephen R. Aylward published in 2000"

Image processing algorithms for digital mammography: a pictorial essay.

Abstract: Digital mammography systems allow manipulation of fine differences in image contrast by means of image processing algorithms. Different display algorithms have advantages and disadvantages for the specific tasks required in breast imaging-diagnosis and screening. Manual intensity windowing can produce digital mammograms very similar to standard screen-film mammograms but is limited by its operator dependence. Histogram-based intensity windowing improves the conspicuity of the lesion edge, but there is loss of detail outside the dense parts of the image. Mixture-model intensity windowing enhances the visibility of lesion borders against the fatty background, but the mixed parenchymal densities abutting the lesion may be lost. Contrast-limited adaptive histogram equalization can also provide subtle edge information but might degrade performance in the screening setting by enhancing the visibility of nuisance information. Unsharp masking enhances the sharpness of the borders of mass lesions, but this algorithm may make even an indistinct mass appear more circumscribed. Peripheral equalization displays lesion details well and preserves the peripheral information in the surrounding breast, but there may be flattening of image contrast in the nonperipheral portions of the image. Trex processing allows visualization of both lesion detail and breast edge information but reduces image contrast.

Radiologists’ Preferences for Digital Mammographic Display

Abstract: PURPOSE: To determine the preferences of radiologists among eight different image processing algorithms applied to digital mammograms obtained for screening and diagnostic imaging tasks. MATERIALS AND METHODS: Twenty-eight images representing histologically proved masses or calcifications were obtained by using three clinically available digital mammographic units. Images were processed and printed on film by using manual intensity windowing, histogram-based intensity windowing, mixture model intensity windowing, peripheral equalization, multiscale image contrast amplification (MUSICA), contrast-limited adaptive histogram equalization, Trex processing, and unsharp masking. Twelve radiologists compared the processed digital images with screen-film mammograms obtained in the same patient for breast cancer screening and breast lesion diagnosis. RESULTS: For the screening task, screen-film mammograms were preferred to all digital presentations, but the acceptability of images processed with Trex and MUSICA al...

The role of neural networks in improving the accuracy of MR spectroscopy for the diagnosis of head and neck squamous cell carcinoma

Abstract: BACKGROUND AND PURPOSE: MR Spectroscopy (MRS) has the unique ability to analyze tissue at the molecular level noninvasively. The purpose of this study was to determine if peak heights revealed by proton MRS ( 1 H-MRS) signals showed that neural networks (NN) provided better accuracy than linear discriminant analysis (LDA) in differentiating head and neck squamous cell carcinoma (SCCA) from muscle METHODS: In vitro 11-T 1 H-MR spectra were obtained on SCCA tissue samples (n = 16) and muscle (n = 12). The peak heights at seven metabolite resonances were measured: olefinic acids at 5.3 ppm, inositol at 3.5 ppm, taurine at 3.4 ppm, choline (Cho) at 3.2 ppm, creatine (Cr) at 3.0 ppm, sialic acid at 2.2 ppm, and methyl at 0.9 ppm. Using leave-one-out experimental design and receiver operating characteristic curve analysis, the ability of NN and LDA classifiers to distinguish SCCA from muscle were compared (given equal weighting of false-negative and false-positive errors). These classifiers were also compared with an existing method that forms a diagnosis by using LDA of the Cho/Cr peak area ratio. RESULTS: NN classifiers, which were identified using height data, achieved better sensitivity and specificity rates in distinguishing SCAA from muscle than did LDA using height or area data. Sensitivity/specificity for the NN analysis of the seven metabolite peak heights were 87.5% and 83.3%, respectively, for a one-hidden-node network and 81.2% and 91.7%, respectively, for a two-hidden-node network. Additional nodes did not improve accuracy. The sensitivity and specificity were 81.2% and 50%, respectively, for LDA of the seven peak heights, and 68% and 83%, respectively, for LDA of the Cho/Cr peak area ratio. CONCLUSION: NN classifiers with peak height data were superior to LDA of the peak heights and LDA of the Cho/Cr peak area ratio for differentiating SCCA from normal muscle. These results show neural network analysis can improve the diagnostic accuracy of 1 H-MRS in differentiating muscle from malignant tissue. Further studies are necessary to confirm our initial findings.