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The use of gradient operators for image enhancement has been widely reported in the literature, but they have not been used routinely in the medical arena, particularly in the most common radiographic plain film procedure, chest radiographs. Gradient operators such as Sobel and Roberts operators, not only enhance image edges but also tend to enhance noise. Overall, the Sobel operator was found to be superior to the Roberts operator in edge enhancement. A theoretical explanation for the superior performance of the Sobel operator was developed based on the concept of analyzing the x and y Sobel masks as linear Alters. By applying pill box, Gaussian, or median filtering prior to applying a gradient operator, noise was reduced, but the pill box and Gaussian filters were much more computationally efficient than the median filter with approximately equal effectiveness in noise reduction. © 1988 IEEE
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Following Mandelbrot's fractal theory, it was found that the fractal dimension could be obtained in medical images by the concept of fractional Brownian motion. An estimation concept for determination of the fractal dimension based upon the concept of fractional Brownian motion was discussed. Two applications were found: 1) classification; 2) edge enhancement and detection. For the purpose of classification, a normalized fractional Brownian motion feature vector was defined from this estimation concept. It represented the normalized average absolute intensity difference of pixel pairs on a surface at different scales. The feature vector used relatively few data items to represent the statistical characteristics of the medical image surface and was invariant to linear intensity transformation. Finally, by calculating normalized fractional Brownian motion feature vectors in five different ultrasonic image surfaces, it was found that the classification of normal and abnormal ultrasonic liver images could be obtained from the differences between their feature vectors. For edge enhancement and detection application, a transformed image was obtained by calculating the fractal dimension of each pixel over the whole medical image. The fractal dimension value of each pixel was obtained by calculating the fractal dimension of a 7 x 7 pixel block centered on this pixel. Preliminary results using projection radiographs suggest that the fractal based image transformation appears to hold promise as an edge enhancement and preprocessing algorithm that does not increase noise in the way that gradient operators do. © 1989 IEEE
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Spectral analysis of Doppler ultrasound has been known to yield valuable information to assess the state of circulation in the peripheral blood vessels. In the past, the raw Doppler data have been directly input into a dedicated spectrum analyzer or, more recently, transformed on a microcomputer with the fast Fourier technique. The fast Hartley technique is used to transform these data. The Hartley transform has the advantages of being a purely real-numbered transform, and therefore for real Doppler data, is not only more conceptually straightforward, but also requires less computer memory, is simpler to calculate, and is better suited to large-scale integration implementation. © 1988 IEEE
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