Fractal Feature Analysis and Classification in Medical Imaging - Chen, C.-C.; Daponte, J.S.; Fox, M.D. (IEEE Transactions on Medical Imaging, 1989)
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
Keywords: Acoustic Imaging; Fractal Feature Analysis; Fractional Brownian Motion; Image Processing--Enhancement; Imaging Techniques; Mathematical Techniques--Vectors; Medical Imaging Classification; Probability--Random Processes; Ultrasonic Liver Images Classification; Ultrasonics--Medical Applications; dept.csc

Statistical classification of ultrasonic image texture - DaPonte, John S.; Gelber, Joel; Fox, Martin D. (Publ by IEEE, 1989)
Qualitative analysis is important because it is not subjective and does not have the potential for variation from one observer to another. A description is given of how statistical hypothesis testing can be used to select the quantitative descriptors best capable of distinguishing between normal and abnormal liver texture. Information is also presented on how both parametric and nonparametric discriminant analysis can be applied to determine how well the quantitative analysis compares with the qualitative diagnosis supplied for each case studied.
Keywords: Acoustic Imaging--Digital Techniques; Biomedical Engineering; Liver Disease Diagnosis; Quantitative Descriptor Selection; Statistical Classification Techniques; Statistical Hypothesis Testing; Statistical Methods--Statistical Tests; Ultrasonic Devices--Medical Applications; Ultrasonic Image Texture Classification; dept.csc; ⛔ No DOI found