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Two different methods are proposed for feature extraction and visualization. The first method is based on automatical searching features for image class on the training set. Special multidimensional co-occurrence matrices are used as the detailed description of the image structure. The features describe quantitative relations between some elemental structures. The features found on the training set are used for recognition, detection and visualization of the key structures and regions. The second method is based on image segmentation, design of topological description for regions of interest (ROIs) and calculation of spatial and textural parameters for segments being a part of the ROI. The training set of 56 images was used as source for finding threshold values of parameters and testing. Application of the methods is demonstrated as an example of diagnosing the head brain norm/pathology (18 CT images), large intestine diseases from 2D contour shape (178 x-ray images), and tumor recognizing from ultrasonic liver images. Software has been developed for IBM AT compatible computers.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. |
