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MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was(More)
Texture analysis methods quantify the spatial variations in gray level values within an image and thus can provide useful information on the structures observed. However, they are sensitive to acquisition conditions due to the use of different protocols and to intra- and interscanner variations in the case of MRI. The influence was studied of two protocols(More)
Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human expert is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation(More)
This paper describes an automatic method for classification and segmentation of different intracardiac masses in tumor echocardiograms. Identification of mass type is highly desirable, since to different treatment options for cardiac tumors (surgical resection) and thrombi (effective anticoagulant treatment) are possible. Correct diagnosis of the character(More)
We present an application-specific integrated circuit (ASIC) CMOS chip that implements a synchronized oscillator cellular neural network with a matrix size of 32 × 32 for object sensing and labeling in binary images. Networks of synchronized oscillators are a recently developed tool for image segmentation and analysis. Its parallel network operation is(More)
The concept of using ANN-like approximators for estimation of dynamic system parameters is considered. It is shown that the modular, classifier-approximator architecture offers new possibilities of real-time observation point selection for optimal ANN performance-in terms of minimum parameter variance.