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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)
This paper presents a concept and initial VHDL simulations of digital circuit which implements a network of synchronised oscillators. This circuit will be able to perform several image processing operations, like image segmentation, edge detection, morphological filtering, noise removal. Properties of digital network will be compared and discussed with(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)
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 presents MaZda software for quantitative image texture analysis. This software, primarily developed for classification of magnetic resonance images, can be applied for wide class of textured images including color ones and 3D data. It enables estimation of almost 300 texture features; includes procedures for their reduction and classification.(More)
The role of tissue characterization by intravascular ultrasound (IVUS) imaging of the aortic wall has not been well established. The artificial neural networks (ANNs) are a promising tool for image classification. The aim of the study was to assess the texture correlation between matching IVUS and histologic images of the aortic wall. The computer-based(More)
Computer-based analysis of textures in magnetic resonance images provides a higher sensitivity to textural changes that cannot be recognized by the naked human eye. Thus, there is a better potential for identifying pathophysiological processes at an earlier stage or of a different character than even a trained radiologist can find. In the present study, the(More)
OBJECT There is a clinical need to be able to assess graft loss of transplanted pancreatic islets (PI) non-invasively with clear-cut quantification of islet survival. We tracked transplanted PI in diabetic mice during the early post-transplant period by magnetic resonance imaging (MRI) and quantified the islet loss using automatic segmentation technique. (More)