Andrzej Obuchowicz

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A computer system has been developed for evaluating the morphome-trical feature extraction. The features are derived directly from a digital scan of breast fine needle biopsy slides. First the background elimination by thresholding hue component is applied, then the actual segmentation is done with region growing technique. The quality of feature space is(More)
This paper describes three cytological image segmentation methods. The analysis includes the watershed algorithm, active contouring and a cellular automata GrowCut method. One can also find here a description of image pre-processing, Hough transform based pre-segmentation and an automatic nuclei localization mechanism used in our approach. Preliminary(More)
A variety of computational intelligence approaches to nuclei segmentation in the microscope images of fine needle biopsy material is presented in this chapter. The segmentation is one of the most important steps of the automatic medical diagnosis based on the analysis of the microscopic images, and is crucial to making a correct diagnostic decision. Due to(More)
Prompt and widely available diagnostics of breast cancer is crucial for the prognosis of patients. One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist. Computer-aided diagnosis can speed up the diagnostic process and allow for large-scale(More)
2 This paper presents an automatic computer system to breast cancer diagnosis. System was designed to distinguish benign from malignant tumors based on fine needle biopsy microscope images. Studies conducted focus on two different problems, the first concern the extraction of morphometric and colorimetric parameters of nuclei from cytological images and the(More)
Fibroadenoma is a benign tumor having some features similar to malignant one. The aim of this study was to examine the impact of fibroadenoma cases on the results of automatic breast cancer diagnostic system based on quantitative morphometric analysis of fine needle biopsy microscopic images. Database of 50 patients (500 images) of benign and malignant(More)
Multidimensional Symmetric α-Stable (SαS) mutations are applied to phenotypic evolutionary algorithms. Such mutations are characterized by non-spherical symmetry for α < 2 and the fact that the most probable distance of mutated points is not in a close neighborhood of the origin, but at a certain distance from it. It is the so-called surrounding effect(More)