Titlebook: Information Technologies in Biomedicine; Ewa Pietka,Jacek Kawa Conference proceedings 2008 Springer-Verlag Berlin Heidelberg 2008 Computer

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Fractal Magnification of Medical Imagesments. There are a number of magnification methods, however most of them cause image distortions. The paper presents a fractal magnification method, which allows minimizing these negative effects besides the blocks effect.

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繁榮地區(qū)

CHASE

調(diào)色板

Shape and Texture Feature Extraction for Retrieval Mammogram in Databasesroperties allowing to retrieve these images and adding . (CBIR) capabilities to PACS makes it more powerful to assist diagnosis. Such systems provide features which combine color, shape and spatial features to query an image. In response to a user’s query, the system returns images that are similar

不再流行

Mathematical Morphology (MM) Features for Classification of Cancerous Masses in Mammogramsignancy, which denotes a special pathology of the tissue, is closely related to the existence of quasi-linear structures (spicules) emanating from the central mass. Hence, the tasks of malignancy and spicularity assessment are very often treated jointly. We propose a novel set of features enriching

臨時抱佛腳

Stroke Display Extensions: Three Forms of Visualizationke diagnosis based on CT examinations was analyzed. The multiscale extraction of the subtlest signs of hypodensity which were often undetected in standard CT scan review was presented. Proposed method was as follows: evidence-based description of ischemic changes, the analysis of hypodensity signs a

審問,審訊

Automated Fuzzy-Connectedness-Based Segmentation in Extraction of Multiple Sclerosis Lesionsancement to the existing ‘fast’ segmentation method. First a fuzzy connectedness relation is introduced, next a short overview of the ‘fast’ segmentation method is presented. Finally, a novel, automated segmentation approach is described. The combined method is applied to segmentation of clinical Ma

PIZZA

achlorhydria

Automatic Registration of?MRI?Brain-group and the B-group is equal six months. The automatic registration of the A- and B-group of MRI brain is based on the entropy and energy measures of fuzziness. First, two sequences (A- and B-group) are converted to a fuzzy representation. Then, the entropy and energy measures are employed in the