By Tomasz Przybyła, Janusz Jeżewski, Dawid Roj (auth.), Ewa Piȩtka, Jacek Kawa (eds.)
ISBN-10: 3642131042
ISBN-13: 9783642131042
Information expertise in Biomedicine is an interdisciplinary examine region, that bridges the distance among methodological achievements in engineering and scientific requisites in scientific analysis and treatment. during this booklet, participants of the tutorial society of technical and clinical history current their examine effects and scientific implementation in an effort to fulfill the practical necessities of licensed physicians for the advantage of the sufferers. a longer quarter is roofed via the articles. It comprises biomedical indications, scientific photograph processing, computer-aided analysis and surgical procedure, biometrics, healthcare and telemedicine, biomechanics, biomaterials, bioinformatics. part on bronchoscopy offers the foundation in addition to new learn reviews played during this box. Papers current numerous theoretical methods and new methodologies according to fuzzy units, mathematical information, mathematical morphology, fractals, wavelets, syntactic tools, synthetic neural networks, graphs and plenty of others.
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Additional resources for Information Technologies in Biomedicine: Volume 2
Example text
International Agrophysics 13, 133–140 (1999) 16. : Classification of wheat grains by digital image analysis using statistical filters. Euphytica 100, 171–178 (1998) Dynamics of the Clusterization Process in an Adaptative Method of Image Segmentation Aleksander Lamz˙ a and Zygmunt Wróbel Abstract. In this paper a new method of automatic image segmentation is presented. In proposed approach a region merging method is applied, where pixels or pixel groups are merged to bigger areas. In the proposed method of the region merging seed settings, segmentation parameter definition, and number of outcoming areas determining can be ommited.
1 Introduction Clustering is a major technique for data mining, used mostly as an unsupervised learning method. pl ˙ Piotr A. pl E. Pi˛etka and J. ): Information Technologies in Biomedicine, AISC 69, pp. 15–24. com 16 M. Charytanowicz et al. grouped together, while dissimilar objects belong to different clusters [9, 12]. As a result, a new set of categories of interest, characterizing the population, is discovered. The clustering methods are generally divided into six groups: hierarchical, partitioning, density-based, grid-based, and soft-computing methods.
Initialize v(0) . Set the iteration index k = 1. (k) 2. Calculate the hyperparameter αi for i = 1, 2, . . , M and β (k) (k) αi = N ∑Nj=1 β (k) = fi ( j) − v(k−1) ( j) N ∑Nj=1 2 v(k−1) ( j) 3. Update the averaged signal v(k) for kth iteration. 2 , (3) (4) 36 A. Momot (k) v(k) ( j) = ∑M i=1 αi f i ( j) (5) (k) β (k) + ∑M i=1 αi 4. If ||v(k) − v(k−1) || > ε then k ← k + 1 and go to 2, else stop. 2 Adaptive Linear Spatial Filtering Described above algorithm could be modified for application to adaptive linear spatial filtering.
Information Technologies in Biomedicine: Volume 2 by Tomasz Przybyła, Janusz Jeżewski, Dawid Roj (auth.), Ewa Piȩtka, Jacek Kawa (eds.)
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