Advances in Intelligent Analysis of Medical Data and by Abdel-Badeeh M. Salem (auth.), Roumen Kountchev, Barna PDF

By Abdel-Badeeh M. Salem (auth.), Roumen Kountchev, Barna Iantovics (eds.)

ISBN-10: 3319000284

ISBN-13: 9783319000282

ISBN-10: 3319000292

ISBN-13: 9783319000299

This quantity is as a result of the fruitful and vibrant discussions in the course of the MedDecSup'2012 overseas Workshop bringing jointly a correct physique of information, and new advancements within the more and more vital box of scientific informatics. This rigorously edited e-book provides new principles geared toward the improvement of clever processing of varied sorts of clinical info and the perfection of the modern computers for clinical selection aid. The e-book offers advances of the clinical info structures for clever archiving, processing, research and search-by-content so one can enhance the standard of the clinical companies for each sufferer and of the worldwide healthcare process. The publication combines in a synergistic manner theoretical advancements with the practicability of the ways constructed and offers the final advancements and achievements in clinical informatics to a vast variety of readers: engineers, mathematicians, physicians, and PhD scholars.

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Extra resources for Advances in Intelligent Analysis of Medical Data and Decision Support Systems

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Active contours (shakes) are widely used in the tasks connecting with image segmentation and boundary extraction. For boundary detection on images, curves with minimal energy are applied. It is supposed that a Visual System of Sign Alphabet Learning for Poorly-Hearing Children 33 target boundary is a smooth closed line [21]. Such assumptions well fit with hand segmentation process. The initial contour is accepted as a simple figure (for example, a circle). Then the contour is distorted, and contour points tend to object boundary by minimization of contour energy.

Keywords: Decorrelation of medical CT image sequences, Hierarchical Adaptive Karhunen-Loeve Transform (HAKLT), Group of Medical Images. 1 Introduction In the last years, large number of new technologies and systems for digital processing of medical images had been created [1, 2, 3], such as: the standard Digital Imaging and Communication in Medicine (DICOM) [4] used for storage, transfer and visualization of images, obtained from computer tomographic systems, MRI and ultrasound scanners; Picture Archiving and Communication System (PACS); systems for digital radiography, teleradiology, etc.

That’s why a method with fewer resources was chosen, and it is the maximum likelihood method. By using blob-detectors [22] we may determine fingers position and orientation, and the number of separated fingers. Also we can calculate the hand skewing position, inner closed contours and the shifting motion during gesture demonstration applying well known image processing methods. For these six parameters we built a set of decision rules based on Table 1 as a production knowledge model. Mostly the application of such decision rules is enough for sign identification.

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Advances in Intelligent Analysis of Medical Data and Decision Support Systems by Abdel-Badeeh M. Salem (auth.), Roumen Kountchev, Barna Iantovics (eds.)


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