By Hubert Wojtowicz, Wieslaw Wajs (auth.), Barna Iantovics, Roumen Kountchev (eds.)
This publication bargains a state-of-the-art assortment overlaying topics regarding Advanced clever Computational applied sciences and determination aid Systems which are utilized to fields like healthcare helping the people in fixing difficulties. The publication brings ahead a wealth of principles, algorithms and case experiences in topics like: clever predictive analysis; clever examining of clinical photographs; new layout for coding of unmarried and sequences of scientific pictures; clinical choice aid structures; analysis of Down’s syndrome; computational views for digital fetal tracking; effective compression of CT photos; adaptive interpolation and halftoning for clinical pictures; purposes of synthetic neural networks for real-life difficulties fixing; current and views for digital Healthcare list structures; adaptive methods for noise relief in sequences of CT pictures etc.
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Extra resources for Advanced Intelligent Computational Technologies and Decision Support Systems
LSb LSByte MSb …. LSb Mask for the used coefficients selection in the highest decomposition level—16 bits: MSByte MSb …. LSb LSByte MSb …. )—6 bits, 2 bits for each group: Initial level, Middle levels and Last level. Coding: 00—WHT; 01—DCT; 02—Plane; 03—Surface; • Original image size (vertical direction)—binary, 16 bits; • Original image size (horizontal direction) – binary, 16 bits; • Used color format (4:1:1, 4:2:2, 4:4:4, 4:2:0)—16 bits. Coding: 00—4:2:0; 01—4:4:4; 02—4:2:2; 04—4:1:1. 4 Sub-Headers for Transform Coefficients Quantization • Sub-header for the last level (brightness): 16 binary numbers, for coefficients (0–15): 8 bits each; New Format for Coding of Single and Sequences of Medical Images 27 • Sub-header for the last level (color): 16 binary numbers, for coefficients (0–15): 8 bits each.
Moreover, the user can select its own rule set. The problem of selecting suitable sets of rules for classification of MMPI profiles has been considered in our previous chapters (see [8–10, 12, 13]). 2 Specific Selection of Rules After general selection of rule sets, the user can determine more precisely which rules will be used in the classification process. Each rule R in the Copernicus system has the form: IF ai1 ðxÞ 2 ½xli1 ; xri1 AND . . AND aik ðxÞ 2 ½xlik ; xrik ; THEN dðxÞ ¼ dm ; ð1Þ where ai1 ; .
Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2005) An Adaptive Approach for Noise Reduction in Sequences of CT Images Veska Georgieva, Roumen Kountchev and Ivo Draganov Abstract CT presents images of cross-sectional slices of the body. The quality of CT images varies depending on penetrating X-rays in a different anatomically structures. Noise in CT is a multi-source problem and arises from the fundamentally statistical nature of photon production. This chapter presents an adaptive approach for noise reduction in sequences of CT images, based on the Wavelet Packet Decomposition and adaptive threshold of wavelet coefficients in the high frequency sub-bands of the shrinkage decomposition.
Advanced Intelligent Computational Technologies and Decision Support Systems by Hubert Wojtowicz, Wieslaw Wajs (auth.), Barna Iantovics, Roumen Kountchev (eds.)