GSA based classification of lung nodules in CT images

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Medical Image processing techniques are widely used in several medical fields for earlier detection of diseases. CAD (Computer Aided Diagnostic) system yields a great deal of information to analyze and evaluate the accuracy of differential structures at short time period. Image quality with detailed edges is the core factors of this research, where low pre-processing technique is used for filtering. As a advanced method compared with standard median filter, the adaptive median filter performs spatial processing to preserve detail and smooth non-impulsive noise. Image segmentation is done using edge detection techniques in lung CT images for acquiring information, which is a prior step for feature extraction and selection. For detecting the differential structures a new edge detection algorithm is proposed, that is Gravitational search algorithm inspired by law of universal gravity. In this approach the edges are detected by the local variation of intensity values and the movement of agents is computed using Gravitational Search Algorithm (GSA).It gives fast and accurate framework for feature extraction and feature selection. For Feature Extraction Gray Level Co-occurrence Matrix

Autorentext

Basic Academic Credentials QualificationBoard/UniversityYearPercentageM.E(Computer Science & Engineering) Sona college of technology2016-1884%B.E(Computer Science & Engineering)A.V.S.Engineering college2012-1675%High SchoolSt.mary s girls higher secondary school201075%

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09786202311441
    • Sprache Englisch
    • Genre Anwendungs-Software
    • Größe H220mm x B150mm x T4mm
    • Jahr 2018
    • EAN 9786202311441
    • Format Kartonierter Einband
    • ISBN 6202311444
    • Veröffentlichung 02.05.2018
    • Titel GSA based classification of lung nodules in CT images
    • Autor Maria Jenifer L
    • Untertitel Sona college of technology, India
    • Gewicht 96g
    • Herausgeber Scholars' Press
    • Anzahl Seiten 52

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