Chinese Journal of Tissue Engineering Research ›› 2012, Vol. 16 ›› Issue (17): 3160-3163.doi: 10.3969/j.issn.1673-8225.2012.17.028

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Research and realization of Tamura texture feature extraction method based on image segmentation 

Lü Xiao-qi, Guo Jin-ge, Zhao Yu-hong, Ren Xiao-ying   

  1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou  014010, Inner Mongolia Autonomous Region, China
  • Received:2011-07-11 Revised:2011-12-19 Online:2012-04-22 Published:2012-04-22
  • Contact: Guo Jin-ge, Master, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, Inner Mongolia Autonomous Region, China yewen37@163.com
  • About author:Lü Xiao-qi☆, Studying for doctorate, Professor, Doctoral supervisor, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, Inner Mongolia Autonomous Region, China lxiaoqi@imust.cn
  • Supported by:

    National Natural Science Foundation of China, No.61179019*; Natural Science Foundation of Inner Mongolia Autonomous Region, No. 2010Zd26*; Innovation Fund of Inner Mongolia University of Science and Technology, No. 2010NC037*

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Abstract:

BACKGROUND: Insight segmentation and registration toolkit is the main provider of medical image processing, segmentation and registration algorithm, but, it lacks of visual function and flexible and practical user interface. Visualization toolkit provides a wealth of medical image processing and analysis tools, with the powerful graphics and visualization capabilities.
OBJECTIVE: To manage, archive and retrieve the diagnosed medical imaging resource by using the previously confirmed cases, diagnose experience and relevant medical history in order to reduce manual intervention and improve the image recall rate and precision rate.
METHODS: The images were preformed with smoothing, de-noising and segmentation pretreatment process on the insight segmentation and registration toolkit platform based on visual perception mechanism, and then texture feature was extracted by Tamura algorithm. Finally, the comparative analysis was completed through experiment data collection and calculation.
RESULTS AND CONCLUSION: The experiment has proved that the method based on the application of texture feature retrieval can facilitate the similarity measurement and improve the retrieval accuracy.
 

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