Open Access Open Access  Restricted Access Subscription Access

Classification of Human Organ Using Image Processing


Affiliations
1 Department of Computer Sciences, Christ University, Bengaluru, India
 

The collection of large database of digital image has been used for efficient and advanced way for classifying and intelligent retrieval of medical imaging. This research work is to classify human organs based on MRI images. The various MRI images of organ have been considered as the data set. The main objective of this research work is to automate the medical imaging system. Digital images retrieved based on its shape by Canny Edge Detection and is clustered together in one class using K-Means Algorithm. 2564 data sets related to brain and heart is considered for this research work. The system was trained to classify the image which results in faster execution in medical field, also helped in obtain noiseless and efficient data.

Keywords

Medical Imaging, Digital Images, Organs, Canny Edge Detection, K-Means Algorithm.
User
Notifications
Font Size

  • MEDICAL IMAGE PROCESSING K.M.M. Rao, V.D.P. Rao
  • Canny, J., “A computational approach to edge detection”, IEEE Trans on Pattern Analysis and Machine Intelligence, 8:679-698, 1986.
  • CBMIR: Shape-Based Image Retrieval Using Canny Edge Detection And K-mean Clustering Algorithm For Medical Images.B.Ramamurthy1,K.R.Chandran2
  • Diagnose Breast Cancer Through Mammograms, Using Image Processing Techniques and Optimization Techniques. Dr. M. Karnan1, K. Rajiv Gandhi2.
  • An Efficient k-Means Clustering Algorithm: Analysis and Implementation. Tapas Kanungo, Senior Member, IEEE, David M. Mount, Member, IEEE, Nathan S. Netanyahu, Member, IEEE, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu, Senior Member, IEEE. IEEE Transactions On Pattern Analysis And Machine Intelligence, 24(7), JULY 2002.
  • http://www.ijcaonline.org/icvci/number11/icvci1458.pdf
  • http://www.ace.tuiasi.ro/users/103/f2_2011_7_(83-98)_Smochina.pdf
  • A Review of Medical Image Classification Techniques. Smitha P. Selection grade lecturer Dept. of CSE, CE Karunagapally Shaji L. Lecture in comp.Appln Dept.of IT, CE, Karunagapally Dr. Mini M. G. Asst. Prof. & HOD Dept. of ECE, MEC Karunagapally International Conference on VLSI, Communication & Instrumentation (ICVCI) 2011 Proceedings published by International Journal of Computer Applications(IJCA)
  • Thies C, Guld MO, Fischer B, Lehmann TM, “Content- based queries on the CasImage database with in the IRMA framework”, Lec Notes in Comp Sci; 3491:781-92. 2005.
  • Thoma GR, Long LR, Antani SK, Biomedical Imaging research and development: knowledge from images in the medical enterprise. Technical Report Lister Hill National Ctr for Biomedical Communications, US National Library of Medicine, NIH 2006;LHNCBCTR-2006-002.

Abstract Views: 241

PDF Views: 1




  • Classification of Human Organ Using Image Processing

Abstract Views: 241  |  PDF Views: 1

Authors

Sindhu
Department of Computer Sciences, Christ University, Bengaluru, India
V. Vaidhehi
Department of Computer Sciences, Christ University, Bengaluru, India

Abstract


The collection of large database of digital image has been used for efficient and advanced way for classifying and intelligent retrieval of medical imaging. This research work is to classify human organs based on MRI images. The various MRI images of organ have been considered as the data set. The main objective of this research work is to automate the medical imaging system. Digital images retrieved based on its shape by Canny Edge Detection and is clustered together in one class using K-Means Algorithm. 2564 data sets related to brain and heart is considered for this research work. The system was trained to classify the image which results in faster execution in medical field, also helped in obtain noiseless and efficient data.

Keywords


Medical Imaging, Digital Images, Organs, Canny Edge Detection, K-Means Algorithm.

References





DOI: https://doi.org/10.13005/ojcst%2F10.02.11