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Cardiac Image Segmentation using Improved Genetic Algorithm


  • Computer Science Department, IMS Engineering College, Ghaziabad – 201009, Uttar Pradesh, India
  • Computer Science and Engineering, Department MNNIT Allahabad UP, Teliarganj, Allahabad – 211004, Uttar Pradesh, India
  • IT Department, JSSATE, Noida – 201301, Uttar Pradesh, India


Objectives: Cardiac Image Segmentation field have a lot of difficulties when we take the big changes in sequences of images that are of different types. In image sequences, Segmentation of objects which are not fixed is more challenging. To handle such situations, use of Improved Genetic Algorithm for Image Segmentation of Cardiac images is presented.Methods: We propose an algorithm based on Improved Genetic Algorithm, for segmentation of medical image sequences, which uses K-mean clustering. For clustering in the feature space, we used feature vector of two- dimension. Findings: In our paper, for Cardiac Image Segmentation, we are presenting a state of art review of various methods and techniques. Various sequences of Cardiac image have been Registered, and then for segmentation process, single image is used. Novelty/Improvement: Satisfactory results have been given by the experiments done on Cardiac images.


Cardiac Image Segmentation, Clustering, Genetic Algorithm, Image Segmentation.

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  • Estrada FJ, Jepson AD. Benchmarking image segmentation algorithms. International Journal of Computer Vision. 2009 May 28; 85:167–81. Crossref
  • Farmer ME, Shugars DS. Application of genetic algorithms for wrapper-based im- age segmentation and classification. IEEE Congress on Evolutionary Computation; 2006 Jul. p. 1300–7.
  • Halder A, Pathak N. An evolutionary dynamic clustering based color image segmentation. International Journal of Image Processing.2011; 4(6).
  • Khashandarag AS, Mirnia M, Sakhavati A. A new method for medical image clustering using genetic algorithm. International Journal of Computer Science. 2013 Jan; 10(1).
  • Pluempitiwiriyawej C, Moura JMF, Wu Y-JL, Ho C. STACS: New active contour scheme for cardiac MR image segmentation. IEEE Transactions on Medical Imaging. 2005 May; 24(5).
  • Galic S, Loncaric S. Cardiac image segmentation using spatio-temporal clustering. Proceeding SPIE 4322, Medical Imaging 2001: Image Processing;2001 Jul 3. Crossref
  • Kirisli HA, Schaap M, Klein S, Neefjes LA, Weustink AC, Van Walsum T, Niessen WJ. Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach. Proceedings SPIE 7623, Medical Imaging 2010: Image Processing.2010 Mar 12; 7623.
  • Mahapatra D. Cardiac image segmentation from cine cardiac MRI using graph cuts and shape priors. Society for Imaging Informatics in Medicine. 2013. Crossref
  • MontagnatJ, Delingette H. 4D deformable models with temporal constraints: application to 4D cardiac image segmentation. Medical Image Analysis. 2005; 9(2005):87– 100. Crossref
  • Petitjean C, Dacher J-N. A review of segmentation methods in short axis cardiac MR images. Medical Image Analysis. 2011. Crossref
  • Metaxas D, Chen T, Huang X, Axel L. Cardiac segmentation from MRI-tagged and CT images. 8th WSEAS International Conference on Computers, Special Session on Imaging and Image Processing of Dynamic Processes in biology and medicine; 2004.
  • Khashandarag AS, Mirnia M, Sakhavati A. A new method for medical image clustering using genetic algorithm. International Journal of Computer Science. 2013 Jan;10(1).
  • Moghaddam MJ, Soltanian-Zadeh H. Medical image segmentation using artificial neural networks. Image Analysis Lab., Radiology Department, Henry Ford Health System, Detroit, Iran Michigan, USA; 2011 Apr.
  • MaulikU. Medical image segmentation using genetic algorithms. IEEE Transactions on Information Technology in Biomedicine.2009 Mar; 13(2).
  • Soesanti I, Susanto A, Widodo TS, Tjokronagoro M. Optimized fuzzy logic application for MRI brain images segmentation. 2011 Oct; 3(5).
  • Sukassini MP, Velmurugan T. A survey on the analysis of segmentation techniques in mammogram images. Indian Journal of Science and Technology. 2015 Sep; 8(22). Crossref
  • Naveen A, Velmurugan T. Identification of calcification in MRI brain images by k-means algorithm. Indian Journal of Science and Technology. 2015 Nov; 8(29). Crossref


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