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An Accelerated Approach for Generalized Entropy Based MRI Image Segmentation

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Advances in Computing and Data Sciences (ICACDS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 721))

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Abstract

Image segmentation, a major research class is frequently governed by the way of two foremost parameters, associated with a specified segmentation procedure: threshold decision and seed-point determination. Various methods such as the histogram based processes, entropy headquartered process, business measure approaches and so on. These are well recognized for threshold choice within the image segmentation issues. In this article, threshold determination is done on the basis of extraordinary entropy measures on each of grey scale and color images. Comparative analysis of the Shannon and non-Shannon entropies (Renyi, Havrda-Charvat, Kapur and Vajda) is done to receive an appropriate threshold worth for the perfect image segmentation. It’s concluded via the simulation experiments performed on MRI images, that the role of the smallest minima obtained within the entropy versus grey-degree plot is different for every entropy measure. The threshold values received from these plots is accordingly elegant on the specific definition of the entropy chosen, which in flip influences segmentation outcome. It’s further observed that the segmentation results acquired, making use of Havrda-Charvat entropy measure is healthier than other entropy measures.

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Authors and Affiliations

  1. Department of Computer Science, Government Engineering College Bikaner, Bikaner, India

    Anushikha Jain, Maninder Singh Nehra & Manoj Kuri

Authors
  1. Anushikha Jain
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  2. Maninder Singh Nehra
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  3. Manoj Kuri
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Corresponding author

Correspondence to Anushikha Jain .

Editor information

Editors and Affiliations

  1. Krishna Engineering College, Ghaziabad, Uttar Pradesh, India

    Mayank Singh

  2. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Raghogarh, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Indira Gandhi Delhi Technical University, New Delhi, India

    Arun Sharma

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada

    Tuncer Ören

  6. Department of Computer and Information Science, University of Michigan-Dearborn, Dearborn, Michigan, USA

    William Grosky

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Jain, A., Nehra, M.S., Kuri, M. (2017). An Accelerated Approach for Generalized Entropy Based MRI Image Segmentation. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds) Advances in Computing and Data Sciences. ICACDS 2016. Communications in Computer and Information Science, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-10-5427-3_37

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  • DOI: https://doi.org/10.1007/978-981-10-5427-3_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5426-6

  • Online ISBN: 978-981-10-5427-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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