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HSV Based Histogram Thresholding Technique for MRI Brain Tissue Segmentation

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Advances in Signal Processing and Intelligent Recognition Systems (SIRS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 968))

Abstract

Background: To bring it as a human interactive perceive color process, an automatic color model based segmentation of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) in Magnetic Resonance Brain images is proposed in this paper.

Methods: Preprocessing process is done for the MRI brain images using wavelet based bivariate shrinkage method and Contour based Brain Segmentation method (CBSM). Then segmentation of brain tissues using Hue Saturation Value (HSV) color model Based Histogram Thresholding Technique (HSVBHTT) was applied. Normal and Alzheimer’s disease (AD) brain images obtained from Internet Brain Segmentation Repository (IBSR) and Minimal Interval Resonance Imaging in Alzheimer’s Disease (MIRIAD) datasets.

Results and Conclusions: The results of proposed method was analyzed with similarity measures and quantitative measures like Jaccard (J), Dice (D), Sensitivity (S) and Specificity (SP) and compared with the manual segmented images which produced better results on segmenting WM, GM and CSF compared to other existing methods.

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Acknowledgement

This work was supported by Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India.

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Authors and Affiliations

  1. Department of Computer Science and Applications, The Gandhigram Rural Institute - (Deemed to be University), Gandhigram, Tamil Nadu, India

    T. Priya & P. Kalavathi

Authors
  1. T. Priya
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  2. P. Kalavathi
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Corresponding author

Correspondence to P. Kalavathi .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology and Management, Kerala, India

    Sabu M. Thampi

  2. Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA

    Oge Marques

  3. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Sri Krishnan

  4. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. University of Naples Federico II, Naples, Italy

    Domenico Ciuonzo

  6. Electrical Engineering Department, Indian Institute of Technology Patna, Patna, India

    Maheshkumar H. Kolekar

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Priya, T., Kalavathi, P. (2019). HSV Based Histogram Thresholding Technique for MRI Brain Tissue Segmentation. In: Thampi, S., Marques, O., Krishnan, S., Li, KC., Ciuonzo, D., Kolekar, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2018. Communications in Computer and Information Science, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-13-5758-9_27

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  • DOI: https://doi.org/10.1007/978-981-13-5758-9_27

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  • Online ISBN: 978-981-13-5758-9

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