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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Multimodal Medical Image Fusion Based on Sugeno's Intuitionistic Fuzzy Sets

  • Tirupal, Talari (Department of Electronics & Communication Engineering, Jawaharlal Nehru Technological University) ;
  • Mohan, Bhuma Chandra (Department of Electronics & Communication Engineering, Bapatla Engineering College) ;
  • Kumar, Samayamantula Srinivas (Department of Electronics & Communication Engineering, Jawaharlal Nehru Technological University)
  • Received : 2016.08.16
  • Accepted : 2017.02.09
  • Published : 2017.04.01
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Abstract

Multimodal medical image fusion is the process of retrieving valuable information from medical images. The primary goal of medical image fusion is to combine several images obtained from various sources into a distinct image suitable for improved diagnosis. Complexity in medical images is higher, and many soft computing methods are applied by researchers to process them. Intuitionistic fuzzy sets are more appropriate for medical images because the images have many uncertainties. In this paper, a new method, based on Sugeno's intuitionistic fuzzy set (SIFS), is proposed. First, medical images are converted into Sugeno's intuitionistic fuzzy image (SIFI). An exponential intuitionistic fuzzy entropy calculates the optimum values of membership, non-membership, and hesitation degree functions. Then, the two SIFIs are disintegrated into image blocks for calculating the count of blackness and whiteness of the blocks. Finally, the fused image is rebuilt from the recombination of SIFI image blocks. The efficiency of the use of SIFS in multimodal medical image fusion is demonstrated on several pairs of images and the results are compared with existing studies in recent literature.

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References

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