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A novel fuzzy pixel intensity correlation based segmentation algorithm for early detection of Alzheimer’s disease

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Abstract

Alzheimer’s disease (AD) is considered to be one of the most fatal neurological disorders and is identified as significant tissue loss in the hippocampus region of human brain. This paper presents a fuzzy based novel segmentation algorithm for brain MRI images. A structuring element for opening of gray scale converted test MRI scans has been proposed in this regard. It effectively enhances the contrast of lateral ventricle region of brain which contains crucial information for mild cognitive impairment (MCI). Proposed rule-base of higher order fuzzy system dynamically chooses edge pixels and accordingly predicts the next probable edge pixel. Proposed fuzzy inference system is inspired by fuzzy connectedness algorithm and converts probable edge pixels into edge pixels depending on the intensity correlation between ordered pixels in support of rule-base and assembles it into an edge contour. Our proposition is finally tested over several ADNI brain images of different subject and orientation. Experimental results identify a promising improvement in detection of object boundaries and enhance contrast both qualitatively and quantitatively.

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Acknowledgements

Authors would like to thank Visvesvaraya PhD Scheme for Electronics & IT under Ministry of Electronics and Information Technology, Government of India for extending necessary financial support to carry out this research work. We thank radiologist Dr. Tapan K. Biswas for providing public large scale brain MRI data set as a part of this research work.

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  1. Department of Instrumentation & Electronics Engineering, Jadavpur University, Kolkata, West Bengal, India

    Sukanta Ghosh, Abhijit Chandra & Rajani K. Mudi

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  1. Sukanta Ghosh
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  2. Abhijit Chandra
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  3. Rajani K. Mudi
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Correspondence to Sukanta Ghosh.

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Ghosh, S., Chandra, A. & Mudi, R.K. A novel fuzzy pixel intensity correlation based segmentation algorithm for early detection of Alzheimer’s disease. Multimed Tools Appl 78, 12465–12489 (2019). https://doi.org/10.1007/s11042-018-6773-z

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