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Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement.

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Title: Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement.
Authors: Subramani, Bharath1 bharath.psna@psnacet.edu.in, Veluchamy, Magudeeswaran1
Source: Journal of Medical Systems. Jun2020, Vol. 44 Issue 6, p1-10. 10p. 5 Color Photographs, 1 Diagram, 4 Charts.
Subjects: Mammograms, Knee radiography, Algorithms, Brain, Diagnostic imaging, Magnetic resonance imaging, Contrast media
Abstract: Contrast enhancement methods are used to reduce image noise and increase the contrast of structures of interest. In medical images where the distinction between normal and abnormal tissue is subtle, accurate interpretation may become difficult if noise levels are relatively high. To provide accurate interpretation and clearer image for the observer with reduced noise levels "a novel adaptive fuzzy gray level difference histogram equalization algorithm" is proposed. At first, gray level difference of an input image is calculated using the binary similar patterns. Then, the gray level differences are fuzzified in order to deal the uncertainties present in the input image. Following the fuzzification, fuzzy gray level difference clip limit is computed to control the insignificant contrast enhancement. Finally, a fuzzy clipped histogram is equalized to obtain the contrast-enhanced MR medical image. The proposed algorithm is analysed both visually and analytically to calculate its performance against the other existing algorithms. Visual and analytical results on various test images affirm that the proposed algorithm outperforms all other existing algorithms and provide a clear path to analyse the fine details and infected portions effectively. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Subramani%2C+Bharath%22">Subramani, Bharath</searchLink><relatesTo>1</relatesTo><i> bharath.psna@psnacet.edu.in</i><br /><searchLink fieldCode="AR" term="%22Veluchamy%2C+Magudeeswaran%22">Veluchamy, Magudeeswaran</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Medical+Systems%22">Journal of Medical Systems</searchLink>. Jun2020, Vol. 44 Issue 6, p1-10. 10p. 5 Color Photographs, 1 Diagram, 4 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Mammograms%22">Mammograms</searchLink><br /><searchLink fieldCode="DE" term="%22Knee+radiography%22">Knee radiography</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Brain%22">Brain</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Contrast+media%22">Contrast media</searchLink>
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  Data: Contrast enhancement methods are used to reduce image noise and increase the contrast of structures of interest. In medical images where the distinction between normal and abnormal tissue is subtle, accurate interpretation may become difficult if noise levels are relatively high. To provide accurate interpretation and clearer image for the observer with reduced noise levels "a novel adaptive fuzzy gray level difference histogram equalization algorithm" is proposed. At first, gray level difference of an input image is calculated using the binary similar patterns. Then, the gray level differences are fuzzified in order to deal the uncertainties present in the input image. Following the fuzzification, fuzzy gray level difference clip limit is computed to control the insignificant contrast enhancement. Finally, a fuzzy clipped histogram is equalized to obtain the contrast-enhanced MR medical image. The proposed algorithm is analysed both visually and analytically to calculate its performance against the other existing algorithms. Visual and analytical results on various test images affirm that the proposed algorithm outperforms all other existing algorithms and provide a clear path to analyse the fine details and infected portions effectively. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10916-020-01568-9
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      – Code: eng
        Text: English
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      – SubjectFull: Mammograms
        Type: general
      – SubjectFull: Knee radiography
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Brain
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      – SubjectFull: Diagnostic imaging
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      – SubjectFull: Magnetic resonance imaging
        Type: general
      – SubjectFull: Contrast media
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      – TitleFull: Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement.
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              Text: Jun2020
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              Y: 2020
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