Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement.
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...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 6; pp. 1 - 11 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143571475&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143571475 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2020 vid: 44 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143571475 143571475 143571475 10.1007/s10916-020-01568-9 143571475 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement. aug: au: Subramani, Bharath Veluchamy, Magudeeswaran affil: Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, 624622, Dindigul, Tamilnadu, India sug: subj: Diagnostic Imaging Image Enhancement Methods Contrast Media Diagnostic Use Knee Radiography Brain Radiography Breast Radiography Magnetic Resonance Imaging Algorithms Human ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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