Low-contrast X-ray enhancement using a fuzzy gamma reasoning model.
X-ray images play an important role in providing physicians with satisfactory information correlated to fractures and diseases; unfortunately, most of these images suffer from low contrast and poor quality. Thus, enhancement of the image will increase the accuracy of correct information on pathologi...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 6; pp. 1177 - 1198 |
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| Autores principales: | , , |
| Formato: | 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=143136810&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143136810 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2020 vid: 58 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143136810 143136810 NLM32193863 10.1007/s11517-020-02122-y NLM32193863 143136810 ppf: 1177 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Low-contrast X-ray enhancement using a fuzzy gamma reasoning model. aug: au: Mouzai, Meriem Tarabet, Chahrazed Mustapha, Aouache affil: Division Télécom, Centre de Développement des Technologies Avancées (CDTA), P.O. Box 17 Baba-Hassen 16303, Algiers, Algeria sug: subj: Spine Hand Image Enhancement Methods Logic Radiography Methods Radiography Statistics and Numerical Data X-Rays Adolescence Infant, Newborn Female Resource Databases Child, Preschool Infant Male Child Ferrans and Powers Quality of Life Index Adolescent: 13-18 years Infant, Newborn: birth-1 month Child, Preschool: 2-5 years Infant: 1-23 months Child: 6-12 years Female Male ab: X-ray images play an important role in providing physicians with satisfactory information correlated to fractures and diseases; unfortunately, most of these images suffer from low contrast and poor quality. Thus, enhancement of the image will increase the accuracy of correct information on pathologies for an autonomous diagnosis system. In this paper, a new approach for low-contrast X-ray image enhancement based on brightness adjustment using a fuzzy gamma reasoning model (FGRM) is proposed. To achieve this, three phases are considered: pre-processing, Fuzzy model for adaptive gamma correction (GC), and quality assessment based on blind reference. The proposed approach's accuracy is examined through two different blind reference approaches based on statistical measures (BR-SM) and dispersion-location (BR-DL) descriptors, supported by resulting images. Experimental results of the proposed FGRM approach on three databases (cervical, lumbar, and hand radiographs) yield favorable results in terms of contrast adjustment and providing satisfactory quality images. Graphical Abstract Graphical abstract of the proposed enhancement method. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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