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...
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| Veröffentlicht in: | Medical & biological engineering & computing 2020-06, Vol.58 (6), p.1177-1197 |
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| creator | Mouzai, Meriem Tarabet, Chahrazed Mustapha, Aouache |
| description | 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 |
| doi_str_mv | 10.1007/s11517-020-02122-y |
| format | Article |
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Graphical Abstract
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Graphical Abstract
Graphical abstract of the proposed enhancement method</description><subject>Adolescent</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Computer Applications</subject><subject>Databases, Factual</subject><subject>Female</subject><subject>Fractures</subject><subject>Fuzzy Logic</subject><subject>Hand - diagnostic imaging</subject><subject>Human Physiology</subject><subject>Humans</subject><subject>Image contrast</subject><subject>Image enhancement</subject><subject>Image Enhancement - methods</subject><subject>Image quality</subject><subject>Imaging</subject><subject>Infant</subject><subject>Infant, Newborn</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Original Article</subject><subject>Physicians</subject><subject>Quality assessment</subject><subject>Quality 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X-ray enhancement using a fuzzy gamma reasoning model</title><author>Mouzai, Meriem ; Tarabet, Chahrazed ; Mustapha, Aouache</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-674485c2d0fbfbe283abf70c5103f180a1877a5e7af3a0dda99e216296aeaaa03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adolescent</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Biomedicine</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Computer Applications</topic><topic>Databases, Factual</topic><topic>Female</topic><topic>Fractures</topic><topic>Fuzzy Logic</topic><topic>Hand - diagnostic imaging</topic><topic>Human Physiology</topic><topic>Humans</topic><topic>Image contrast</topic><topic>Image enhancement</topic><topic>Image Enhancement - methods</topic><topic>Image 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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</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>32193863</pmid><doi>10.1007/s11517-020-02122-y</doi><tpages>21</tpages></addata></record> |
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| subjects | Adolescent Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Child Child, Preschool Computer Applications Databases, Factual Female Fractures Fuzzy Logic Hand - diagnostic imaging Human Physiology Humans Image contrast Image enhancement Image Enhancement - methods Image quality Imaging Infant Infant, Newborn Male Medical imaging Original Article Physicians Quality assessment Quality control Radiographs Radiography Radiography - methods Radiography - statistics & numerical data Radiology Reasoning Spine - diagnostic imaging X-Rays |
| title | Low-contrast X-ray enhancement using a fuzzy gamma reasoning model |
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