2018
DOI: 10.1049/joe.2017.0899
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Enhancement techniques for abnormality detection using thermal image

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Cited by 9 publications
(5 citation statements)
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“…Medical imaging is an important source and information source of modern medical appraisal. With the advanced application of medical imaging technology in the medical process, the accuracy and timing of medical examination have also been greatly improved [ 2 ]. For example, ultrasound and X-ray examinations, some scholars have shown that the diagnostic accuracy rate under medical imaging tests is over 97%.…”
Section: Introductionmentioning
confidence: 99%
“…Medical imaging is an important source and information source of modern medical appraisal. With the advanced application of medical imaging technology in the medical process, the accuracy and timing of medical examination have also been greatly improved [ 2 ]. For example, ultrasound and X-ray examinations, some scholars have shown that the diagnostic accuracy rate under medical imaging tests is over 97%.…”
Section: Introductionmentioning
confidence: 99%
“…The trial thermal pattern was generated using computational fluid dynamics software (Star CCM+). The trial image is generated such that, the colour blue depicts the lowest temperature and extends to the colour red which stands for the highest temperature [16]. The analysis was performed in the numerically generated image and then repeated for the thermal image of the cold storage…”
Section: Histogram Methodsmentioning
confidence: 99%
“…Erosion strips one layer of both inner and outer boundaries. It avoids small x=f−(f(Θ)s), where f is an image of the regions, s is a 3×3 structuring element, and x is an image of the region boundaries [16]. Dilation adds one layer on both the inner and outer boundaries.…”
Section: Morphological Functionmentioning
confidence: 99%
“…Previous research in image processing for forensic cases has identified several critical areas for further improvement. The first is the sufficient use of large datasets to develop high-performing systems [15]- [17]. Second, appropriate segmentation methods to detect wound edges in detail are selected as some algorithms struggle with detecting small or irregularly shaped wounds [14], [18].…”
Section: Introductionmentioning
confidence: 99%