Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/136857
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dc.contributor.authorMuhammad Adamen_US
dc.contributor.authorNg, Eddie Yin Kweeen_US
dc.contributor.authorOh, Shu Lihen_US
dc.contributor.authorHeng, Marabelle L.en_US
dc.contributor.authorHagiwara, Yukien_US
dc.contributor.authorTan, Jen Hongen_US
dc.contributor.authorTong, Jasper W. K.en_US
dc.contributor.authorAcharya, U. Rajendraen_US
dc.date.accessioned2020-01-31T05:27:54Z-
dc.date.available2020-01-31T05:27:54Z-
dc.date.issued2018-
dc.identifier.citationMuhammad Adam, Ng, E. Y. K., Oh, S. L., Heng, M. L., Hagiwara, Y., Tan, J. H., . . . Acharya, U. R. (2018). Automated characterization of diabetic foot using nonlinear features extracted from thermograms. Infrared Physics & Technology, 89, 325-337. doi:10.1016/j.infrared.2018.01.022en_US
dc.identifier.issn1350-4495en_US
dc.identifier.urihttps://hdl.handle.net/10356/136857-
dc.description.abstractDiabetic foot is a major complication of diabetes mellitus (DM). The blood circulation to the foot decreases due to DM and hence, the temperature reduces in the plantar foot. Thermography is a non-invasive imaging method employed to view the thermal patterns using infrared (IR) camera. It allows qualitative and visual documentation of temperature fluctuation in vascular tissues. But it is difficult to diagnose these temperature changes manually. Thus, computer assisted diagnosis (CAD) system may help to accurately detect diabetic foot to prevent traumatic outcomes such as ulcerations and lower extremity amputation. In this study, plantar foot thermograms of 33 healthy persons and 33 individuals with type 2 diabetes are taken. These foot images are decomposed using discrete wavelet transform (DWT) and higher order spectra (HOS) techniques. Various texture and entropy features are extracted from the decomposed images. These combined (DWT + HOS) features are ranked using t-values and classified using support vector machine (SVM) classifier. Our proposed methodology achieved maximum accuracy of 89.39%, sensitivity of 81.81% and specificity of 96.97% using only five features. The performance of the proposed thermography-based CAD system can help the clinicians to take second opinion on their diagnosis of diabetic foot.en_US
dc.language.isoenen_US
dc.relation.ispartofInfrared Physics and Technologyen_US
dc.rights© 2018 Elsevier B.V. All rights reserved. This paper was published in Infrared Physics and Technology and is made available with permission of Elsevier B.V.en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleAutomated characterization of diabetic foot using nonlinear features extracted from thermogramsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.identifier.doi10.1016/j.infrared.2018.01.022-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85041455618-
dc.identifier.volume89en_US
dc.identifier.spage325en_US
dc.identifier.epage337en_US
dc.subject.keywordsFooten_US
dc.subject.keywordsDiabetesen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
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