@article{scholars490,
           pages = {4070--4073},
         journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference},
           title = {Objective assessment of psoriasis erythema for PASI scoring.},
            year = {2008},
             doi = {10.1109/iembs.2008.4650103},
            note = {cited By 3},
          author = {Ahmad Fadzil, M. H. and Ihtatho, D. and Affandi, A. M. and Hussein, S. H.},
            issn = {1557170X},
        abstract = {Skin colour is vital information in dermatological diagnosis. It reflects pathological condition beneath the skin and commonly being used to indicate the extent of a disease. Psoriasis is a skin disease which is indicated by the appearance of red plaques. Although there is no cure for psoriasis, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, PASI (Psoriasis Area and Severity Index) which is the current gold standard method is used to determine severity of psoriasis lesion. Erythema (redness) is one parameter in PASI. Commonly, the erythema is assessed visually, thus leading to subjective and inconsistent result. In this work, we proposed an objective assessment of psoriasis erythema for PASI scoring. The colour of psoriasis lesion is analyzed by DeltaL, Deltahue, and Deltachroma of CIELAB colour space. References of lesion with different scores are obtained from the selected lesions by two dermatologists. Results based on 38 lesions from 22 patients with various level of skin pigmentation show that PASI erythema score can be determined objectively and consistent with dermatology scoring.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903805283&doi=10.1109\%2fiembs.2008.4650103&partnerID=40&md5=46e63489b81f0f48d412ee6102d808d3},
        keywords = {algorithm; article; automated pattern recognition; computer assisted diagnosis; dermatology; equipment design; human; metabolism; methodology; observer variation; pathophysiology; psoriasis; skin; skin pigmentation; statistical model; theoretical model; vision, Algorithms; Dermatology; Diagnosis, Computer-Assisted; Equipment Design; Humans; Models, Statistical; Models, Theoretical; Observer Variation; Pattern Recognition, Automated; Psoriasis; Skin; Skin Pigmentation; Vision, Ocular, MLCS; MLOWN}
}