Validation on 3D surface roughness algorithm for measuring roughness of psoriasis lesion - Ahmad Fadzil, M Hani; Prakasa, Esa; Nugroho, Hermawan; Affandi, Azura Mohd; Hussein, Suraiya Hussein (World Academy of Science Engineering and Technology, 2010)
Psoriasis is a widespread skin disease affecting up to 2% population with plaque psoriasis accounting to about 80%. It can be identified as a red lesion and for the higher severity the lesion is usually covered with rough scale. Psoriasis Area Severity Index (PASI) scoring is the gold standard method for measuring psoriasis severity. Scaliness is one of PASI parameter that needs to be quantified in PASI scoring. Surface roughness of lesion can be used as a scaliness feature, since existing scale on lesion surface makes the lesion rougher. The dermatologist usually assesses the severity through their tactile sense, therefore direct contact between doctor and patient is required. The problem is the doctor may not assess the lesion objectively. In this paper, a digital image analysis technique is developed to objectively determine the scaliness of the psoriasis lesion and provide the PASI scaliness score. Psoriasis lesion is modeled by a rough surface. The rough surface is created by superimposing a smooth base (curve) surface with a triangular waveform. For roughness determination, a polynomial surface fitting is used to estimate base surface followed by a subtraction between rough and base surface to give height map matrix (surface deviations). Roughness index is calculated by using average roughness equation to the height map matrix. The roughness algorithm has been tested to 55 lesion models. From roughness validation result, only 2 models can not be accepted (percentage error is greater than 10%). These errors occur due the tapes not mounted to the curve surface properly. This has resulted in folding at the edges. Roughness algorithm is validated for roughness measurement on abrasive papers at flat surface. The Pearson’s correlation coefficient of grade value (G) of abrasive paper and Ra is -0.9488, its shows there is a strong relation between G and Ra. The algorithm needs to be improved by surface filtering, especially to overcome a problem with noisy data.