Objective assessment of psoriasis erythema for PASI scoring

Abstract

Skin colour is vital information in dermatological diagnosis as it reflects the pathological condition beneath the skin. It is commonly used to indicate the extent of diseases such as psoriasis, 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, the current gold standard method, PASI (Psoriasis Area and Severity Index), is used to determine severity of psoriasis lesion. Erythema (redness) is one parameter in PASI and this condition is assessed visually, thus leading to subjective and inconsistent results. Current methods or instruments that assess erythema have limitations, such as being able to measure erythema well for low pigmented skin (fair skin) but not for highly pigmented skin (dark skin) or vice versa. In this work, we proposed an objective assessment of psoriasis erythema for PASI scoring for different (low to highly pigmented) skin types. The colour of psoriasis lesions are initially obtained by using a chromameter giving the values L*, a*, and b* of CIELAB colour space. The L* value is used to classify skin into three categories: low, medium and highly pigmented skin. The lightness difference (DeltaL*), hue difference (Deltah(ab)), chroma (DeltaC*(ab)) between lesions and the surrounding normal skin are calculated and analysed. It is found that the erythema score of a lesion can be distinguished by their Deltah(ab) value within a particular skin type group. 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 for different skin types i.e. low (fair skin) to highly pigmented (dark skin) skin types can be determined objectively and consistent with dermatology scoring.

Objective measurement of erythema in psoriasis using digital color photography with color calibration

“…We compiled a list of features from other similar studies that were intended to match the degree of redness in an image (9), (10), (11). This is the complete list of features considered:

Mean, standard deviation, skewness, and kurtosis of each of the R, G, and B components

Mean, standard deviation, skewness, and kurtosis of the difference between the red band and the green band (RG) and likewise for the difference between the blue and green bands (BG)

Proportion of the image that is red (R/(R+G+B))

Mean, standard deviation, skewness, and kurtosis of each of the L*, a*, b* components

Differences between the means of the red, green, and blue channels of the plaque and surrounding skin

Difference between the average hemoglobin and melanin components of the plaque and surrounding skin (10)

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What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

“…As a result there have been substantial efforts to develop novel noninvasive optical imaging techniques as a way to detect and analyze skin lesions [222], [100], with recent advances involving smart hand-held devices [36], [263]. In addition to skin cancer, image-based methods can apply to other common skin diseases like chronic inflammatory psoriasis [149], [57], pressure ulcers [141] and hyper-pigmentation evolution [194].…”

The reliability and validity of outcome measures for atopic dermatitis in patients with pigmented skin: A grey area

“…Also, cultural and environmental factors can change how the skin is cared for, leading to further heterogeneity in manifestation. In addition, the clinician’s perception of color may be distorted by the background skin pigmentation or be mistaken for post-inflammatory hyperpigmentation (Ahmad Fadzil et al, 2009). Furthermore, clinical experience with managing patients with skin of color is a contributing factor.…”