No abstract

Psoriasis is a skin disorder which is caused by a genetic fault. 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 measure psoriasis severity by evaluating the area, erythema, scaliness and thickness of the plaques. However, the determination of PASI can be tedious and subjective. In this work, we develop a computer vision method that determines one of the PASI parameters, the lesion area. The method isolates healthy and healed skin areas from lesion areas by analysing the hue and chroma information in the CIE L*a*b* colour space. Centroids of healthy skin and psoriasis in the hue-chroma space are determined from selected sample. The Euclidean distance of all pixels from each centroid is calculated. Pixels are assigned to either healthy skin or psorasis lesion classes based on the minimum Euclidean distance. The study involves patients from different ethnic origins having three different skin tones. Results obtained show that the proposed method is able to determine lesion areas with accuracy higher than 90% for 28 out of 30 cases.

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.

With the proposed method, physicians are able to assess vitiligo treatment efficacies objectively.

In this paper, the effects of process parameters, such as the tool rotational and traverse speeds, on temperature evolution and the microstructural and mechanical properties of dissimilar friction stir welding between 3 mm thick AA 7075 aluminum alloy and polycarbonate (PC) plates were investigated. The tool rotational and traverse speeds were varied from 3000 to 3500 rpm and 50 to 150 mm min−1, respectively. The joint fabricated at 3250 rpm and 100 mm min−1 yielded a highest tensile load of 586 N. Microstructural analysis of the stir zone revealed an interlock phenomenon, the transportation of AA 7075 in polycarbonate, and the absence of ceramic-type (carbide, hydride or oxide) compounds. Microhardness (HV) measurement on the weld zone showed an uneven distribution due to the complicated microstructure of the welded joint. The maximum temperatures of 164°C and 66°C were obtained at 3250 rpm and 100 mm min−1 at a distance of 5 mm away from the welding centerline in the AA 7075 and PC side, respectively.

In this paper, we describe an image processing scheme to analyze and determine areas of skin that have undergone repigmentation in particular, during the treatment of vitiligo. In vitiligo cases, areas of skin become pale or white due to the lack of skin pigment called melanin. Vitiligo treatment causes skin repigmentation resulting in a normal skin color. However, it is difficult to determine and quantify the amount of repigmentation visually during treatment because the repigmentation progress is slow and moreover changes in skin color can only be discerned over a longer time frame typically 6 months. Here, we develop a digital image analysis scheme that can identify and determine vitiligo skin areas and repigmentation progression on a shorter time period. The technique is based on principal component analysis and independent component analysis which converts the RGB skin image into a skin image that represent skin areas due to melanin and haemoglobin only, followed by segmentation process. Vitiligo skin lesions are identified as skin areas that lack melanin (non-melanin areas). In the initial studies of 4 patients, the method has been able to quantify repigmentation in vitiligo lesion. Hence it is now possible to determine repigmentation progression objectively and treatment efficacy on a shorter time cycle.

No abstract

Psoriasis is a skin disorder which is caused by genetic fault. There is no cure for psoriasis, however, 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 measure psoriasis severity by evaluating the area, erythema, scaliness and thickness of the plaques. However, the calculation of PASI can be tedious and subjective. In this work, we develop a computer vision method that determines one of the PASI parameter, the lesion area. The method isolates healthy (or healed) skin areas from lesion areas by analyzing the hue and chroma information in the CIE L*a*b* colour space. Centroids of healthy skin and psoriasis in the hue-chroma space are determined from selected sample. Euclidean distance of all pixels from each centroid is calculated. Each pixel is assigned to the class with minimum Euclidean distance. The study involves patients from three different ethnic origins having different skin tones. Results obtained show that the proposed method is comparable to the dermatologist visual approach.