Farnaz Mohammadhedayati, Mohammadtaghi Ahady, Shagayegh Manouchehri,
Volume 10, Issue 2 (8-2019)
Abstract
Background and Aim: Demodex is a common mite and ectoparasite in humans and animals. The existence of Demodex folliculorum and Demodex brevis in human skin can have a role in some inflammatory skin diseases such as acne, rosacea, and dermatitis. This study aimed to identify the prevalence of Demodex ectoparasite in women and its possible association with skin lesions.
Methods: Fifty women with skin lesions (case group) and 50 women without skin lesions (control group) were selected and evaluated by clinical and
laboratory tests. The study was approved by the Ethics Committee and the volunteers provided written informed consent. The skin scrapings were investigated by placing in a 10% potassium hydroxide (KOH) and lactophenol solutions and were analyzed under microscope to detect the Demodex.
Results: Twenty-two out of 50 patients (with acne, rosacea, dermatitis, and eczema) had Demodex folliculorum infestation (44%). The highest levels of infestation were observed in women aged 20-30 years (22%) and the infestation of Demodex was only confirmed in 10 cases (20%) out of the 50 subjects in the control group.
Conclusion: The rate of Demodex in patients with skin lesions was much higher than healthy subjects. There was a significant association between Demodex and skin lesions (sig.=0.023, P<0.05). The authors suggest that Demodox treatment should be considered in the therapeutic strategy of some inflammatory skin diseases.
Fatemeh Torkashvand, Abdolah Chalechale, Sina Vafi,
Volume 15, Issue 4 (2-2025)
Abstract
Background and Aim: Image brightness heterogeneity is one of the major challenges in computer image processing that can lead to inaccurate results in image segmentation. Despite the existence of numerous segmentation methods, few studies have been conducted on the effect of brightness heterogeneity and the selection of the best color channels in segmentation. In this paper, different color spaces have been used for automatic detection of skin lesions.
Methods: In this study, the LSE (Level Set Evolution) segmentation method along with intensity smoothing has been used for computer recognition of skin lesions. First, the brightness heterogeneity is reduced and a more uniform image is created. Then, the proposed segmentation divides the image domain into distinct regions. This method results in more accurate recognition of skin lesions.
Results: The proposed method has been tested on 200 dermoscopic images from the known PH2 dataset using different color channels. The results show that this method performs better than other methods. Accuracy of 97%, sensitivity of 98%, specificity of 99% and Dice coefficient of 92% have been obtained.
Conclusion: This method has the ability to accurately isolate and diagnose lesions and can help doctors in the treatment process of skin lesions.
