Introduction: This paper presents a comprehensive review of acne grading and measurement. Acne is a chronic disorder of the pilosebaceous units, with excess sebum production, follicular epidermal hyperproliferation, inflammation and Propionibacterium acnes activity. Most patients are affected with acne vulgaris, which is the prevalent type of acne. Acne vulgaris consists of comedones (whitehead and blackhead), papules, pustules, nodules and cysts. Objectives: To review and identify the issues for acne vulgaris grading and computational assessment methods. To determine the future direction for addressing the identified issues. Methods: There are two main methods of assessment for acne severity grading, namely, lesion counting and comparison of patient with a photographic standard. For the computational assessment method, the emphasis is on computational imaging techniques.

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Acne affects 85% of adolescents at some time during their lives. There are various causes for acne including genetic, hormonal, sebaceous activity, bacteria, climate, chemical and psychological. Till now, dermatologists use manual methods such as direct visual assessment and ordinary flash photography to assess the acne. These methods are very time consuming and tedious. To address these issues, researchers in recent years have proposed computational imaging methods for aiding in the acne diagnosis. This paper proposes an algorithm to identify acne lesions, scars and normal skin features from photographs taken by Digital Single-Lens Reflex (DSLR) cameras. The images are converted from RGB to CIELAB color space, thresholded to three clusters and segmented using minimum Euclidean distance. The segmentation results from randomly selected images show sensitivity and specificity of greater than 80%.

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