Many applications need to segment out all small round regions in an image. This task of finding dots can be viewed as a region segmentation problem where the dots form one region and the areas between dots form the other. We formulate it as a graph cuts problem with two types of grouping cues: short-range attraction based on feature similarity and long-range repulsion based on feature dissimilarity. The feature we use is a pixel-centric relational representation that encodes local convexity: Pixels inside the dots and outside the dots become sinks and sources of the feature vector. Normalized cuts on both attraction and repulsion pop out all the dots in a single binary segmentation. Our experiments show that our method is more accurate and robust than state-of-art segmentation algorithms on four categories of microscopic images. It can also detect textons in natural scene images with the same set of parameters.

Quantifying alopecia areata in real time has been a challenge for clinicians and investigators. Although several scoring systems exist, they can be cumbersome. Because there are more clinical trials in alopecia areata, there is an urgent need for a quantitative system that is reproducible, standardized, and simple. In this article, a computer imaging algorithm to recreate the Severity of Alopecia Tool scoring system in an automated way is presented. A pediatric alopecia areata image set of four view-standardized photographs was created, and texture analysis was used to distinguish between normal hair and bald scalp. By exploiting local image statistics and the similarity of hair appearance variations across the pediatric alopecia examples, we then used a reference set of hair textures, derived from intensity distributions over very small image patches, to provide global context and improve partitioning of each individual image into areas of different hair densities. This algorithm can mimic a Severity of Alopecia Tool (score) and may also provide more information about the continuum of changes in density of hair seen in alopecia areata.

Background: Machine learning (ML), a subset of artificial intelligence (AI) that aims to teach machines to automatically learn tasks by inferring patterns from data, holds significant promise to aid psoriasis care. Applications include evaluation of skin images for screening and diagnosis as well as clinical management including treatment and complication prediction. Objective: To summarize literature on ML applications to psoriasis evaluation and management and to discuss challenges and opportunities for future advances. Methods: We searched MEDLINE, Google Scholar, ACM Digital Library, and IEEE Xplore for peer-reviewed publications published in English through December 1, 2019. Our search queries identified publications with any of the 10 computing-related keywords and “psoriasis” in the title and/or abstract. Results: Thirty-three studies were identified. Articles were organized by topic and synthesized as evaluation- or management-focused articles covering 5 content categories: (A) Evaluation using skin images: (1) identification and differential diagnosis of psoriasis lesions, (2) lesion segmentation, and (3) lesion severity and area scoring; (B) clinical management: (1) prediction of complications and (2) treatment. Conclusion: Machine learning has significant potential to aid psoriasis evaluation and management. Current topics popular in ML research on psoriasis are the evaluation of medical images, prediction of complications, and treatment discovery. For patients to derive the greatest benefit from ML advancements, it is helpful for dermatologists to have an understanding of ML and how it can effectively aid their assessments and decision-making.

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The qualitative grading of acne is important for routine clinical care and clinical trials, and although many useful systems exist, no single acne global grading system has had universal acceptance. In addition, many current instruments focus primarily on evaluating primary lesions (eg, comedones, papules, and nodules) or exclusively on signs of secondary change (eg, postinflammatory hyperpigmentation, scarring).OBJECTIVES To develop and validate an acne global grading system that provides a comprehensive evaluation of primary lesions and secondary changes due to acne. DESIGN, SETTING, AND PARTICIPANTSThis diagnostic study created a multidimensional acne severity feature space by analyzing decision patterns of pediatric dermatologists evaluating acne. Modeling acne severity patterns based on visual image features was then performed to reduce dimensionality of the feature space to a novel 2-dimensional grading system, in which severity levels are functions of multidimensional acne cues. The system was validated by 6 clinicians on a new set of images. All images used in this study were taken from a retrospective, longitudinal data set of 150 patients diagnosed with acne, ranging across the entire pediatric population (aged 0-21 years), excluding images with any disagreement on their diagnosis, and selected to adequately span the range of acne types encountered in the clinic.