Hair segmentation and counting algorithms in microscopy image

One emerging subject in medical image processing is to quantitatively assess the health and the properties of cranial hairs, including density, diameter, length, level of oiliness, and others. This information helps hair specialists with making a more accurate diagnosis and the therapy required. Unfortunately, present hair care and scalp diagnosis systems lack both robustness and efficiency. Hair counting is usually done manually, producing results that are often unreliable. To solve this problem, we developed a practical hair counting algorithm. This analytic system calculates the number of hairs on a scalp using an unsupervised mechanism, providing accurate information for both the hair specialist and the patient. Our proposed hair counting algorithm is substantially more accurate than the Hough-based one, and is robust to curls, oily scalp, noise-corruption, and overlapping hairs, under various levels of illumination.