Description
If detected early, skin cancer has a 95-100 percent successful treatment rate; therefore early detection is crucial and several computer-aided methods have been developed to assist dermatologists. In skin images removing hairs without altering the lesion is important to effectively apply detection algorithms. This research focuses on the use of image processing techniques to remove hairs by identifying hair pixels contained within a binary image mask using the Generalized Radon Transform. The Radon Transform was adapted to find quadratic curves characterized by rotational angle and scaling. The method detects curved hairs in the image mask for removal and replacement through pixel interpolation. Implementing this technique in MATLAB gives the ability to perform tests rapidly on both simulated and actual images. The quadratic Radon transform performs well in curve detection; however, the research points out the need for better algorithms to improve hair masking, peak detection, and interpolation replacement
Publication Date
4-12-2013
Publisher
Case Western Reserve University Research ShowCase 2013
City
Cleveland, OH
Keywords
Radon transform, curve detection, Quadratic, Hair Removal
Recommended Citation
Kretzler, Madison and Buchner, Marc, "Automated Curved Hair Detection and Removal in Skin Images to Support Automated Melanoma Detection" (2013). Research ShowCASE. 6.
https://commons.case.edu/research-showcase/6