Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4964892 | Computers in Biology and Medicine | 2017 | 11 Pages |
Abstract
The proposed algorithm was tested using two datasets of healthy skin images and lesion images respectively. These datasets were taken from different imaging platforms in various illumination levels and varying skin colors. We compared the hair detection and segmentation results from our algorithm and six other hair segmentation methods of state of the art. Our method exhibits high value of sensitivity: 75% and specificity: 95%, which indicates significantly higher accuracy and better balance between true positive and false positive detection than the other methods.
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Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Ian Lee, Xian Du, Brian Anthony,