BACKGROUND/ PURPOSE: Dermoscopy images often suffer from low contrast caused by different light conditions, which reduces the accuracy of lesion border detection. Accordingly for lesion recognition, automatic melanoma border detection (MBD) is an initial as well as crucial task. METHOD: In this article, a novel perceptually oriented approach for MBD is presented by combing region and edge-based segmentation techniques. The MBD system for color contrast and segmentation improvement consists of four main steps: first, the RGB dermoscopy image is transformed to CIE L*a*b* color space, lesion contrast is then enhanced by adjusting and mapping the intensity values of the lesion pixels in the specified range using the three channels of CIE L*a*b*, a hill-climbing algorithm is used later to detect region-of-interest (ROI) map in a perceptually oriented color space using color channels (L*,a*,b*) and finally, an adaptive thresholding is applied to determine the optimal lesion border. Manually drawn borders obtained from an experienced dermatologist are utilized as a ground truth for performance evaluation. RESULTS: The proposed MBD method is tested on a total of 100 dermoscopy images. A comparative study with three state-of-the-art color and texture-based segmentation techniques (JSeg, dermatologists-like tumor area extraction: DTEA and region-based active contours: RAC), is also conducted to show the effectiveness of our MBD method using measures of true positive rate (TPR), false positive rate (FPR), and error probability (EP). Among different algorithms, our MBD algorithm achieved TPR of 94.25%, FPR of 3.56%, and EP of 4%. CONCLUSIONS: The proposed MBD approach is highly accurate to detect the lesion border area. The MBD software and sample of dermoscopy images can be downloaded at http://cs.ntu.edu.pk/research.php.
BACKGROUND/ PURPOSE: Dermoscopy images often suffer from low contrast caused by different light conditions, which reduces the accuracy of lesion border detection. Accordingly for lesion recognition, automatic melanoma border detection (MBD) is an initial as well as crucial task. METHOD: In this article, a novel perceptually oriented approach for MBD is presented by combing region and edge-based segmentation techniques. The MBD system for color contrast and segmentation improvement consists of four main steps: first, the RGB dermoscopy image is transformed to CIE L*a*b* color space, lesion contrast is then enhanced by adjusting and mapping the intensity values of the lesion pixels in the specified range using the three channels of CIE L*a*b*, a hill-climbing algorithm is used later to detect region-of-interest (ROI) map in a perceptually oriented color space using color channels (L*,a*,b*) and finally, an adaptive thresholding is applied to determine the optimal lesion border. Manually drawn borders obtained from an experienced dermatologist are utilized as a ground truth for performance evaluation. RESULTS: The proposed MBD method is tested on a total of 100 dermoscopy images. A comparative study with three state-of-the-art color and texture-based segmentation techniques (JSeg, dermatologists-like tumor area extraction: DTEA and region-based active contours: RAC), is also conducted to show the effectiveness of our MBD method using measures of true positive rate (TPR), false positive rate (FPR), and error probability (EP). Among different algorithms, our MBD algorithm achieved TPR of 94.25%, FPR of 3.56%, and EP of 4%. CONCLUSIONS: The proposed MBD approach is highly accurate to detect the lesion border area. The MBD software and sample of dermoscopy images can be downloaded at http://cs.ntu.edu.pk/research.php.
Authors: Tomasz Szmuda; Paweł Słoniewski; Wiktor Olijewski; Janusz Springer; Przemysław M Waszak Journal: J Neurooncol Date: 2015-02-22 Impact factor: 4.130
Authors: Lavinia Ferrante di Ruffano; Yemisi Takwoingi; Jacqueline Dinnes; Naomi Chuchu; Susan E Bayliss; Clare Davenport; Rubeta N Matin; Kathie Godfrey; Colette O'Sullivan; Abha Gulati; Sue Ann Chan; Alana Durack; Susan O'Connell; Matthew D Gardiner; Jeffrey Bamber; Jonathan J Deeks; Hywel C Williams Journal: Cochrane Database Syst Rev Date: 2018-12-04