Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
Department of Computer Engineering, Kırıkkale University, 71451 Kırıkkale, Turkey
*
Author to whom correspondence should be addressed.
Diagnostics 2019, 9(3), 72; https://doi.org/10.3390/diagnostics9030072
Received: 30 May 2019
/
Revised: 26 June 2019
/
Accepted: 8 July 2019
/
Published: 10 July 2019
(This article belongs to the Section Medical Imaging and Theranostics)
Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.
View Full-Text
Keywords:
skin cancer; skin lesion segmentation; melanoma; convolutional neural networks; Yolo; GrabCut
▼
Show Figures
Graphical abstract
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Ünver, H.M.; Ayan, E. Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm. Diagnostics 2019, 9, 72. https://doi.org/10.3390/diagnostics9030072
AMA Style
Ünver HM, Ayan E. Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm. Diagnostics. 2019; 9(3):72. https://doi.org/10.3390/diagnostics9030072
Chicago/Turabian StyleÜnver, Halil M., and Enes Ayan. 2019. "Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm" Diagnostics 9, no. 3: 72. https://doi.org/10.3390/diagnostics9030072
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
