NDLI: Automated skin lesion segmentation of dermoscopic images using GrabCut and k-means algorithms

Content Provider	IET Digital Library
Author	Jaisakthi, Seetharani Murugaiyan Mirunalini, Palaniappan Aravindan, Chandrabose
Abstract	Skin cancer is the most common type of cancer in the world and the incidents of skin cancer have been rising over the past decade. Even with a dermoscopic imaging system, which magnifies the lesion region, detecting and classifying skin lesions by visual examination is laborious due to the complex structures of the lesions. This necessitates the need for an automated skin lesion diagnosis system to enhance the diagnostic capability of dermatologists. In this study, the authors propose an automatic skin lesion segmentation method which can be used as a preliminary step for lesion classification. The proposed method comprises two major steps, namely preprocessing and segmentation. In the preprocessing step, noise such as illumination, hair and rulers are removed using filtering techniques and in the segmentation phase, skin lesions are segmented using the GrabCut segmentation algorithm. The k-means clustering algorithm is then used along with the colour features learnt from the training images to improve the boundaries of the segments. To evaluate the authors’ proposed method, they have used ISIC 2017 challenge dataset and P H 2 dataset. They have obtained Dice coefficient values of 0.8236 and 0.9139 for ISIC 2017 test dataset and P H 2 dataset, respectively.
Starting Page	1088
Ending Page	1095
Page Count	8
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 8, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5289
Journal	IET Computer Vision
Publisher Date	2018-07-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Automated Skin Lesion Diagnosis System Automatic Skin Lesion Segmentation Method Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging Cancer Colour Features Combinatorial Mathematics Computer Vision And Image Processing Technique Dermoscopic Image Dermoscopic Imaging System Diagnostic Capability Enhancement Filtering Method in Signal Processing Filtering Technique GrabCut Segmentation Algorithm Graph Theory Image Classification Image Colour Analysis Image Filtering Image Recognition Image Segmentation ISIC 2017 Challenge Dataset K-means Clustering Algorithm Lesion Region Medical Image Processing Object Detection Optical And Laser Radiation Patient Diagnostic Method And Instrumentation Pattern Clustering PH2 Dataset Preprocessing Step Segmentation Step Set Theory Skin Skin Cancer Skin Lesion Classification Skin Lesion Detection Visual Examination
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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