Paper Info
Title
Automatic Skin Lesion Segmentation Based on Supervised Learning
Abstract
The accuracy of automatic skin lesion detection is important in the computer-aided diagnosis (CAD) of skin cancers. In this paper, a novel method of automatic skin lesion segmentation to get the accurate border is proposed. The initial lesion is extracted by the Otsu's threshold firstly. Secondly, the outer peripheral region around the initial lesion is obtained with the affinity propagation clustering method (AP). The outer periphery is divided into small homogeneous sub-regions using simple linear iterative clustering (SLIC). Finally, the homogeneous sub-regions are classified into the background skin and lesion by supervised learning and the accuracy border is obtained. A series of experiments done on the proposed method and the other four state-of-the-art automatic methods show that the proposed method delivers better accuracy and robust segmentation results.
Year
DOI
Venue
2013
10.1109/ICIG.2013.39
ICIG
Keywords
Field
DocType
otsu threshold,supervised learning,pattern clustering,slic,skin cancer diagnosis,novel method,background skin,automatic skin lesion segmentation,automatic skin lesion detection,learning (artificial intelligence),initial lesion,image segmentation,sub-regions,affinity propagation clustering method,cancer,feature extraction,skin cancer,state-of-the-art automatic method,simple linear iterative clustering,better accuracy,accuracy border,ap,computer-aided diagnosis,iterative methods,medical image processing,learning artificial intelligence,accuracy,skin
Computer vision,Scale-space segmentation,Pattern recognition,Lesion,Computer science,Segmentation,Segmentation-based object categorization,Supervised learning,Feature extraction,Image segmentation,Artificial intelligence,Cluster analysis
Conference
Citations 
PageRank 
References 
2
0.37
10
Authors
4
Name
Order
Citations
PageRank
Yefen Wu161.10
Fengying Xie218215.33
Zhiguo Jiang332145.58
Ru-Song Meng4282.75