[1]赵晓梅,刘兆邦,张正平,等.基于超像素和密度聚类算法的皮肤镜图像分割[J].计算机技术与发展,2020,30(06):167-171.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 032]
　ZHAO Xiao-mei,LIU Zhao-bang,ZHANG Zheng-ping,et al.Dermoscopy Image Segmentation Based on Superpixel and Density Clustering Algorithm[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(06):167-171.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 032]
点击复制

基于超像素和密度聚类算法的皮肤镜图像分割()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年06期
页码:
167-171
栏目:
应用开发研究
出版日期:
2020-06-10

文章信息/Info

Title:
Dermoscopy Image Segmentation Based on Superpixel and Density Clustering Algorithm
文章编号:
1673-629X(2020)06-0167-05
作者:
赵晓梅1 ; 刘兆邦2 ; 张正平1 ; 谢 璟3 ; 陆千琦3*
1. 贵州大学 大数据与信息工程学院,贵州 贵阳 550025; 2. 中国科学院 苏州生物医学工程技术研究所,江苏 苏州 215163; 3. 温州市人民医院,浙江 温州 325699
Author(s):
ZHAO Xiao-mei1 ; LIU Zhao-bang2 ; ZHANG Zheng-ping1 ; XIE Jing3 ; LU Qian-qi3*
1. School of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China; 2. Suzhou Institute of Biomedical Engineering and Technology,Chinese Academy of Sciences,Suzhou 215163,China; 3. Wenzhou People’s Hospital,Wenzhou 325699,China
关键词:
皮肤病变; 超像素; 密度聚类; 合并; 形态学方法
Keywords:
skin lesions; superpixel; density clustering; merge; morphological method
分类号:
TP391. 7
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 06. 032
摘要:
皮肤病变的精确分割是实现皮肤病图像自动分析的关键步骤,为后续的特征提取、病变分类等步骤提供了便捷性。 然而,现有方法存在分割不足或分割过度的问题,通常会出现皮损的边缘部分丢失和背景错分的情况。 针对这些问题,提出了一种新的自动皮肤病变分割方法,该方法包含 4 个步骤,首先预处理皮肤病图像,去除毛发噪声,增加分割的精确度;随后利用超像素对图像进行初始分割,形成网格状图像;然后采用密度聚类算法对颜色相近的超像素进行合并;最后使用形态学方法处理得到最终的分割结果。 在 ISIC2018 公开的皮肤镜图像数据集上进行实验,结果表明,所提算法与其他分割方法相比:分割结果更精确,更鲁棒,另外从分割指标上也可得出,该分割算法在边缘处理上更加完美。
Abstract:
Accurate segmentation of skin lesions is the key step to realize automatic analysis of skin disease images,which provides convenience for subse-quent steps such as feature extraction and lesion classification. However,the existing methods have the problems of insufficient segmentation or excessive segmentation, which usually results in the loss of skin lesions and the wrong separation of background. Aiming at these problems, we propose a new automatic skin lesion segmentation method which consists of four steps. Firstly,the skin image is pretreated to remove hair noise and increase the segmentation accuracy. Then the image is initially segmented by superpixel to form a mesh image. Next the superpixels with similar colors are merged by the density clustering algorithm. Finally,morphological methods are used to obtain the final segmentation results. Experiment on the dermoscope image data set published by ISIC2018 shows that compared with other segmentation methods, the proposed algorithm is more accurate and robust in segmentation results. In addition,it can be concluded from the segmentation index that the proposed segmentation algorithm is more perfect in edge processing.

相似文献/References:

[1]辛月兰.基于超像素的Grabcut彩色图像分割[J].计算机技术与发展,2013,(07):48.
　XIN Yue-lan.Superpixel-based Grabcut Color Image Segmentation[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2013,(06):48.
[2]袁恒东.基于标签传递图割的图像分割算法[J].计算机技术与发展,2017,27(12):35.[doi:10.3969/ j. issn.1673-629X.2017.12.008]
　YUAN Heng-dong.An Image Segmentation Algorithm Based on Label Propagation Graph Cut[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2017,27(06):35.[doi:10.3969/ j. issn.1673-629X.2017.12.008]
[3]张小凤,刘向阳.基于图像超像素分析的图像分割方法[J].计算机技术与发展,2018,28(07):25.[doi:10.3969/ j. issn.1673-629X.2018.07.006]
　ZHANG Xiao-feng,LIU Xiang-yang.Image Segmentation Based on Image Superpixel Analysis[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2018,28(06):25.[doi:10.3969/ j. issn.1673-629X.2018.07.006]
[4]张 健,钟中志,柯艳国,等.基于 MHSW 特征融合的火焰检测[J].计算机技术与发展,2019,29(12):184.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 033]
　ZHANG Jian,ZHONG Zhong-zhi,KE Yan-guo,et al.Flame Detection Based on MHSW Feature Fusion[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2019,29(06):184.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 033]
[5]汪 昊,刘向阳.基于区域中心的交互式图像前景提取方法[J].计算机技术与发展,2020,30(02):12.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 003]
　WANG Hao,LIU Xiang-yang.An Interactive Image Foreground Extraction Method Based on Region Center[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(06):12.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 003]
[6]李 普,陈 黎.基于超像素随机游走的自然场景图像分割方法[J].计算机技术与发展,2021,31(12):61.[doi:10. 3969 / j. issn. 1673-629X. 2021. 12. 011]
　LI Pu,CHEN Li.Natural Scene Image Segmentation Method Based on Super-pixel Random Walk[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2021,31(06):61.[doi:10. 3969 / j. issn. 1673-629X. 2021. 12. 011]
[7]颜玉杰,刘向阳.基于测地距离的超像素分析算法[J].计算机技术与发展,2022,32(02):58.[doi:10. 3969 / j. issn. 1673-629X. 2022. 02. 009]
　YAN Yu-jie,LIU Xiang-yang.Superpixel Analysis Algorithm Based on Geodesic Distance[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2022,32(06):58.[doi:10. 3969 / j. issn. 1673-629X. 2022. 02. 009]

更新日期/Last Update: 2020-06-10