Border Detection of Skin Lesion Images Based on Fuzzy C-Means Thresholding

被引:14
|
作者
Supot, Sookpotharom [1 ]
机构
[1] Bangkok Univ, Sch Engn, Dept Elect Engn, Klongluang 12120, Pathumtani, Thailand
来源
THIRD INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING | 2009年
关键词
skin lesion; malignant melanoma; image segmentation; fuzzy c-means;
D O I
10.1109/WGEC.2009.96
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The accurate location of the border of skin lesions is an important first step in the automatic diagnosis of malignant melanoma. In this paper, we propose a new method of segmentation to locate the skin lesion. The method consists of two stages; image pre-processing and image segmentation. As the first step of image analysis, pre-processing techniques are implemented to remove noise and undesired structures for the images using median filtering. In the second step, the fuzzy c-means (FCM) thresholding technique is used to segment and localize the lesion. The border detection results are visually examined by an expert dermatologist and are found to be highly accurate.
引用
收藏
页码:777 / 780
页数:4
相关论文
共 50 条
  • [21] A massive images classification method based on MapReduce parallel fuzzy C-means clustering
    Hu, Jinping
    Cheng, Qian
    Wen, Zhicheng
    JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2021, 21 (04) : 999 - 1011
  • [22] Wavelet Frame-Based Fuzzy C-Means Clustering for Segmenting Images on Graphs
    Wang, Cong
    Pedrycz, Witold
    Yang, JianBin
    Zhou, MengChu
    Li, ZhiWu
    IEEE TRANSACTIONS ON CYBERNETICS, 2020, 50 (09) : 3938 - 3949
  • [23] Fuzzy C-Means Based Feature Selection Mechanism for Wireless Intrusion Detection
    Tseng, Chinyang Henry
    Tsaur, Woei-Jiunn
    Mujiono
    2021 INTERNATIONAL CONFERENCE ON SECURITY AND INFORMATION TECHNOLOGIES WITH AI, INTERNET COMPUTING AND BIG-DATA APPLICATIONS, 2023, 314 : 143 - 152
  • [24] Overlapping Community Detection Algorithm Based on Spectral and Fuzzy C-Means Clustering
    He, Xiaoshan
    Guo, Kun
    Liao, Qinwu
    Yan, Qiaoling
    COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING, CHINESECSCW 2018, 2019, 917 : 487 - 497
  • [25] Intrusion Detection Network Based on Fuzzy C-Means and Particle Swarm Optimization
    Zhang, Zhongxing
    Gu, Baoping
    PROCEEDINGS OF THE 6TH INTERNATIONAL ASIA CONFERENCE ON INDUSTRIAL ENGINEERING AND MANAGEMENT INNOVATION, VOL 2: INNOVATION AND PRACTICE OF INDUSTRIAL ENGINEERING AND MANAGMENT, 2016, : 111 - 119
  • [26] Color image segmentation using histogram thresholding - Fuzzy C-means hybrid approach
    Tan, Khang Siang
    Isa, Nor Ashidi Mat
    PATTERN RECOGNITION, 2011, 44 (01) : 1 - 15
  • [27] Application of Network Intrusion Detection Based on Fuzzy C-Means Clustering Algorithm
    Ren, Wuling
    Cao, Jinzhu
    Wu, Xianjie
    2009 THIRD INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL 3, PROCEEDINGS, 2009, : 19 - +
  • [28] An intelligent skin-color capture method based on fuzzy C-means with applications
    Hsiao, Shih-Wen
    Yen, Chih-Huang
    Lee, Chu-Hsuan
    COLOR RESEARCH AND APPLICATION, 2017, 42 (06) : 775 - 787
  • [29] Modeling of Articular Cartilage with Goal of Early Osteoarthritis Extraction Based on Local Fuzzy Thresholding Driven by Fuzzy C-Means Clustering
    Kubicek, Jan
    Krestanova, Alice
    Penhaker, Marek
    Augustynek, Martin
    Cerny, Martin
    Oczka, David
    INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2019, PT II, 2019, 11432 : 289 - 299
  • [30] The MinMax Fuzzy C-Means
    Mashayekhi, Yoosof
    Nazerfard, Ehsan
    Rahbar, Arman
    Mahmood, Samira Shirzadeh Haji
    2019 IEEE 5TH CONFERENCE ON KNOWLEDGE BASED ENGINEERING AND INNOVATION (KBEI 2019), 2019, : 210 - 215