Blotch detection in pigmented skin lesions using Fuzzy Co-Clustering and Texture Segmentation

被引:13
|
作者
Madasu, Vamsi K. [1 ]
Lovell, Brian C. [2 ]
机构
[1] Univ Queensland, Brisbane, Qld 4072, Australia
[2] NICTA, Melbourne, Vic, Australia
关键词
D O I
10.1109/DICTA.2009.15
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The 'Fuzzy Co-Clustering Algorithm for Images (FCCI)' technique has been successfully applied to colour segmentation of medical images. The goal of this work is to extend this technique by the inclusion of texture features as a clustering parameter for detecting blotches in skin lesions based on colour information. The objective function is optimized using the bacterial foraging algorithm which gives image specific values to the parameters involved in the algorithm. Experiments show the efficacy of the proposed method in extracting malignant blotches from different types of pigmented skin lesion images.
引用
收藏
页码:25 / +
页数:2
相关论文
共 50 条
  • [31] Anomaly Detection in Microblogging via Co-Clustering
    Wu Yang
    Guo-Wei Shen
    Wei Wang
    Liang-Yi Gong
    Miao Yu
    Guo-Zhong Dong
    Journal of Computer Science and Technology, 2015, 30 : 1097 - 1108
  • [32] Segmentation of Pigmented Skin Lesions Using Non-negative Matrix Factorization
    Cavalcanti, Pablo G.
    Scharcanski, Jacob
    Martinez, Cesar E.
    Di Persia, Leandro E.
    2014 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC) PROCEEDINGS, 2014, : 72 - 75
  • [33] Pigmented skin lesion segmentation based on sparse texture representations
    Martinez, Cesar E.
    Albornoz, Enrique M.
    12TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS, 2017, 10160
  • [34] Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering
    Keuper, Margret
    Tang, Siyu
    Andres, Bjoern
    Brox, Thomas
    Schiele, Bernt
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 42 (01) : 140 - 153
  • [35] Multitask possibilistic and fuzzy co-clustering algorithm for clustering data with multisource features
    Jiaqi Ren
    Youlong Yang
    Neural Computing and Applications, 2020, 32 : 4785 - 4804
  • [36] Multitask fuzzy Bregman co-clustering approach for clustering data with multisource features
    Sokhandan, Alireza
    Adibi, Peyman
    Sajadi, Mohammadreza
    NEUROCOMPUTING, 2017, 247 : 102 - 114
  • [37] Spectral co-clustering documents and words using fuzzy K-harmonic means
    Na Liu
    Fei Chen
    Mingyu Lu
    International Journal of Machine Learning and Cybernetics, 2013, 4 : 75 - 83
  • [38] Spectral co-clustering documents and words using fuzzy K-harmonic means
    Liu, Na
    Chen, Fei
    Lu, Mingyu
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2013, 4 (01) : 75 - 83
  • [39] A Comparative Study on Utilization of Semantic Information in Fuzzy Co-clustering
    Takahata, Yusuke
    Honda, Katsuhiro
    Ubukata, Seiki
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13199 LNAI : 216 - 225
  • [40] A Fuzzy Co-clustering Interpretation of Probabilistic Latent Semantic Analysis
    Honda, Katsuhiro
    Goshima, Takafumi
    Ubukata, Seiki
    Notsu, Akira
    2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2016, : 718 - 723