Structure-Based Evaluation Methodology for Curvilinear Structure Detection Algorithms

被引:0
|
作者
Jiang, Xiaoyi [1 ]
Lambers, Martin [2 ]
Bunke, Horst [3 ]
机构
[1] Univ Munster, Dept Comp Sci, D-4400 Munster, Germany
[2] Univ Siegen, Comp Graph Grp, D-57068 Siegen, Germany
[3] Univ Bern, Inst Comp Sci & Appl Math, CH-3012 Bern, Switzerland
来源
GRAPH-BASED REPRESENTATIONS IN PATTERN RECOGNITION | 2011年 / 6658卷
关键词
VESSEL SEGMENTATION; IMAGES;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Curvilinear structures are useful features, particularly in medical image analysis. Typically, a pixel-wise comparison with manually specified ground truth is used for performance evaluation. In this paper we propose a novel structure-based methodology for evaluating the performance of curvilinear structure detection algorithms. We consider the two aspects of performance, namely detection rate and detection accuracy, separately. This is in contrast to their mixed handling in earlier approaches that typically produces biased impression of detection quality. The proposed performance measures provide a more informative and precise performance characterization. A series of experiments in the context of retinal vessel detection are presented to demonstrate the advantages of our approach.
引用
收藏
页码:305 / 314
页数:10
相关论文
共 50 条
  • [1] Structure-based evaluation methodology for curvilinear structure detection algorithms
    Jiang X.
    Lambers M.
    Bunke H.
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 6658 LNCS : 305 - 314
  • [2] Supervised evaluation methodology for curvilinear structure detection algorithms
    Jiang, XY
    Mojon, D
    16TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL I, PROCEEDINGS, 2002, : 103 - 106
  • [3] Structure-Based Algorithms for Microvessel Classification
    Smith, Amy F.
    Secomb, Timothy W.
    Pries, Axel R.
    Smith, Nicolas P.
    Shipley, Rebecca J.
    MICROCIRCULATION, 2015, 22 (02) : 99 - 108
  • [4] Structural performance evaluation of curvilinear structure detection algorithms with application to retinal vessel segmentation
    Jiang, Xiaoyi
    Lambers, Martin
    Bunke, Horst
    PATTERN RECOGNITION LETTERS, 2012, 33 (15) : 2048 - 2056
  • [5] Structure-based ontology evaluation
    Huang Ning
    Diao Shihan
    ICEBE 2006: IEEE INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING, PROCEEDINGS, 2006, : 132 - +
  • [6] A Structure-Based Methodology for Analog Layout Generation
    Chen, Yu-Hsien
    Chi, Hao-Yu
    Song, Ling-Yen
    Liu, Chien-Nan Jimmy
    Chen, Hung-Ming
    2019 16TH INTERNATIONAL CONFERENCE ON SYNTHESIS, MODELING, ANALYSIS AND SIMULATION METHODS AND APPLICATIONS TO CIRCUIT DESIGN (SMACD 2019), 2019, : 33 - 36
  • [7] Hashing for Structure-Based Anomaly Detection
    Leveni, Filippo
    Magri, Luca
    Alippi, Cesare
    Boracchi, Giacomo
    IMAGE ANALYSIS AND PROCESSING, ICIAP 2023, PT II, 2023, 14234 : 25 - 36
  • [8] Image structure-based saliency detection
    Hong, Jing
    Chen, Yufei
    Liu, Xianhui
    Zhao, Weidong
    Jia, Ning
    Zhou, Qiangqiang
    JOURNAL OF ELECTRONIC IMAGING, 2017, 26 (04)
  • [9] Algorithms for structure-based grouping in XML-OLAP
    Kit, Chantola
    Amagasa, Toshiyuki
    Kitagawa, Hiroyuki
    INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS, 2009, 5 (02) : 122 - +
  • [10] General structure-based classification of Optimization Algorithms for an objective comparison
    Niccolai, Alessandro
    Gonano, Carlo Andrea
    Grimaccia, Francesco
    Mussetta, Marco
    Zich, Riccardo Enrico
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON ELECTROMAGNETICS IN ADVANCED APPLICATIONS (ICEAA), 2015, : 1580 - 1583