Detection and Segmentation of Masses in Mammograms by The Rule Based Elimination Approach

被引:0
|
作者
Ture, Hayati [1 ]
Kayikcioglu, Temel [1 ]
机构
[1] Karadeniz Tech Univ, Elekt & Elekt Muhendisligi Bolumu, Trabzon, Turkey
关键词
Mammogram; mass; salient dense region; lifetime; rule-based-elimination;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
In this study, a method was proposed that eliminated the non-suspicious salient regions for the detection and segmentation of masses in mammograms. Since suspicious regions are generally salient dense regions, the method firstly extracts the maximum regions of interest (ROIs) that have the optimum lifetime. Subsequently, these ROIs are segmented with the rule based elimination using morphological and intensity properties. The texture features taken from the suspicious regions are classified by Rus Boost method for detection of masses. The developed method has been tested on all mammograms, which includes mass, taken from the MIAS database. Experimental results demonstrate that the method achieves a satisfactory performance during the detection and segmentation of suspicious regions.
引用
收藏
页数:4
相关论文
共 50 条
  • [1] Detection of circumscribed masses in mammograms using morphological segmentation
    Herredsvela, J
    Gulsrud, TO
    Engan, K
    Medical Imaging 2005: Image Processing, Pt 1-3, 2005, 5747 : 902 - 913
  • [2] Segmentation of the Breast Region in Digital Mammograms and Detection of Masses
    Sahakyan, Armen
    Sarukhanyan, Hakop
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2012, 3 (02) : 102 - 105
  • [3] Multilevel segmentation for automatic detection of malignant masses in digital mammograms based on threshold comparison
    Rodriguez-Esparza, Erick
    Zanella-Calzada, Laura A.
    Oliva, Diego
    Hinojosa, Salvador
    Perez-Cisneros, Marco
    2019 IEEE LATIN AMERICAN CONFERENCE ON COMPUTATIONAL INTELLIGENCE (LA-CCI), 2019, : 166 - 171
  • [4] Morphological operation and scaled Reyni entropy based approach for masses detection in mammograms
    Vikhe, P. S.
    Thool, V. R.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2018, 77 (18) : 23777 - 23802
  • [5] Detection of masses on mammograms using advanced segmentation techniques and an HMOE classifier
    Li, H
    Lo, SCB
    Wang, Y
    Hayes, W
    Freedman, MT
    Mun, SK
    DIGITAL MAMMOGRAPHY '96, 1996, 1119 : 397 - 400
  • [6] Rule Based Elimination of Regions of Interest in Pectoral Muscle on Mammograms
    Ture, Hayati
    Kayikcioglu, Temel
    2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU), 2016, : 1905 - 1908
  • [7] Morphological operation and scaled Réyni entropy based approach for masses detection in mammograms
    P. S. Vikhe
    V. R. Thool
    Multimedia Tools and Applications, 2018, 77 : 23777 - 23802
  • [8] Automated detection of masses in digital mammograms based on pyramid
    Wang, He
    Huang, Lin-Lin
    Zhao, Xiao-Jie
    2007 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION, VOLS 1-4, PROCEEDINGS, 2007, : 183 - 187
  • [9] Enhanced multi-level thresholding segmentation and rank based region selection for detection of masses in mammograms
    Dominguez, Alfonso Rojas
    Nandi, Asoke K.
    2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL I, PTS 1-3, PROCEEDINGS, 2007, : 449 - 452
  • [10] Detection of masses in mammograms using enhanced multilevel-thresholding segmentation and region selection based on rank
    Dominguez, A. Rojas
    Nandi, A. K.
    PROCEEDINGS OF THE FIFTH IASTED INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING, 2007, : 370 - 375