Breast tumor segmentation in digital mammograms using spiculated regions

被引:11
|
作者
Pezeshki, Hamed [1 ]
机构
[1] Islamic Azad Univ, Dept Comp Engn, Miyaneh Branch, Miyaneh, Iran
关键词
Breast Cancer; Mammography; Image segmentation; Mass; Spiculated; Image thresholding; MASS SEGMENTATION; VISUAL ENHANCEMENT; LEVEL SET; CLASSIFICATION; IMAGES; DENSITY; BENIGN; MODEL; NET;
D O I
10.1016/j.bspc.2022.103652
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Mammogram image segmentation is the process of partitioning mammograms into meaningful and separate areas. However, during the segmentation process, masses are extracted and the spiculated regions of a mass, which contain significant characteristics of the mass margins, are omitted. The present research introduces a new method for segmentation of tumor mammograms that extracts the spiculated regions and the mass core. Generally, the pixels of a spiculated region are located along a line and the pixels of the mass core regions are similar. The proposed method extracts these regions using the differences between a pixel and its adjacent pixels. The proposed method uses three thresholds to delete redundant pixels from the spiculated regions and the mass core. These regions then are merged to form the segmented tumor. The results show that the respective mean of the Dice and Jaccard coefficients for the suggested segmentation method, respectively, are 0.9309 and 0.9024 for MIAS and 0.9557 and 0.9132 for DDSM. Quantitative analysis of the results confirms that the suggested segmentation method is comparable to other techniques and extracts the segmentation of tumor accurately.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] Multilevel learning-based segmentation of ill-defined and spiculated masses in mammograms
    Tao, Yimo
    Lo, Shih-Chung B.
    Freedman, Matthew T.
    Makariou, Erini
    Xuan, Jianhua
    MEDICAL PHYSICS, 2010, 37 (11) : 5993 - 6002
  • [22] Segmentation of Micro Calcification Clusters in Digital Mammograms Using UIQI
    Murty, Patnala S. R. Chandra
    Sudheer, T.
    Reddy, E. Sreenivasa
    ADVANCES IN DIGITAL IMAGE PROCESSING AND INFORMATION TECHNOLOGY, 2011, 205 : 165 - 172
  • [23] Detection and Segmentation of Microcalcifications in Digital Mammograms using Multifractal Analysis
    Sahli, Ines Slim
    Bettaieb, Hanen Akkari
    Ben Abdallah, Asma
    Bhouri, Imen
    Bedoui, Mohamed Hedi
    5TH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, THEORY, TOOLS AND APPLICATIONS 2015, 2015, : 180 - 184
  • [24] Wavelet compression and segmentation of digital mammograms
    Lucier, Bradley J.
    Kallergi, Maria
    Qian, Wei
    DeVore, Ronald A.
    Clark, Robert A.
    Saff, Edward B.
    Clarke, Laurence P.
    Journal of Digital Imaging, 1994, 7 (01)
  • [25] Segmentation of suspicious densities in digital mammograms
    te Brake, GM
    Karssemeijer, N
    MEDICAL PHYSICS, 2001, 28 (02) : 259 - 266
  • [26] Segmentation for the enhancement of microcalcifications in digital mammograms
    Milosevic, Marina
    Jankovic, Dragan
    Peulic, Aleksandar
    TECHNOLOGY AND HEALTH CARE, 2014, 22 (05) : 701 - 715
  • [27] Breast Cancer Risk Analysis Based on a Novel Segmentation Framework for Digital Mammograms
    Chen, Xin
    Moschidis, Emmanouil
    Taylor, Chris
    Astley, Susan
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2014, PT I, 2014, 8673 : 536 - 543
  • [28] Identification of regions of interest in digital mammograms
    Singh, Sameer
    Al-Mansoori, Reem
    Journal of Intelligent Systems, 2000, 10 (02) : 183 - 210
  • [29] Segmentation of mammograms into distinct morphological texture regions
    Baeg, S
    Popov, AT
    Kamat, VG
    Batman, S
    Sivakumar, K
    Kehtarnavaz, N
    Dougherty, ER
    Shah, RB
    11TH IEEE SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, PROCEEDINGS, 1998, : 20 - 25
  • [30] Segmentation of Regions of Interest in Mammograms in a Topographic Approach
    Hong, Byung-Woo
    Sohn, Bong-Soo
    IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2010, 14 (01): : 129 - 139