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
  • [31] Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network
    Soulami, Khaoula Belhaj
    Kaabouch, Naima
    Saidi, Mohamed Nabil
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 76
  • [32] Tumor detection in digital mammograms
    Banerjee, A
    Chellappa, R
    2000 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL III, PROCEEDINGS, 2000, : 432 - 435
  • [33] Multiscale tumor detection and segmentation in mammograms
    Zhang, XP
    2002 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, PROCEEDINGS, 2002, : 213 - 216
  • [34] Study on Breast Mass Segmentation in Mammograms
    Gu, Shenghua
    Ji, Yao
    Chen, Yunjie
    Wang, Jin
    Kim, Jeong-Uk
    2015 3RD INTERNATIONAL CONFERENCE ON COMPUTER, INFORMATION AND APPLICATION, 2015, : 22 - 25
  • [35] Segmentation of Tumor in Digital Mammograms Using Wavelet Transform Modulus Maxima on a Low Cost Parallel Computing System
    Sulaiman, Hanifah
    Ibrahim, Arsmah
    Alias, Norma
    5TH KUALA LUMPUR INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING 2011 (BIOMED 2011), 2011, 35 : 720 - +
  • [36] Pectoral Muscle Segmentation from Digital Mammograms Using a Transformative Approach
    Mahaveera, Dhanush Jain
    Gujar, Shubham Arun
    Cen, Steven Yong
    Lei, Xiaomeng
    Hwang, Darryl H.
    Varghese, Bino A.
    2023 19TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS, SIPAIM, 2023,
  • [37] Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM
    Servulo de Oliveira, Fernando Soares
    de Carvalho Filho, Antonio Oseas
    Silva, Aristofanes Correa
    de Paiva, Anselmo Cardoso
    Gattass, Marcelo
    COMPUTERS IN BIOLOGY AND MEDICINE, 2015, 57 : 42 - 53
  • [38] Automatic Tumor Segmentation in Digital Breast Tomosynthesis Using U-Net
    Qasem, A.
    Qin, G.
    Wang, J.
    Zhou, Z.
    MEDICAL PHYSICS, 2020, 47 (06) : E584 - E584
  • [39] An approach to using a generalized breast model to segment digital mammograms
    Bakic, P
    Brzakovic, D
    Brzakovic, P
    Zhu, Z
    11TH IEEE SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, PROCEEDINGS, 1998, : 84 - 89
  • [40] A fractal approach to the segmentation of microcalcifications in digital mammograms
    INSERM U66, Institut Gustave Roussy, 39 rue Camille Desmoulins, 94805 Villejuif Cedex, France
    MED. PHYS., 4 (381-390):