Breast Mass Segmentation in Mammographic Images by an Effective Region Growing Algorithm

2008 ◽  
pp. 948-957 ◽  
Author(s):  
Arianna Mencattini ◽  
Giulia Rabottino ◽  
Marcello Salmeri ◽  
Roberto Lojacono ◽  
Emanuele Colini
Keyword(s):  
Region Growing ◽  
Breast Mass ◽  
Mammographic Images ◽  
Mass Segmentation

scholarly journals Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention

2021 ◽  
Author(s):  
Yutong Yan ◽  
Pierre-Henri Conze ◽  
Gwenolé Quellec ◽  
Mathieu Lamard ◽  
Beatrice Cochener ◽  
...  
Keyword(s):  
Breast Mass ◽  
Two Stage ◽  
Multi Scale ◽  
Mass Segmentation ◽  
User Intervention

Adaptive channel and multiscale spatial context network for breast mass segmentation in full-field mammograms

2021 ◽  
Author(s):  
Wenwei Zhao ◽  
Meng Lou ◽  
Yunliang Qi ◽  
Yiming Wang ◽  
Chunbo Xu ◽  
...  
Keyword(s):  
Spatial Context ◽  
Breast Mass ◽  
Full Field ◽  
Mass Segmentation

scholarly journals Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network

2020 ◽  
Vol 61 ◽  
pp. 102027
Author(s):  
Michal Byra ◽  
Piotr Jarosik ◽  
Aleksandra Szubert ◽  
Michael Galperin ◽  
Haydee Ojeda-Fournier ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Breast Mass ◽  
Mass Segmentation

Optimizing Breast Mass Segmentation Algorithms with Generative Adversarial Nets

2019 ◽  
pp. 608-620
Author(s):  
Qi Yin ◽  
Haiwei Pan ◽  
Bin Yang ◽  
Xiaofei Bian ◽  
Chunling Chen
Keyword(s):  
Breast Mass ◽  
Segmentation Algorithms ◽  
Mass Segmentation

Level Set Breast Mass Segmentation in Contrast-Enhanced and Non-Contrast-Enhanced Breast CT

2012 ◽  
pp. 697-704 ◽  
Author(s):  
Hsien-Chi Kuo ◽  
Maryellen L. Giger ◽  
Ingrid Reiser ◽  
John M. Boone ◽  
Karen K. Lindfors ◽  
...  
Keyword(s):  
Level Set ◽  
Breast Mass ◽  
Breast Ct ◽  
Contrast Enhanced ◽  
Mass Segmentation

SEGMENTATION OF MASS IN MAMMOGRAMS USING A NOVEL INTELLIGENT ALGORITHM

2006 ◽  
Vol 20 (02) ◽  
pp. 255-270 ◽  
Author(s):  
WEIDONG XU ◽  
SHUNREN XIA ◽  
HUILONG DUAN ◽  
MIN XIAO
Keyword(s):  
Region Growing ◽  
Experimental Result ◽  
Fatty Tissue ◽  
Intelligent Algorithm ◽  
Matching Model ◽  
Canny Edge Detection ◽  
Iterative Thresholding ◽  
Canny Edge ◽  
Mass Segmentation ◽  
Suspicious Area

In order to improve the performance of mass segmentation on mammograms, an intelligent algorithm is proposed in this paper. It establishes two mass models to characterize the various masses, and the ones in the denser tissue are represented with Model I, while the ones in the fatty tissue are represented with Model II. Then, it uses iterative thresholding to extract the suspicious area, as well as the rough regions of those masses matching Model II, and applies a DWT-based technique to locate those masses matching Model I, which are hidden in the high gray-level intensity and contrast area. A region growing process restricted by Canny edge detection is subsequently used to segment the rough regions of those masses matching Model I, and finally snakes are carried out to find all the mass regions roughly extracted above. Thirty patient cases with 60 mammograms and 107 masses were used for evaluation, and the experimental result has demonstrated the algorithm's better performance over the conventional methods.

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