Open AccessSpace-occupying lesions identification in mammary glands using a mixed computational strategy
Author(s) -
Sandra Vargas,
Miguel Vera,
Miguel Vera,
J Salazar-Torres,
Yoleidy Huérfano,
Oscar Valbuena,
E Gelvez-Almeida
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1414/1/012016
Subject(s) - thresholding , modality (human–computer interaction) , segmentation , artificial intelligence , breast cancer , magnetic resonance imaging , computer science , pattern recognition (psychology) , identification (biology) , population , image segmentation , noise (video) , computer vision , mammography , image processing , cancer , image quality , image (mathematics) , radiology , medicine , biology , botany , environmental health
The mammary pathology can manifest itself in multiple ways and originates space-occupying lesions. The breast cancer is a space-occupying lesion, which is highly prevalent, especially in women, and worldwide it is one of the leading causes of morbidity and mortality in this population. The main image modality for breast cancer detection is the magnetic resonance but this kind of image modality introduces several imperfections that affect the image quality. Some of these imperfections or problems are: inhomogeneity in the anatomical structures, riccian noise and artifacts. These problems make the analysis of the image information a real challenge. To address these problems, in this paper, we propose a computational technique able to extract a space-occupying lesion linked to breast cancer, present in magnetic resonance images. For this, the original image is processed with statistical-arithmetic filters and segmentation algorithms based on thresholding and multi-seed region growing techniques. The results, based on Dice score, show that the proposed technique is suitable for segmenting the breast cancer due high correlation between semi-automatic and manual segmentations. This technique can be useful in the detection, characterization and monitoring of this type of cancer and it can let to medical doctors to realize their work more efficiently.