Breast cancer continues to be one of the main causes of death among women. Various studies have confirmed that the early detection of sub-clinical cancers may improve the prognosis. X-ray mammography in this case is the best diagnostic technique. It’s based on the interaction of a cone beam X-ray with the mole tissue. The projection image obtained can be analyzed qualitatively by the radiologists. But, an automatic treatment and quantitative analysis of this kind of images is suitable. For this reason several studies are conducted to develop tools to help with diagnosis of this disease (CAD: Computer-Assisted Diagnosis). We propose in this paper a new method to segment mammographic images based partly on a pyramidal architecture. The original image is fragmented (quadtree) initially to homogeneous regions. Each region is then associated with a peak of graph. It gathers data within homogeneous groups named regions classes’ c, then we use HCA (Hierarchical classification ascendant) and k-means to find the optimal partition for the largest possible value of c at the initial stage. This technique gives good results, and allows calculating morphological parameters of the breast cancer.
| Published in | American Journal of Nano Research and Applications (Volume 3, Issue 4) |
| DOI | 10.11648/j.nano.20150304.12 |
| Page(s) | 78-81 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
Mamography, Image Segmentation, K-means, Irregular Pyramid, HCA
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APA Style
Mohammed Rmili, Abdellatif Siwane, Fatiha Adnani, Fatiha Essodegui, Abdelmajid El Moutaouakkil. (2015). A New Approach to Image Segmentation Mammogram. American Journal of Nano Research and Applications, 3(4), 78-81. https://doi.org/10.11648/j.nano.20150304.12
ACS Style
Mohammed Rmili; Abdellatif Siwane; Fatiha Adnani; Fatiha Essodegui; Abdelmajid El Moutaouakkil. A New Approach to Image Segmentation Mammogram. Am. J. Nano Res. Appl. 2015, 3(4), 78-81. doi: 10.11648/j.nano.20150304.12
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Download@article{10.11648/j.nano.20150304.12,
author = {Mohammed Rmili and Abdellatif Siwane and Fatiha Adnani and Fatiha Essodegui and Abdelmajid El Moutaouakkil},
title = {A New Approach to Image Segmentation Mammogram},
journal = {American Journal of Nano Research and Applications},
volume = {3},
number = {4},
pages = {78-81},
doi = {10.11648/j.nano.20150304.12},
url = {https://doi.org/10.11648/j.nano.20150304.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.nano.20150304.12},
abstract = {Breast cancer continues to be one of the main causes of death among women. Various studies have confirmed that the early detection of sub-clinical cancers may improve the prognosis. X-ray mammography in this case is the best diagnostic technique. It’s based on the interaction of a cone beam X-ray with the mole tissue. The projection image obtained can be analyzed qualitatively by the radiologists. But, an automatic treatment and quantitative analysis of this kind of images is suitable. For this reason several studies are conducted to develop tools to help with diagnosis of this disease (CAD: Computer-Assisted Diagnosis). We propose in this paper a new method to segment mammographic images based partly on a pyramidal architecture. The original image is fragmented (quadtree) initially to homogeneous regions. Each region is then associated with a peak of graph. It gathers data within homogeneous groups named regions classes’ c, then we use HCA (Hierarchical classification ascendant) and k-means to find the optimal partition for the largest possible value of c at the initial stage. This technique gives good results, and allows calculating morphological parameters of the breast cancer.},
year = {2015}
}
TY - JOUR T1 - A New Approach to Image Segmentation Mammogram AU - Mohammed Rmili AU - Abdellatif Siwane AU - Fatiha Adnani AU - Fatiha Essodegui AU - Abdelmajid El Moutaouakkil Y1 - 2015/07/17 PY - 2015 N1 - https://doi.org/10.11648/j.nano.20150304.12 DO - 10.11648/j.nano.20150304.12 T2 - American Journal of Nano Research and Applications JF - American Journal of Nano Research and Applications JO - American Journal of Nano Research and Applications SP - 78 EP - 81 PB - Science Publishing Group SN - 2575-3738 UR - https://doi.org/10.11648/j.nano.20150304.12 AB - Breast cancer continues to be one of the main causes of death among women. Various studies have confirmed that the early detection of sub-clinical cancers may improve the prognosis. X-ray mammography in this case is the best diagnostic technique. It’s based on the interaction of a cone beam X-ray with the mole tissue. The projection image obtained can be analyzed qualitatively by the radiologists. But, an automatic treatment and quantitative analysis of this kind of images is suitable. For this reason several studies are conducted to develop tools to help with diagnosis of this disease (CAD: Computer-Assisted Diagnosis). We propose in this paper a new method to segment mammographic images based partly on a pyramidal architecture. The original image is fragmented (quadtree) initially to homogeneous regions. Each region is then associated with a peak of graph. It gathers data within homogeneous groups named regions classes’ c, then we use HCA (Hierarchical classification ascendant) and k-means to find the optimal partition for the largest possible value of c at the initial stage. This technique gives good results, and allows calculating morphological parameters of the breast cancer. VL - 3 IS - 4 ER -
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