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Robust information clustering incorporating spatial information for breast mass detection in digitized mammograms

Aize Cao, Qing Song, Xulei Yang
2008 Computer Vision and Image Understanding  
In this paper, we investigate a robust information clustering (RIC) algorithm incorporating spatial information for breast mass detection in digitized mammograms.  ...  The algorithm is robust in the sense that both peak and valley of image intensity histogram are estimated and the pixels corresponding to valley in the histogram are clustered adaptively to image content  ...  To gain robustness, we focus on the property of density estimation p(x i ) 2 p(X).  ... 
doi:10.1016/j.cviu.2007.07.004 fatcat:syexl4bgmvdg3d23od2qwil4ci

Breast Cancer Detection in Mammograms based on Clustering Techniques- A Survey

R. Ramani, S. Valarmathy, N. Suthanthira Vanitha
2013 International Journal of Computer Applications  
This paper is a survey of recent clustering techniques for detection of breast cancer. These fuzzy clustering algorithms have been widely studied and applied in a variety of application areas.  ...  Segmentation is very valuable for doctor and radiologists to analysis the data in the mammogram. Accuracy rate of breast cancer in mammogram depends on the image segmentation.  ...  system for early detection of breast cancer.  ... 
doi:10.5120/10123-4885 fatcat:jd5fbiwtinggrijhghvcytv4my

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Chiranji Lal Chowdhary, Mohit Mittal, Kumaresan P., P. A. Pattanaik, Zbigniew Marszalek
2020 Sensors  
For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied.  ...  More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women.  ...  These are also detected and extracted for further outlier mining.  ... 
doi:10.3390/s20143903 pmid:32668793 fatcat:o3s3sgwnsfhy5agtukhkbuk5km

Fully Automated Breast Density Segmentation and Classification Using Deep Learning

Nasibeh Saffari, Hatem A. Rashwan, Mohamed Abdel-Nasser, Vivek Kumar Singh, Meritxell Arenas, Eleni Mangina, Blas Herrera, Domenec Puig
2020 Diagnostics  
Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated.  ...  Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms' fatty tissue background.  ...  Also, this work is partly supported by the Ministry of Science and Innovation (Spain) through project PID2019-105789RB-I00. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/diagnostics10110988 pmid:33238512 fatcat:gx4ozlqgunenlnctxduu6zbjze

A Review on Automatic Mammographic Density and Parenchymal Segmentation

Wenda He, Arne Juette, Erika R. E. Denton, Arnau Oliver, Robert Martí, Reyer Zwiggelaar
2015 International Journal of Breast Cancer  
Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer.  ...  Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment.  ...  Acknowledgments This work was funded by the Biomedical Research Units (BRU)/National Institute for Social Care and Health Research International Journal of Breast Cancer 25 (NISCHR).  ... 
doi:10.1155/2015/276217 pmid:26171249 pmcid:PMC4481086 fatcat:md5d5canabchpmen2nimvtn5z4

Classification of malignant and benign masses based on hybrid ART2LDA approach

L. Hadjiiski, B. Sahiner, Heang-Ping Chan, N. Petrick, M. Helvie
1999 IEEE Transactions on Medical Imaging  
A new type of classifier combining an unsupervised and a supervised model was designed and applied to classification of malignant and benign masses on mammograms.  ...  The unsupervised model was based on an adaptive resonance theory (ART2) network which clustered the masses into a number of separate classes.  ...  ., for providing the LABROC1 and CLABROC programs.  ... 
doi:10.1109/42.819327 pmid:10695530 fatcat:j2icbnvjxzh43odbezbpymka7e

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Jesus A. Basurto-Hurtado, Irving A. Cruz-Albarran, Manuel Toledano-Ayala, Mario Alberto Ibarra-Manzano, Luis A. Morales-Hernandez, Carlos A. Perez-Ramirez
2022 Cancers  
This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing  ...  Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/cancers14143442 pmid:35884503 pmcid:PMC9322973 fatcat:n7ufym6pn5hbni2v2rp42xvtga

Breast Cancer Detection via Mammographic Images : A Survey

Mary Walowe Mwadulo, Raphael Angulu, Stephen Makau Mutua
2020 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
This paper analyzes mammographic detection of breast cancer by providing an explanation on development and classification of Breast Cancer, Image representation models for breast tumor, mammography technologies  ...  cancer databases, and a review of mammographic breast cancer detection studies are presented.  ...  To detect microcalcification Boulehmi and Hourami [46] proposed a technique for microcalcification detection based on generalized Gaussian density.  ... 
doi:10.32628/cseit20633 fatcat:5ivxt3yt4vczlge5v5277m76bu

Segmentation and Feature Extraction in Medical Imaging: A Systematic Review

Chiranji Lal Chowdhary, D.P. Acharjya
2020 Procedia Computer Science  
In fact a lot of medical imaging techniques are available but authors restrict survey to tumor detection through mammograms or magnetic resonance imaging.  ...  In fact a lot of medical imaging techniques are available but authors restrict survey to tumor detection through mammograms or magnetic resonance imaging.  ...  Mammography is useful for early detection of breast cancer.  ... 
doi:10.1016/j.procs.2020.03.179 fatcat:5oarv6vyjfdsjpoectpeiie2fe

Front Matter: Volume 9414

2015 Medical Imaging 2015: Computer-Aided Diagnosis  
The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon.  ...  Some conference presentations may not be available for publication. The papers published in these proceedings reflect the work and thoughts of the authors and are published herein as submitted.  ...  in screening mammograms [9414-18] 9414 0K Comparison of computer-aided detection of clustered microcalcifications in digital mammography and digital breast tomosynthesis [9414-19] 9414 0L Initial  ... 
doi:10.1117/12.2194210 dblp:conf/micad/X15 fatcat:wzirgkwiwbgvba6jlaucm3azzq

Breast compression parameters among women imaged with full field digital mammography and breast tomosynthesis in BreastScreen Norway

N. Moshina, Solveig Hofvind, Gunvor Waade, Åsne Holen, Berit Hanestad, Sofie Sebuødegård, K. Pedersen, Elizabeth A. Krupinski
2018 14th International Workshop on Breast Imaging (IWBI 2018)  
and BMI) and image-based parameters (BI-RADS density, volumetric breast density (VBD) and detectability).  ...  We applied mixed-effects linear models to estimate the association of age, fibroglandular volume, breast volume and volumetric breast density over time including data on BMI and HT.  ...  The aim of this study is to exploit to use of CNNs for mass detection in mammograms using pre-trained networks.  ... 
doi:10.1117/12.2317918 dblp:conf/iwbi/WadeHHSMPH18 fatcat:gyksxd5b2jf4jpntucqs5zjc5i

Review of Laser Raman Spectroscopy for Surgical Breast Cancer Detection: Stochastic Backpropagation Neural Networks

Ragini Kothari, Yuman Fong, Michael C. Storrie-Lombardi
2020 Sensors  
This paper discusses significant advancements in the use of LRS in surgical breast cancer diagnosis, with an emphasis on statistical and machine learning strategies employed for precise, transparent and  ...  Stochastic backpropagation artificial neural networks inherently provide both pieces of information for each and every tissue site examined by LRS.  ...  Schmolze, and D. Mena for insightful discussions. We also thank two anonymous reviewers for substantially improving this manuscript.  ... 
doi:10.3390/s20216260 pmid:33147836 pmcid:PMC7663399 fatcat:of7z5lsjufehvglsh5od3p2qoy

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

Songfeng Li, Jun Wei, Heang-Ping Chan, Mark A Helvie, Marilyn A Roubidoux, Yao Lu, Chuan Zhou, Lubomir M Hadjiiski, Ravi K Samala
2018 Physics in Medicine and Biology  
In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DM).  ...  A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method.  ...  The content of this paper does not necessarily reflect the position of the government and no official endorsement of any equipment and product of any companies mentioned should be inferred.  ... 
doi:10.1088/1361-6560/aa9f87 pmid:29210358 pmcid:PMC5784848 fatcat:f6gbhu7qnfgs3iiexch2aaa2oy

Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Martin C Nwadiugwu
2020 Bioinformatics and Biology Insights  
Result of the review showed that unlike Denclue and Fuzzy-C which are more efficient in handling noisy data, BIRCH can handle data set with outliers and have a better time complexity.  ...  Denclue, Fuzzy-C, and Balanced Iterative and Clustering using Hierarchies (BIRCH) were the 3 gene-based clustering algorithms selected.  ...  algorithmic technique that has been applied in many areas of biology and medicine such as profiling the mycobacterium tuberculosis, detecting the size and stages of breast cancer, discovery of subtypes  ... 
doi:10.1177/1177932220909851 pmid:32284672 pmcid:PMC7133071 fatcat:swnhlkpqwrhm5o4rp5qmxmkifu

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Muhammad Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, Dilovan Asaad Zebari
2020 Journal of Applied Science and Technology Trends  
The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities.  ...  This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches.  ...  Despite this, an MRI can detect the presence of breast cancer in patients with sonograms, mammograms, and physical exams that are not final.  ... 
doi:10.38094/jastt1328 fatcat:hprvicqbvbeb5fbsu6ozrdntci
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