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Self-Organization Map Based Segmentation of Breast Cancer

A. Arokiyamary Delphina, M. Kamarasan, S. Sathiamoorthy
2018 Asian Journal of Engineering and Applied Technology  
This paper presents a segmentation method by utilizing SOM.  ...  Breast cancer is second major leading cause of cancer fatality in women.  ...  PROPOSED FRAMEWORK FOR SEGMENTATION OF BREAST CANCER IMAGES The following fig. 1 represents proposed framework for classification of breast cancer cell using Pre-processing step, Segmentation step.  ... 
doi:10.51983/ajeat-2018.7.2.1015 fatcat:nfprwqkl5bafbafjauycemoqzy

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Leila Belkhodja, Hamdadou Djamila
2019 International Journal of Interactive Multimedia and Artificial Intelligence  
Cardoso and the Breast Research Group from the INESC Porto (Universidade do Porto) for providing the INBREAST database and for useful discussions.  ...  breast cancer early.  ...  rate caused by breast cancer.  ... 
doi:10.9781/ijimai.2018.12.006 fatcat:3rj3iqldvrclzaow4qqg37alqq

Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism-Like Optimization Algorithm

S. S. Ittannavar, R. H. Havaldar, B. D. Parameshachari
2022 BioMed Research International  
This research proposes a new multiobjective optimization technique for segmenting the breast masses from the mammographic image.  ...  In recent times, breast mass is the most diagnostic sign for early detection of breast cancer, where the precise segmentation of masses is important to reduce the mortality rate.  ...  [23] developed a pectoral muscle and breast boundary segmentation algorithm for early recognition of breast cancer.  ... 
doi:10.1155/2022/8576768 pmid:35083334 pmcid:PMC8786533 fatcat:adwuqx2xmrhqrmapf63ilzka24

Accurate Segmentation of Nuclear Regions with Multi-Organ Histopathology Images Using Artificial Intelligence for Cancer Diagnosis in Personalized Medicine

Tahir Mahmood, Muhammad Owais, Kyoung Jun Noh, Hyo Sik Yoon, Ja Hyung Koo, Adnan Haider, Haseeb Sultan, Kang Ryoung Park
2021 Journal of Personalized Medicine  
It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer.  ...  Experiments were performed on two publicly available datasets: (1) The Cancer Genome Atlas (TCGA), and (2) Triple-Negative Breast Cancer (TNBC).  ...  George et al. proposed an automated nuclear segmentation method [8] in breast cancer histopathology images for diagnosis and prognosis of breast cancer.  ... 
doi:10.3390/jpm11060515 fatcat:sphsihbahnghbf2pwgkfhsl4uq

Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques

V. K. Reshma, Nancy Arya, Sayed Sayeed Ahmad, Ihab Wattar, Sreenivas Mekala, Shubham Joshi, Daniel Krah, Siddesh G.M.
2022 BioMed Research International  
Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer.  ...  In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with  ...  Acknowledgments No funding is available for this research work.  ... 
doi:10.1155/2022/8363850 pmid:35281604 pmcid:PMC8913119 fatcat:hc3lp5tbdndedgw5wn75sfbgfe

Comparative Study on Automated Cell Nuclei Segmentation Methods for Cytology Pleural Effusion Images

Khin Yadanar Win, Somsak Choomchuay, Kazuhiko Hamamoto, Manasanan Raveesunthornkiat
2018 Journal of Healthcare Engineering  
Automated cell nuclei segmentation is the most crucial step toward the implementation of a computer-aided diagnosis system for cancer cells.  ...  Therefore, this paper presents a comparative study of twelve nuclei segmentation methods for cytology pleural effusion images.  ...  University, ailand, for the insightful suggestions, including their cooperation for the dataset and ground truth segmentation.  ... 
doi:10.1155/2018/9240389 fatcat:3lgiafvcxneehhfhckp5m4ts7y

Accurate detection of aneuploidies in array CGH and gene expression microarray data

C. L. Myers, M. J. Dunham, S. Y. Kung, O. G. Troyanskaya
2004 Bioinformatics  
ACKNOWLEDGEMENTS We would like to thank Peter Kasson, Mitchell Garber, Kai Li, Kara Dolinski and David Botstein for valuable discussions and help in analyzing biological results.  ...  breast cancer.  ...  The input-output relation for each of the filters is given on the left. y[n] is the output as a function of x[n] where n refers to gene index on the chromosome and N is the window size of each filter.  ... 
doi:10.1093/bioinformatics/bth440 pmid:15284100 fatcat:ugdg3d6cknffzodlcon5zjyj2y

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Fuyong Xing, Lin Yang
2016 IEEE Reviews in Biomedical Engineering  
In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.  ...  Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information.  ...  , and 58 breast cancer cell images.  ... 
doi:10.1109/rbme.2016.2515127 pmid:26742143 pmcid:PMC5233461 fatcat:hx5ldvsppvgzxk6rdiok7siyvi

A deep learning pipeline for breast cancer ki-67 proliferation index scoring [article]

Khaled Benaggoune, Zeina Al Masry, Jian Ma, Christine Devalland, L.H Mouss, Noureddine Zerhouni
2022 arXiv   pre-print
The Ki-67 proliferation index is an essential biomarker that helps pathologists to diagnose and select appropriate treatments.  ...  First, semantic segmentation is performed by combining the Squeez and Excitation Resnet and Unet algorithms to extract nuclei from the background.  ...  The authors would like to thank Etienne Martin, a biomedical engineer, for his valuable help.  ... 
arXiv:2203.07452v1 fatcat:xqvyabsajrchjlm5fh3sseyuyq

Inhomogeneous Image Segmentation using Hybrid Active Contours Model with Application to Breast Tumor Detection

Asim Niaz, Asif Aziz Memon, Kaynat Rana, Aditi Joshi, Shafiullah Soomro, Jin Seok KANG, Kwang Nam Choi
2020 IEEE Access  
Thus, the proposed method offers a powerful tool for early breast cancer detection and consequent mitigation of breast cancer impacts.  ...  CONCLUSION Accurate breast mass identification is critical for early breast cancer detection.  ...  He is currently a Professor with the School of Computer Science and Engineering, Chung-Ang University.  ... 
doi:10.1109/access.2020.3029333 fatcat:pe2rkxkhdjeuxmbfwqhvby4ojq

A review of machine learning approaches, challenges and prospects for computational tumor pathology [article]

Liangrui Pan, Zhichao Feng, Shaoliang Peng
2022 arXiv   pre-print
in breast, colon, prostate, lung, and various tumour disease scenarios.  ...  The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles,  ...  Although CNN-based U-Net has achieved good results on histological image segmentation tasks, some researchers prefer to use DL to develop algorithms for breast cancer cell nucleus segmentation [65]  ... 
arXiv:2206.01728v1 fatcat:g7r7fsw2bzafpkkyg6hpzjyt5e

Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms

Wenqing Sun, Bin Zheng, Fleming Lure, Teresa Wu, Jianying Zhang, Benjamin Y. Wang, Edward C. Saltzstein, Wei Qian
2014 Computerized Medical Imaging and Graphics  
A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed  ...  Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers.  ...  For better illustration, the segmentation example is implemented on the mammogram with malignant mass tissue (a) original image (b) enhanced image (c) segmentation result C.  ... 
doi:10.1016/j.compmedimag.2014.03.001 pmid:24725671 fatcat:4vojj25zfffs3etxild6v5klzy

An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle

Yan Liu, H. D. Cheng, Jianhua Huang, Yingtao Zhang, Xianglong Tang
2012 Journal of digital imaging  
In this paper, a novel lesion segmentation within breast ultrasound (BUS) image based on the cellular automata principle is proposed.  ...  The experimental results demonstrate that the proposed method can handle BUS images with blurry boundaries and low contrast well and can segment breast lesions accurately and effectively.  ...  Therefore, developing a method insensitive to initial condition and robust to noise is useful for solving a breast lesion segmentation task.  ... 
doi:10.1007/s10278-011-9450-6 pmid:22237810 pmcid:PMC3447089 fatcat:g77l4pfvmjczxcomvxp7d37dvy

Automatic breast ultrasound image segmentation: A survey

Min Xian, Yingtao Zhang, H.D. Cheng, Fei Xu, Boyu Zhang, Jianrui Ding
2018 Pattern Recognition  
In clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging and essential for cancer diagnosis and treatment planning.  ...  Breast cancer is one of the leading causes of cancer death among women worldwide.  ...  Cell competition: Chen et al. [138] proposed a cell-competition approach for BUS image segmentation.  ... 
doi:10.1016/j.patcog.2018.02.012 fatcat:mm6vwa7c3nhmvmpnmiegz6edue

Joint Inference of Clonal Structure using Single-cell DNA-Seq and RNA-Seq data [article]

Xiangqi Bai, Lin Wan, Charlie Xia
2020 bioRxiv   pre-print
We also applied CCNMF to the paired scRNA and scDNA data from a triple-negative breast cancer xenograft, resolved its underlying clonal structures, and identified differential genes between cell clusters  ...  We benchmarked CCNMF using both simulated and real cell mixture derived datasets and fully demonstrated its robustness and accuracy.  ...  We applied CCNMF to characterize a gastric cancer cell line, an ovarian cancer cell mixture and a patient-derived triple negative breast cancer xenograft.  ... 
doi:10.1101/2020.02.04.934455 fatcat:taghxbtpavcsvdiyyjrtcb2c3a
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