Filters








52 Hits in 5.6 sec

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer

M. M. Mehdy, P. Y. Ng, E. F. Shair, N. I. Md Saleh, C. Gomes
2017 Computational and Mathematical Methods in Medicine  
Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection.  ...  Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer.  ...  Acknowledgments This study has been supported by the Departments of Computer and Communication Engineering, Electrical and Electronics Engineering and Chemical and Environmental Engineering at Universiti  ... 
doi:10.1155/2017/2610628 pmid:28473865 pmcid:PMC5394406 fatcat:25zbqzges5ahjh6dmswxomxp6q

Diabetic retinopathy through retinal image analysis: A review

Y. Madhu Sudhana Reddy, R. S. Ernest Ravindran, K. Hari Kishore
2017 International Journal of Engineering & Technology  
The major approaches in DR are categorized into four classes namely Preprocessing, Optic Disk Detection, Blood Vessel Extraction and supervised classification.  ...  The optic disk, blood vessels and exudates gives more analytical details about the retinal image, segmentation of those features are very important.  ...  Yao and Chen [73] uses a 2-D Gaussian matched filter for retinal vessel enhancement and then a simplified pulse coupled neural network is employed to segment the blood vessels by firing neighborhood  ... 
doi:10.14419/ijet.v7i1.5.9072 fatcat:xzhr5aw4kbb5bbg43rmqbr2fbe

Medical Image Colorization for Better Visualization and Segmentation [chapter]

Muhammad Usman Ghani Khan, Yoshihiko Gotoh, Nudrat Nida
2017 Communications in Computer and Information Science  
The proposed framework starts with preprocessing to remove noise and improve edge information. Then colour information is embedded to each pixel of a subject image.  ...  Medical images contain precious anatomical information for clinical procedures. Improved understanding of medical modality may contribute significantly in arena of medical image analysis.  ...  In the experiment, a PCNN (pulse coupled neural network) and a thresholding are used for segmentation of colorized medical images.  ... 
doi:10.1007/978-3-319-60964-5_50 fatcat:h5uoajazcrgd7m5xopat6i62ui

Medical image analysis with artificial neural networks

J. Jiang, P. Trundle, J. Ren
2010 Computerized Medical Imaging and Graphics  
of how neural networks can be applied to these areas and providing a foundation for further research and practical development.  ...  to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems  ...  Fu et al [N21] proposed an automatic hybrid model, in which the statistical expectation maximization (EM) and the spatial pulse coupled neural network (PCNN) were integrated for brain MRI segmentation.  ... 
doi:10.1016/j.compmedimag.2010.07.003 pmid:20713305 fatcat:iycrdoy4yfgjfof2ml4xk7iz6i

A Survey of Image Processing Algorithms in Digital Mammography [chapter]

Jelena Bozek, Mario Mustra, Kresimir Delac, Mislav Grgic
2009 Studies in Computational Intelligence  
This chapter gives a survey of image processing algorithms that have been developed for detection of masses and calcifications.  ...  Wavelet detection methods and other recently proposed methods for calcification detection are presented.  ...  Renata Huzjan Korunic and Mr. Milan Grzan from the Department of Diagnostic and Interventional Radiology, University Hospital "Dubrava", Zagreb, Croatia, for providing digital mammographic images.  ... 
doi:10.1007/978-3-642-02900-4_24 fatcat:yugcbf3fx5hvdlk7ukoqeab4xi

The Utilization of Template Matching Method for License Plate Recognition: A Case Study in Malaysia [chapter]

Norazira A. Jalil, A. S. H. Basari, Sazilah Salam, Nuzulha Khilwani Ibrahim, Mohd Adili Norasikin
2014 Lecture Notes in Electrical Engineering  
This paper reviews image processing and neural network techniques applied at different stages which are preprocessing, filtering, feature extraction, segmentation and recognition in such way to remove  ...  The image of the vehicle license plate is captured and processed to produce a textual output for further processing.  ...  We also would like to thank all friends and colleagues for their helpful comments and courage.  ... 
doi:10.1007/978-3-319-07674-4_100 fatcat:ks5b3gs62vccrgx2syug6r55e4

Towards Better Segmentation of Abnormal Part in Multimodal Images Using Kernel Possibilistic C Means Particle Swarm Optimization With Morphological Reconstruction Filters

Sumathi R., Venkatesulu Mandadi
2021 International Journal of E-Health and Medical Communications (IJEHMC)  
Contrast-limited adaptive histogram equalization method is used for preprocessing images to enhance the intensity level and view the tumor part clearly.  ...  The proposed approach yields 97.06% segmentation accuracy and 98.08% classification accuracy for multimodal images with an average of 5s for all multimodal images.  ...  An hybrid approach (Aboul EllaHassanien &Tai-hoon Kim, 2012) to segment the tumor region by pulse coupled neural networks and SVM classifier is used to discriminate the tumor as cancer or not and proved  ... 
doi:10.4018/ijehmc.20210501.oa4 fatcat:p4empiixnjcnxkqzhvvqmmudqe

Frontmatter [chapter]

Nilanjan Dey, Gitanjali Shinde, Parikshit Mahalle, Henning Olesen
2019 The Internet of Everything  
The huge part in a mammogram image does not have a few MCs [3] . Ting et al. used self-regulated multilayer perceptron (MLP) neural network (NN) for the classification of BC.  ...  From digitized mammogram, automatically detect the suspicious lesion; for increasing the quality of the image some preprocessing steps have been done.  ...  IoT cloud infrastructure and stimulation of pulse parameter using sensors. Finally, the summary of the survey has been carried out by reviewing related studies. Behari et al.  ... 
doi:10.1515/9783110628517-fm fatcat:3npfgq3o65f5pdbphf6pi4qn3a

Dense Convolutional Network and Its Application in Medical Image Analysis

Tao Zhou, XinYu Ye, HuiLing Lu, Xiaomin Zheng, Shi Qiu, YunCan Liu, Chen Li
2022 BioMed Research International  
The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.  ...  Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis.  ...  [32] designed a lightweight neural network using simplified dense block consisting of two dense units, stitching adjacent blocks of mammogram and osteosarcoma histological images as input; feature vectors  ... 
doi:10.1155/2022/2384830 pmid:35509707 pmcid:PMC9060995 fatcat:7jp3tmtph5hk5gthgcomeccnte

A survey of breast cancer screening techniques: thermography and electrical impedance tomography

J. Zuluaga-Gomez, N. Zerhouni, Z. Al Masry, C. Devalland, C. Varnier
2019 Journal of Medical Engineering & Technology  
and convolutional neural networks.  ...  Last advances in computational tools, infra-red cameras and devices for bio-impedance quantification allowed the development of parallel techniques like, thermography, infra-red imaging and electrical  ...  Acknowledgement(s) This study has been support by the INTERREG institution, section France and Switzerland. Under the framework of the SBRA project.  ... 
doi:10.1080/03091902.2019.1664672 pmid:31545114 fatcat:rhfhwriwmjduxirkqtpn2hr64a

Deep Learning in Cardiology

Paschalis Bizopoulos, Dimitrios Koutsouris
2019 IEEE Reviews in Biomedical Engineering  
In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology.  ...  We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.  ...  [227] apply a pixelwise, patch-based procedure for breast arterial calcification detection in mammograms using a ten layer CNN and morphologic operation for post-processing.  ... 
doi:10.1109/rbme.2018.2885714 fatcat:pa47trmskvflvig5cotth265q4

Segmentation of biological images containing multitarget labeling using the jelly filling framework

Neeraj J. Gadgil, Paul Salama, Kenneth W. Dunn, Edward J. Delp
2018 Journal of Medical Imaging  
Biomedical imaging when combined with digital image analysis is capable of quantitative morphological and physiological characterizations of biological structures.  ...  We present a "jelly filling" method for segmentation of 3-D biological images containing multitarget labeling.  ...  We are grateful to Malgorzata Kamocka, Tarek Ashkar, Sherry Clendenon and James Sluka, Yuan Le, Randall Kroeker, Hal Kipfer, and Chen Lin for providing the image data.  ... 
doi:10.1117/1.jmi.5.4.044006 pmid:30840740 pmcid:PMC6251206 fatcat:rzjzwyfxoffklcugsxsqjcqsly

Wavelets in Temporal and Spatial Processing of Biomedical Images

Andrew F. Laine
2000 Annual Review of Biomedical Engineering  
We next describe some applications in magnetic resonance imaging, including activation detection and denoising of functional magnetic resonance imaging and encoding schemes.  ...  Next, wavelets in tomography are reviewed, including their relationship to the radon transform and applications in position emission tomography imaging.  ...  Finally, automated detection and classification of masses have been accomplished by methods of template matching (159) and artificial neural networks (160, 161) .  ... 
doi:10.1146/annurev.bioeng.2.1.511 pmid:11701522 fatcat:zyudqts6ave2lirghksik5i3ie

Program

2021 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)  
, and deep neural network (DNN), for network intrusion detection.  ...  In this study, we use MTCNN[1] for preprocessing and LRCNs[2] network structure and time-series face image data to try to solve the problem of unrecognizable user due to face blocking or face angle  ... 
doi:10.1109/icce-tw52618.2021.9602919 fatcat:aetmvxb7hfah7iuucbamos2wgu

Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?

Simon J. Doran, John H. Hipwell, Rachel Denholm, Björn Eiben, Marta Busana, David J. Hawkes, Martin O. Leach, Isabel dos Santos Silva
2017 Medical Physics (Lancaster)  
A semiautomated, bias-corrected, fuzzy C-means (BC-FCM) method was combined with morphological operations to segment the overall breast volume from in-phase Dixon images.  ...  Contemporaneously acquired T 1 -and T 2 -weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the  ...  ACKNOWLEDGMENTS We are extremely grateful to all the families who took part in the ALSPAC study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers,  ... 
doi:10.1002/mp.12320 pmid:28477346 pmcid:PMC5697622 fatcat:nj6dgiwrl5gzvj7e4qi2onfuuq
« Previous Showing results 1 — 15 out of 52 results