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Skeletal growth estimation using radiographic image processing and analysis

S. Mahmoodi, B.S. Sharif, E.G. Chester, J.P. Owen, R. Lee
2000 IEEE Transactions on Information Technology in Biomedicine  
Images were obtained from hand radiographs of 32 male and 25 female children of age 1-16 yr.  ...  From these descriptors, a feature vector was selected for a regression model and a Bayesian estimator. The estimation accuracy was 84% for females and 82% for males.  ...  suggested an artificial intelligence approach based on Bayesian inference to localize and segment phalanges [9] .  ... 
doi:10.1109/4233.897061 pmid:11206814 fatcat:jdxy4ybb3vhc3nz2bzv23jsixa

A CRF approach to fitting a generalized hand skeleton model

Radu Paul Mihail, Gustav Blomquist, Nathan Jacobs
2014 IEEE Winter Conference on Applications of Computer Vision  
We present a new point distribution model capable of modeling joint subluxation (shifting) in rheumatoid arthritis (RA) patients and an approach to fitting this model to posteroanterior view hand radiographs  ...  We provide an empirical analysis of the relative value of different potential functions.  ...  Kristine Lohr and Judy Goldsmith for helping us obtain the Rheumatoid Arthritis Dataset. We also gratefully acknowledge DARPA grant D11AP00255 which partially supported this work.  ... 
doi:10.1109/wacv.2014.6836070 dblp:conf/wacv/MihailBJ14 fatcat:sxp3n5e43fffll6osz4tllqxk4

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Sami Bourouis, Abdullah Alharbi, Nizar Bouguila
2021 Journal of Imaging  
We tackle the current problem via a fully Bayesian approach based on a flexible statistical model named shifted-scaled Dirichlet mixture models (SSDMM).  ...  We investigate here a Markov Chain Monte Carlo (MCMC) estimator, which is a computer–driven sampling method, for learning the developed model.  ...  Acknowledgments: Authors would like to thank the Deanship of Scientific Research, Taif University, Kingdom of Saudi Arabia, for their funding support under grant number 1-441-50.  ... 
doi:10.3390/jimaging7010007 pmid:34460578 pmcid:PMC8321244 fatcat:vlnqexzuovhp7bcfmsi66hfuwe

Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images

Hassen Sallay, Sami Bourouis, Nizar Bouguila
2020 Computers  
They also confirm the superiority of the Gamma mixture model compared to the Gaussian mixture model for medical images' classification.  ...  The proposed approach takes advantage of the Gamma distribution flexibility, the online learning scalability, and the variational inference efficiency.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/computers10010006 fatcat:mwqmrsv2qjeuli76yyrrqcjxty

A Static SMC Sampler on Shapes for the Automated Segmentation of Aortic Calcifications [chapter]

Kersten Petersen, Mads Nielsen, Sami S. Brandt
2010 Lecture Notes in Computer Science  
Our method suits for segmentation tasks where the number of objects is not known a priori, or where the object of interest is invisible and can only be inferred from other objects in the image.  ...  In this paper, we propose a sampling-based shape segmentation method that builds upon a global shape and a local appearance model.  ...  By following this Bayesian idea, we benefit especially multi-stage segmentation models.  ... 
doi:10.1007/978-3-642-15561-1_48 fatcat:sksbqzkvqrcwzbqaymmmujntty

Evidential reasoning for object recognition

T.O. Binford, T.S. Levitt
2003 IEEE Transactions on Pattern Analysis and Machine Intelligence  
hierarchical Bayesian inference, Bayesian networks, and decision graphs to evidential reasoning for object recognition.  ...  The principles summarize research and findings by the authors up through the mid-1990s, including seminal results in object-centered computer vision, figure-ground discrimination, and the application of  ...  Bayesian Inference for Object Recognition Bayesian Networks (BNs) [34] , [21] combine symbolic, logical, probabilistic, and statistical information in a network formalism that integrates model-based  ... 
doi:10.1109/tpami.2003.1206513 fatcat:d7eny7q7lbetxe7vjklrkebxvu

Skeletal Bone Age Assessment – Research Directions

P. Thangam, T. V. Mahendiran, K. Thanushkodi
2012 Journal of Engineering Science and Technology Review  
Bone age is assessed from the left-hand wrist radiograph and then compared with the chronological age. A discrepancy between the two indicates abnormalities.  ...  The work was motivated by the increasing awareness of the need for bone age assessment (BAA) schemes featuring an appropriate methodology for skeletal age estimation.  ...  Region based technique Manos et. al. developed computer based techniques, in 1994 for the segmentation of hand-wrist radiographs and in particular those obtained for the TW2 method of skeletal bone age  ... 
doi:10.25103/jestr.051.16 fatcat:td5pnyekfngkvjsdxqqd7osxra

Improved convergence of gradient-based reconstructions using multiscale models

Gregory S. Cunningham, Igor Koyfman, Kenneth M. Hanson, Murray H. Loew, Kenneth M. Hanson
1996 Medical Imaging 1996: Image Processing  
In this article, we use explicit models of geometry for a variety of Bayesian estimation problems, including image segmentation, reconstruction and restoration.  ...  Explicit models of geometry, also called deformable models, snakes, or active contours, have been used extensively to solve image segmentation problems in a non-Bayesian framework.  ...  ACKNOWLEDGEMENTS This work was supported by the U.S. Dept. of Energy under Contract W-7405-ENG-36.  ... 
doi:10.1117/12.237918 dblp:conf/miip/CunninghamKH96 fatcat:6tcmefzetjco3alua464qarjfq

CBCT of a Moving Sample from X-rays and Multiple Videos

2018 IEEE Transactions on Medical Imaging  
Our second innovation is to rely on Bayesian inference to solve for a dense attenuation volume given planar radioscopic images of a moving sample.  ...  Results show that the proposed strategy is able to reconstruct dense volumetric attenuation models from a very limited number of radiographic views over time on synthetic and in-situ data.  ...  ACKNOWLEDGEMENTS This research was partly funded by the KINOVIS (ANR-11-EQPX-0024) and CaMoPi (ANR-16-CE33-0014) projects.  ... 
doi:10.1109/tmi.2018.2865228 pmid:30106718 fatcat:622has2zife4hcxsamwd22lbwm

Smart Spotting of Pulmonary TB Cavities Using CT Images

V. Ezhil Swanly, L. Selvam, P. Mohan Kumar, J. Arokia Renjith, M. Arunachalam, K. L. Shunmuganathan
2013 Computational and Mathematical Methods in Medicine  
But the automatic technique proposed in this paper focuses on accurate detection of disease by computed tomography (CT) using computer-aided detection (CAD) system.  ...  classification using Bayesian classifier.  ...  In [16] , a technique to detect TB has been proposed, based on binarization process to set edge lines of ribs, Gradient vector flow model for segmentation and K-means algorithm for classification.  ... 
doi:10.1155/2013/864854 pmid:24367393 pmcid:PMC3866811 fatcat:246pq6q45jgwhkgngyb6uzllda

Computer-aided diagnosis in chest radiography: a survey

B. Van Ginneken, B.M. Ter Haar Romeny, M.A. Viergever
2001 IEEE Transactions on Medical Imaging  
The purpose of this survey is to categorize and briefly review the literature on computer analysis of chest images, which comprises over 150 papers published in the last 30 years.  ...  This explains the continued interest in computer-aided diagnosis for chest radiography.  ...  ACKNOWLEDGMENT This paper is a result of a four-year project on computer-aided diagnosis in chest radiography performed at the Image Sciences Instutute, University Medical Center Utrecht, The Netherlands  ... 
doi:10.1109/42.974918 pmid:11811823 fatcat:5msbcqvs6reabpd6nodjpxp4hi

A Survey of Uncertainty in Deep Neural Networks [article]

Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang (+2 others)
2022 arXiv   pre-print
The modeling of these uncertainties based on deterministic neural networks, Bayesian neural networks, ensemble of neural networks, and test-time data augmentation approaches is introduced and different  ...  For a practical application, we discuss different measures of uncertainty, approaches for the calibration of neural networks and give an overview of existing baselines and implementations.  ...  Prince, of the environment model for model-based reinforcement learning,” in and A.  ... 
arXiv:2107.03342v3 fatcat:cex5j3xq5fdijjdtdbt2ixralm

Uncertainty Assisted Robust Tuberculosis Identification with Bayesian Convolutional Neural Networks

Zain Ul Abideen, Mubeen Ghafoor, Kamran Munir, Madeeha Saqib, Ata Ullah, Tehseen Zia, Syed Ali Tariq, Ghufran Ahmad, Asma Zahra
2020 IEEE Access  
This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN).  ...  Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty.  ...  Moreover, we have deployed Bayesian based CNN (B-CNN) architecture to overcome the softmax inference issue.  ... 
doi:10.1109/access.2020.2970023 pmid:32391238 pmcid:PMC7176037 fatcat:snyjhak635fh5birmhcoaurydm

Cardiomegaly Detection on Chest Radiographs: Segmentation versus Classification

Ecem Sogancioglu, Keelin Murphy, Erdi Calli, Ernst Scholten, Steven Schalekamp, Bram Van Ginneken
2020 IEEE Access  
We used the publicly available ChestX-ray14 dataset, and obtained heart and lung segmentation annotations for 778 chest radiographs for the development of the segmentation-based approach.  ...  The performance of the segmentation-based system with an AUC of 0.977 is significantly better for classifying cardiomegaly than the classificationbased model which achieved an AUC of 0.941.  ...  SEGMENTATION-BASED METHOD The segmentation-based approach (seg-method) is designed to address the cardiomegaly detection task on chest radiographs, through segmentation of the heart and lungs and subsequent  ... 
doi:10.1109/access.2020.2995567 fatcat:n5vrcjoc2jfujfabsgtx46sa5q

Estimating MRI Image Quality via Image Reconstruction Uncertainty [article]

Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso
2021 arXiv   pre-print
We train Bayesian CNNs using a heteroscedastic uncertainty model to recover clean images from noisy data, providing measures of uncertainty over the predictions.  ...  Recognising this distinction between visual and algorithmic quality has the impact that, depending on the downstream task, less data can be excluded based on "visual quality" reasons alone.  ...  On the other hand, for the model trained with synthetic artefacts, perfect alignment between the artefact and clean images enables the uncertainty network to correctly model the noise in the data.  ... 
arXiv:2106.10992v1 fatcat:jt3ktdw7djdw5fok75qg5p3cqu
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