Markov random fields
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Images of ocular fundus are routinely utilized in ophthalmology. Since an examination using fundus camera is relatively fast and cheap procedure, it can be used as a proper diagnostic tool for screening of retinal diseases such as the glaucoma. One of the glaucoma symptoms is progressive atrophy of the retinal nerve fiber layer (RNFL) resulting in variations of the RNFL thickness. Here, we introduce a novel approach to capture these variations using computer-aided analysis of the RNFL textural appearance in standard and easily available color fundus images. The proposed method uses the features based on Gaussian Markov random fields and local binary patterns, together with various regression models for prediction of the RNFL thickness. The approach allows description of the changes in RNFL texture, directly reflecting variations in the RNFL thickness. Evaluation of the method is carried out on 16 normal ("healthy") and 8 glaucomatous eyes. We achieved significant correlation (normals: ρ=0.72±0.14; p≪0.05, glaucomatous: ρ=0.58±0.10; p≪0.05) between values of the model predicted output and the RNFL thickness measured by optical coherence tomography, which is currently regarded as a standard glaucoma assessment device. The evaluation thus revealed good applicability of the proposed approach to measure possible RNFL thinning.
- MeSH
- barva * MeSH
- discus nervi optici patologie MeSH
- fundus oculi MeSH
- glaukom patologie MeSH
- lidé MeSH
- Markovovy řetězce * MeSH
- nervová vlákna patologie MeSH
- normální rozdělení MeSH
- optická koherentní tomografie MeSH
- retinální gangliové buňky patologie MeSH
- vylepšení obrazu metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
... Monte Carlo statistical methods, particularly those based on Markov chains, are now an essential component ... ... coverage of these simulation techniques, with incorporation of the most recent developments in the field ... ... In particular, the introductory coverage of random variable generation has been totally revised, with ... ... The authors do not assume familiarity with Monte Carlo techniques (such as random variable generation ... ... ), with computer programming, or with any Markov chain theory (the necessary concepts are developed in ...
Springer texts in statistics
2nd ed. xxx, 645 s., grafy
... This book extends the field by allowing for multivariate times. ... ... As the field is rather new, the concepts and the possible types of data are described in detail, and ... ... The Markov model makes up an important special case, but it also describes how easily more general models ... ... Frailty models, which are random effects models for survival data, make a second approach; they extend ... ... processes 167 -- 5.7 Markov extension models 168 -- 5.8 General models 169 -- 5.9 Conditionally simple ...
Statistics for biology and health
1st ed. xvii, 542 s.
This paper explains general model-based approaches to two medical image pattern recognition applications. The first application is Bayesian segmentation of breast regions of interest i.e: pectoral muscle, fatty and fibroglandular regions, using Markov Random Fields. This work is a part of a computer aided diagnosis project aiming at evaluating breast cancer risk and its association with texture characteristics of regions of interest on digitized mammograms. Owing to obtained segmentation results, the proposed method could be considered as a satisfying first approach for segmenting regions of interest in a breast. Another dermatology application with the aim to classify skin images of several disorders is briefly discussed. The second part of our contribution will give an overview of the achievements of another project that has beentaking place since 2000 in the UK. The Medical Image Management and Assessment System is developed in cooperation between QinetiQ, Inc. and Universities of Cambridge, Oxford and Surrey. We will show how image analysis tools form the basis of a telemedicine system targeted at early breast cancer grade assessment. The system is capable of processing images uploaded from remote sites. It enables image analysis and classification based on combined outputs of several co-operating automated classifiers, textual and graphical annotation of processed images, semi-automatic diagnosis generation, natural language text query processing, and more. This is an example of a visionary approach that, in the long term, may lead to substantialsavings of breast cancer diagnosis cost by eliminating unnecessary thorough examination of false-alarm cases.
... Programming 43 -- 2.9.5 Divide-and-Conquer Algorithms 48 -- 2.9.6 Machine Learning 48 -- 2.9.7 Randomized ... ... -- 10.12 Notes 379 -- Biobox: Ron Shamir 380 -- 10.13 Problems 384 -- Contents xiii -- 11 Hidden Markov ... ... Models 387 -- 11.1 CG-Islands and the \"Fair Bet Casino\" 387 -- 11.2 The Fair Bet Casino and Hidden Markov ... ... Profile HMM Alignment 398 -- 11.6 Notes 400 -- Biobox: David Haussler 403 -- 11.7 Problems 407 -- 12 Randomized ... ... Algorithms 409 -- 12.1 The Sorting Problem Revisited 409 -- 12.2 Gibbs Sampling 412 -- 12.3 Random Projections ...
Computational molecular biology series
[1st ed.] xviii, 435 s. : il.
- MeSH
- algoritmy MeSH
- informatika MeSH
- Konspekt
- Lékařské vědy. Lékařství
- NLK Obory
- lékařská informatika
... Concepts in Probability Theory 2 -- 1.1.3 Combining Predictive and Diagnostic Supports 6 -- 1.1.4 Random ... ... - 2.8 Nontemporal Causation and Statistical Time 57 -- 2.9 Conclusions 59 -- 2.9.1 On Minimality, Markov ...
1st ed. xii, 384 s.
- MeSH
- kauzalita MeSH
- pravděpodobnost MeSH
- Konspekt
- Přírodní vědy. Matematické vědy
- NLK Obory
- přírodní vědy
- statistika, zdravotnická statistika
