The EM Algorithm and Extensions, 2E

McLachlan, Geoffrey J.; Krishnan, T.

doi:10.1002/9780470191613

Cited by 1,921 publications

(1,560 citation statements)

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“…After this cycle, a skin tumour with the contour of largest area is selected [12]. Many research studies are made by using this approach of EM algorithm and could be found in [13,16–18,56,57]. …”

Section: Methodsmentioning

confidence: 99%

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Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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Ultrasonic and digital dermatoscopy diagnostic methods are used in order to estimate the changes of structure, as well as to non-invasively measure the changes of parameters of lesions of human tissue. These days, it is very actual to perform the quantitative analysis of medical data, which allows to achieve the reliable early-stage diagnosis of lesions and help to save more lives. The proposed automatic statistical post-processing method based on integration of ultrasonic and digital dermatoscopy measurements is intended to estimate the parameters of malignant tumours, measure spatial dimensions (e.g. thickness) and shape, and perform faster diagnostics by increasing the accuracy of tumours differentiation. It leads to optimization of time-consuming analysis procedures of medical images and could be used as a reliable decision support tool in the field of dermatology.

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Section: Methodsmentioning

confidence: 99%

“…If the data mining process would be simple enough, the management of information problems would be already solved long time ago (R. Bellazzi, B. Zupan [14]). Practical data mining application in medicine has some obvious barriers as technological problems, trans-disciplinary communication, ethics, and patient data security [13,17,18]. …”

Section: Introductionmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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“…If the membership is still expressed as 18 (31) then the realization of G-G can be based on the fuzzy cluster centers which are expressed as (32) Otherwise, using the probabilistic expectation (with p=2), the posterior probability 18,24 will be (33) where is the observation, and a priori probability is (34) Hence we have the probabilistic expectation as (35) Since the posterior probability for cluster i is conditional upon the observationY′, by assuming a multivariate normal density distribution for each cluster, we have (36) where α′ is a normalization constant independent of i that can be determined by using .…”

Section: Improvement With G-g Approachmentioning

confidence: 99%

Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation

He¹,

Sajja²,

Datta³

et al. 2008

Ann Biomed Eng

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Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy cmeans (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.

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“…Table II reports the mean and variance estimates of the haplotype main effect β, the environmental main effect γ, and the interaction effect ν under the different genetic models. For the full retrospective and prospective approaches, we estimated the variance-covariance matrix using the empirical observed information matrix [McLachlan and Krishnan, 1997] as suggested by Lake et al [2003]. For the cases-only approach, we estimated the variancecovariance matrix using the observed information matrix.…”

Section: Simulations: Unmatched Datamentioning

confidence: 99%

“…We again approximate the estimated variance-covariance matrix using the empirical observed information matrix as described in McLachlan and Krishnan [1997].…”

Section: Simulations: Finely-stratified Datamentioning

confidence: 99%

Simple methods for assessing haplotype‐environment interactions in case‐only and case‐control studies

Kwee

Epstein

Manatunga

et al. 2006

Genetic Epidemiology

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For investigating haplotype-environment interactions in case-control studies, one can implement statistical methods based either on a retrospective likelihood (modeling the probability of haplotype and environment conditional on disease status) or a prospective likelihood (modeling the probability of disease status conditional on haplotype and environment). Retrospective approaches are generally more powerful than prospective approaches, but require an explicit model of the joint distribution of haplotype and environmental factors in the sample with the latter being particularly unattractive to specify. To resolve this issue, we propose a number of simple retrospective procedures for haplotypeenvironment interaction analysis that do not require explicit modeling of environmental covariates in the sample. We first consider a cases-only procedure, followed by a simple likelihood for casecontrol data that is proportional to the full-retrospective likelihood. Finally, we consider a retrospective procedure for inference on haplotype-environment interaction effects in matched or finely-stratified case-control studies. Our methods are based on the assumptions that haplotypes and environmental covariates are independent in the target population and that disease is rare. We illustrate our approaches using case-control data from the Finland-United States Investigation of Non-Insulin Dependent Diabetes Mellitus (FUSION) genetic study and simulated data.

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The EM Algorithm and Extensions, 2E

Cited by 1,921 publications

References 0 publications

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation

Simple methods for assessing haplotype‐environment interactions in case‐only and case‐control studies

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