A probabilistic framework for image information fusion with an application to mammographic analysis

Velikova, Marina; Lucas, Peter; Samulski, Maurice; Karssemeijer, Nico

doi:10.1016/j.media.2012.01.003

Cited by 31 publications

(19 citation statements)

References 21 publications

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“…The flexibility, interpretability, simplicity and statistically better performance are the key aspects of the proposed framework. Velikova et al [13] came up with a unified probabilistic framework for information fusion to boost decision making in determining the characteristics of regions in complimentary image views. In this framework, accuracy is ensured in uncertain domain knowledge without considering the image resolution.…”

Section: Application Of Bayesian Network In Image Processingmentioning

confidence: 99%

“…BN is used in various image processing applications such as in image segmentation [9], [43], development of unified framework [13], [16], classification image based information [8], [17], [18], interacted regions analysis of an image [7] with enhanced recognition rate [12], traffic incident detection [10] and signature verification [15]. BN is supportive in establishing the relationships among associated objects [14].…”

Section: A Representation Of Bayesian Network In Image Processingmentioning

confidence: 99%

“…The goal is to build inference engine for domain knowledge and training of data of few case studies. In [13], the characteristics of regions in complimentary images are determined probabilistically for boosting decision making. Low-level image of indoor and outdoor scenes are classified by predicting semantic features.…”

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confidence: 99%

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An Overview of Bayesian Network Applications in Uncertain Domains

Iqbal¹,

Yin²,

Hao³

et al. 2015

IJCTE

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Abstract-Uncertainty is a major barrier in knowledge discovery from complex problem domains. Knowledge discovery in such domains requires qualitative rather than quantitative analysis. Therefore, the quantitative measures can be used to represent uncertainty with the integration of various models. The Bayesian Network (BN) is a widely applied technique for characterization and analysis of uncertainty in real world domains. Thus, the real application of BN can be observed in a broad range of domains such as image processing, decision making, system reliability estimation and PPDM (Privacy Preserving in Data Mining) in association rule mining and medical domain analysis. BN techniques can be used in these domains for prediction and decision support. In this article, a discussion on general BN representation, draw inferences, learning and prediction is followed by applications of BN in some specific domains. Domain specific BN representation, inferences and learning process are also presented. Building upon the knowledge presented, some future research directions are also highlighted.Index Terms-Uncertainty, knowledge discovery, Bayesian network, image processing, decision making, privacy preservation, system reliability estimation. I. INTRODUCTIONUncertainty is a commonly faced problem in real world applications. Uncertainty can be described as an inadequate amount of information [1]. Nevertheless, uncertainty may also exist in situations that have enough amount of information [2]. Furthermore, uncertainty may be alleviated or eliminated with the addition of new information. Addition of more information in complex processes may lead to mining of limited knowledge. Uncertainty can be computed mathematically with probability theory. In uncertain situations, there is an involvement of possibility of states of attributes. Consequently, the models established on probabilistic inferences have the capability to assign a probabilistic value according to a defined principle. Accordingly, the prediction with large number of states in a model is accomplished. The question rises "how prediction is realized in the presence of large number of states in a model?" An answer to this question is the employment of Bayesian Network (BN) with several variables [3]- [5]. BNs, also known as belief networks, belong to the family of probabilistic graphical models. These graphical structures correspond to knowledge about an uncertain domain. More specifically, each node in the graphical structure represents a random variable, while the edges/arcs between the nodes represent conditional dependencies among nodes. These conditional dependencies are estimated by using acknowledged statistical and computational methods. Consequently, BNs incorporate concepts from graph and probability theory, computer science, and statistics.Since last two decades, BN is recognized as an important tool for a number of expert systems especially in domains involving uncertainty [6]. This recognition of BN has several reasons behind it. First, BN encodes the depen...

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Section: Application Of Bayesian Network In Image Processingmentioning

confidence: 99%

Section: A Representation Of Bayesian Network In Image Processingmentioning

confidence: 99%

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An Overview of Bayesian Network Applications in Uncertain Domains

Iqbal¹,

Yin²,

Hao³

et al. 2015

IJCTE

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“…There is other recent research where causal independence models have been explored in designing medical Bayesian networks [37,41,38,43]. Bayesian networks also offer powerful methods to represent and reason about uncertain state changes over time.…”

Section: Preliminariesmentioning

confidence: 99%

Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare

Velikova

Scheltinga

Lucas

et al. 2014

International Journal of Approximate Reasoning

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Bridging the gap between the theory of Bayesian networks and solving an actual problem is still a big challenge and this is in particular true for medical problems, where such a gap is clearly evident. We argue that Bayesian networks offer appropriate technology for the successful modelling of medical problems, including the personalisation of healthcare. Personalisation is an important aspect of remote disease management systems. It involves the forecasting of progression of a disease based on the interpretation of patient data by a disease model. A natural foundation for disease models is physiological knowledge, as such knowledge facilitates building clinically understandable models. This paper proposes ways to represent such knowledge as part of engineering principles employed in building clinically practical probabilistic models. The methodology has been used to construct a temporal Bayesian network model for preeclampsia -a pregnancy-related disorder. The model is the first of its kind and an integral part of a mobile home-monitoring system intended for use in daily pregnancy care. We conducted an evaluation study with actual patient data to obtain insight into the model's performance and suitability. The results obtained are encouraging and show the potential of exploiting physiological knowledge for personalised decisionsupport systems.

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“…By the end of the 1980s, people began to use it in common image processing such as multi-focus image and visible image [3]. After the 90s, huge progress had been made in the research of image fusion due to such multi-resolution decomposition algorithm and multiresolution fusion theory as Laplacian Pyramid and Gaussian pyramid, however, these algorithms did not decompose or transfer directly on the images to be fused in the fusion processing, instead, the fusion processing was only conducted in one level [4].…”

Section: Introductionmentioning

confidence: 99%