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Case Retrieval in Medical Databases by Fusing Heterogeneous Information
TL;DR: A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper.
Abstract: A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework.
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TL;DR: This paper presents a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieved, and retrieval from diverse datasets.
Abstract: Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in the creation of image databases, as well as picture archiving and communication systems. These repositories now contain images from a diverse range of modalities, multidimensional (three-dimensional or time-varying) images, as well as co-aligned multimodality images. These image collections offer the opportunity for evidence-based diagnosis, teaching, and research; for these applications, there is a requirement for appropriate methods to search the collections for images that have characteristics similar to the case(s) of interest. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. Medical CBIR is an established field of study that is beginning to realize promise when applied to multidimensional and multimodality medical data. In this paper, we present a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieval, and retrieval from diverse datasets. We use these categories as a framework for discussing the state of the art, focusing on the characteristics and modalities of the information used during medical image retrieval.
194 citations
Cites methods from "Case Retrieval in Medical Databases..."
...Similarly, in [51], wavelets were fused with contextual semantic data for case retrieval....
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...Nonimage data Text: [56, 57, 70–76], [77, 78]; annotation or ontology: [33, 79, 80]; others: [50, 51]...
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...2D images Radiographs: [35–37]; spine X-rays: [38–44]; cervicographs: [45, 46]; mammograms: [47–49], [50, 51]; retinopathy: [49], [50, 51] 3D+ images CT: [31, 32, 52], [33]; MRI: [53–55]; dynamic PET: [56, 57]; PET-CT: [58–69]...
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University of Dundee1, University of Padua2, Oak Ridge National Laboratory3, Istituto Italiano di Tecnologia4, University of Tennessee Health Science Center5, Jules Stein Eye Institute6, University of Lincoln7, Institute for Infocomm Research Singapore8, University of Iowa9, National University of Singapore10, RMIT University11, Johns Hopkins University Applied Physics Laboratory12, Johns Hopkins University13, Charles Sturt University14, University of Burgundy15, French Institute of Health and Medical Research16, University of Edinburgh17, Princess Alexandra Eye Pavilion18
TL;DR: In this paper, the authors present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks.
Abstract: This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running submitted software automatically on the data stored, with clear and widely agreed-on performance criteria, to provide a fair comparison.
166 citations
TL;DR: This survey will directly help researchers understand the research developments of MSIF under RST and provide state-of-the-art understanding in specialized literature, as well as clarify the approaches and application of MSif in RST research community.
Abstract: Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary subject, and is referred to as, multi-sensor information fusion which was originated in the 1970s. Nowadays, the types and updates of data are becoming more multifarious and frequent, which bring new challenges for information fusion to deal with the multi-source data. Consequently, the construction of MSIF models suitable for different scenarios and the application of different fusion technologies are the core problems that need to be solved urgently. Rough set theory (RST) provides a computing paradigm for uncertain data modeling and reasoning, especially for classification issues with noisy, inaccurate or incomplete data. Furthermore, due to the rapid development of MSIF in recent years, the methodologies of learning under RST are becoming increasingly mature and systematic, unveiling a framework which has not been mentioned in the literature. In order to better clarify the approaches and application of MSIF in RST research community, this paper reviews the existing models and technologies from the perspectives of MSIF model (i.e., homogeneous and heterogeneous MSIF model), multi-view rough sets information fusion model (i.e., multi-granulation, multi-scale and multi-view decisions information fusion models), parallel computing information fusion model, incremental learning fusion technology and cluster ensembles fusion technology. Finally, RST based MSIF related research directions and challenges are also covered and discussed. By providing state-of-the-art understanding in specialized literature, this survey will directly help researchers understand the research developments of MSIF under RST.
105 citations
TL;DR: Results show that the proposed approach can be implemented in real practice for analysing noisy radiography images, which have many useful medical applications such as diagnosis of diseases related to lung, breast, musculoskeletal or pediatric studies.
Abstract: This paper introduces a three-step framework for classifying multiclass radiography images The first step utilizes a de-noising technique based on wavelet transform (WT) and the statistical Kolmogorov Smirnov (KS) test to remove noise and insignificant features of the images An unsupervised deep belief network (DBN) is designed for learning the unlabelled features in the second step Although small-scale DBNs have demonstrated significant potential, the computational cost of training the restricted Boltzmann machine is a major issue when scaling to large networks Moreover, noise in radiography images can cause a significant corruption of information that hinders the performance of DBNs The combination of WT and KS test in the first step helps improve performance of DBNs Discriminative feature subsets obtained in the first two steps serve as inputs into classifiers in the third step for evaluations Five frequently used classifiers including naive Bayes, radial basis function network, random forest, sequential minimal optimization, and support vector machine and four different case studies are implemented for experiments using the Image Retrieval in Medical Application data set The experimental results show that the three-step framework has significantly reduced computational cost and yielded a great performance for multiclass radiography image classification Along with effective applications in image processing in other fields published in the literature, deep learning network in this paper has again demonstrated its robustness in handling a complex set of medical images This implies that the proposed approach can be implemented in real practice for analysing noisy radiography images, which have many useful medical applications such as diagnosis of diseases related to lung, breast, musculoskeletal or pediatric studies
101 citations
TL;DR: A new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning is developed, which first investigates a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap.
Abstract: Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system.
60 citations
References
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Book•
01 Jan 1988TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Abstract: From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
15,671 citations
Book•
01 Jan 1976
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
Abstract: Both in science and in practical affairs we reason by combining facts only inconclusively supported by evidence. Building on an abstract understanding of this process of combination, this book constructs a new theory of epistemic probability. The theory draws on the work of A. P. Dempster but diverges from Depster's viewpoint by identifying his "lower probabilities" as epistemic probabilities and taking his rule for combining "upper and lower probabilities" as fundamental. The book opens with a critique of the well-known Bayesian theory of epistemic probability. It then proceeds to develop an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions. This rule, together with the idea of "weights of evidence," leads to both an extensive new theory and a better understanding of the Bayesian theory. The book concludes with a brief treatment of statistical inference and a discussion of the limitations of epistemic probability. Appendices contain mathematical proofs, which are relatively elementary and seldom depend on mathematics more advanced that the binomial theorem.
14,565 citations
TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
Abstract: Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.
6,447 citations
"Case Retrieval in Medical Databases..." refers background in this paper
...1(b), the edge from the parent node to its child node indicates that variable has a direct influence on variable ....
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...It relies on the idea that analogous problems have similar solutions....
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TL;DR: An overview of the foundational issues related to case-based reasoning is given, some of the leading methodological approaches within the field are described, and the current state of the field is exemplified through pointers to some systems.
Abstract: Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.
5,750 citations
"Case Retrieval in Medical Databases..." refers background in this paper
...G. Cazuguel and C. Roux are with the Department of Image et Traitement de l’Information (ITI), Institut Telecom/Telecom Bretagne, F-29200 Brest, France, and also with the Institut National de la Santé et de la Recherche Médicale (INSERM) U650, F-29200 Brest, France (e-mail: guy.cazuguel@telecom-bretagne.eu; christian.roux@telecom-bretagne.eu)....
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...…and C. Roux are with the Department of Image et Traitement de l’Information (ITI), Institut Telecom/Telecom Bretagne, F-29200 Brest, France, and also with the Institut National de la Santé et de la Recherche Médicale (INSERM) U650, F-29200 Brest, France (e-mail:…...
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...*G. Quellec is with the Department of Image et Traitement de l’Information (ITI), Institut Telecom/Telecom Bretagne, F-29200 Brest, France, and also with the Institut National de la Santé et de la Recherche Médicale (INSERM) U650, F-29200 Brest, France (e-mail: gwenole.quellec@telecom-bretagne.eu)....
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3,582 citations
"Case Retrieval in Medical Databases..." refers methods in this paper
...These methods are applied in Section IV to CADx in two heterogeneous databases: a diabetic retinopathy database and a mammography database....
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