A Hybrid System Using Symbolic and Numeric Knowledge for the Semantic Annotation of Sulco-Gyral Anatomy in Brain MRI Images

Mechouche, Ammar; Morandi, Xavier; Golbreich, Christine; Gibaud, Bernard

doi:10.1109/tmi.2009.2026746

Cited by 15 publications

(14 citation statements)

References 25 publications

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“…The next step is to describe the visual features of the ROI using a set of semantic terms. For the sake of generality, the proposed semantic framework can be instantiated with two strategies for choosing these terms: (1) automatic annotation using a machine learning algorithm to predict the presence (or the absence) of the terms from computational imaging features derived from the ROI [46] or (2) manual annotations based on observations made by a radiologist. As the first strategy is out of the scope of this paper, we focus in this work the second strategy.…”

Section: Methodsmentioning

confidence: 99%

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

Kurtz

Beaulieu

Napel

et al. 2014

Journal of Biomedical Informatics

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Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification.

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Section: Methodsmentioning

confidence: 99%

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

Kurtz

Beaulieu

Napel

et al. 2014

Journal of Biomedical Informatics

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“…signal is currently available on the platform, we can check the consistency of this result set by the semantic rule expressed in Figure 3 in the way Mechouche et al proceeded for brain annotation issues [3]. This semantic checking will provide a set of results R signal×knowledge lowering the false alarm rate and subsequently improving the specificity rate of the image engine.…”

Section: Nt H Ec a S Et H A Trmentioning

confidence: 99%

“…Few works have operationally explored this kind of idea among which we can mention [3,4]. Section 2 focuses on the low-level image analysis modules currently available in our system.…”

Section: Introductionmentioning

confidence: 99%

Ontology-Driven Image Analysis for Histopathological Images

Othmani

Meziat

Loménie

2010

Advances in Visual Computing

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Abstract. Ontology-based software and image processing engine must cooperate in new fields of computer vision like microscopy acquisition wherein the amount of data, concepts and processing to be handled must be properly controlled. Within our own platform, we need to extract biological objects of interest in huge size and high-content microscopy images. In addition to specific low-level image analysis procedures, we used knowledge formalization tools and high-level reasoning ability of ontology-based software. This methodology made it possible to improve the expressiveness of the clinical models, the usability of the platform for the pathologist and the sensitivity or sensibility of the low-level image analysis algorithms.

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“…However, this approach is applicable only to CT data sets of human torso, and verified only within a small set of sample images. Another similar system [63] uses numerical atlas and symbolic knowledge (ontology and description logic rules) in an integrated way to achieve semiautomatic annotation of brain MRI images.…”

Section: Setting Systemsmentioning

confidence: 99%