BackgroundThe use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine learning techniques to construct a knowledge base. However, they often suffer from low precision in extracting entity and relationships. In this paper, we described a data-driven sublanguage pattern mining method that can be used to create a knowledge model. We combined natural language processing (NLP) and semantic network analysis in our model generation pipeline.MethodsAs a use case of our pipeline, we utilized data from an open source imaging case repository, Radiopaedia.org, to generate a knowledge model that represents the contents of medical imaging reports. We extracted entities and relationships using the Stanford part-of-speech parser and the “Subject:Relationship:Object” syntactic data schema. The identified noun phrases were tagged with the Unified Medical Language System (UMLS) semantic types. An evaluation was done on a dataset comprised of 83 image notes from four data sources.ResultsA semantic type network was built based on the co-occurrence of 135 UMLS semantic types in 23,410 medical image reports. By regrouping the semantic types and generalizing the semantic network, we created a knowledge model that contains 14 semantic categories. Our knowledge model was able to cover 98% of the content in the evaluation corpus and revealed 97% of the relationships. Machine annotation achieved a precision of 87%, recall of 79%, and F-score of 82%.ConclusionThe results indicated that our pipeline was able to produce a comprehensive content-based knowledge model that could represent context from various sources in the same domain.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0645-3) contains supplementary material, which is available to authorized users.

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Functional magnetic resonance imaging for presurgical brain mapping enables neurosurgeons to identify viable tissue near a site of operable pathology which might be at risk of surgery-induced damage. However, focal brain pathology (e.g., tumors) may selectively disrupt neurovascular coupling while leaving the underlying neurons functionally intact. Such neurovascular uncoupling can result in false negatives on brain activation maps thereby compromising their use for surgical planning. One way to detect potential neurovascular uncoupling is to map cerebrovascular reactivity using either an active breath-hold challenge or a passive resting-state scan. The equivalence of these two methods has yet to be fully established, especially at a voxel level of resolution. To quantitatively compare breath-hold and resting-state maps of cerebrovascular reactivity, we first identified threshold settings that optimized coverage of gray matter while minimizing false responses in white matter. When so optimized, the resting-state metric had moderately better gray matter coverage and specificity. We then assessed the spatial correspondence between the two metrics within cortical gray matter, again, across a wide range of thresholds. Optimal spatial correspondence was strongly dependent on threshold settings which if improperly set tended to produce statistically biased maps. When optimized, the two CVR maps did have moderately good correspondence with each other (mean accuracy of 73.6%). Our results show that while the breath-hold and resting-state maps may appear qualitatively similar they are not quantitatively identical at a voxel level of resolution.

Material detection is a vital need in dual-energy Xray luggage inspection systems at security of airport and strategic places. In this paper, a novel material detection algorithm based on power density function (PDF) estimation of three material categories in dual-energy X-ray images is proposed. In this algorithm, PDF of each material category is estimated from grayscale values of a synthetic image that is called fused image, using Gaussian Mixture Models (GMM). The fused image is obtained from wavelet subbands of high energy and low energy X-ray images. High and low energy Xray images enhance using two background removing and denoising stages as a preprocessing procedure. The proposed algorithm is evaluated on real images that have been captured from a dual-energy X-ray luggage inspection system. The obtained results show that the proposed algorithm is effective and operative in detecting of metallic, organic and mixed materials with acceptable accuracy.Keywords-Material detection, GMM-based power density function, fused image, dual-energy X-ray image. I. INTRODUCTIONHaving tremendous increase of terroristic threat in world, the need for inspection systems in public and strategic places is unavoidable [1]. For this purpose, dual-energy Xray luggage inspection systems are playing a basic role in increasing of security at airports, federal buildings. These systems utilize X-rays of two different energies as lowenergy and high-energy X-ray. The high-energy X-ray is generated with a high anode voltage around 140 kV, and the low energy X-ray is generated with a low anode voltage around 80 kV [2].In these systems, images are formed based on the energy absorption or transmission by objects [3]. When highenergy X-rays penetrate objects, the energy absorption depends considerably on the material's density. The higher the density is, the higher the energy absorption by the object, and so the darker image formed. For low-energy X-rays, however, the energy absorption depends primarily on the effective atomic number of the material as well as the thickness of the object. However, objects of high density materials such as metal are dark in both low and highenergy X-ray images, but objects of the lighter elements show as darker regions in low-energy images compared to high-energy images. Consequently, the light illicit materials such as explosive, illicit drug can be detected by comparing