Intelligent image content semantic description for cardiac 3D visualisations

Trzupek, Mirosław; Ogiela, Marek R.; Tadeusiewicz, Ryszard

doi:10.1016/j.engappai.2011.05.005

Cited by 26 publications

(18 citation statements)

References 14 publications

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“…So the mechanism presented makes it possible to transform image information contained in images into a machine format, namely a characteristic description of graphs modelling the coronary vascularisation. What is more, this description can be additionally complemented by sequences generated by sequential grammars [9] that are used to represent the width graphs of individual coronary vessels (represented by the edges of the graph modelling the coronary vascularisation). Of course, the use of grammar formalisms also offers a number of other possibilities available when indexing with the use of the grammars applied.…”

Section: Technologies Of Semantic Image Retrieval -Example Of Ct Imagmentioning

confidence: 99%

“…The presented methodology of automatically creating semantic descriptions of images stored in multi-media medical databases is based on methods of semantically interpreting coronary arteries, successfully used to describe and identify lesions in coronary vascularisation images as part of previous studies by the authors [6][7][8][9]. What is important in creating sequences describing images from a database is a method of effectively transforming the image information contained in these images (which is easily perceived by a human) to a machine format which supports the intelligent, semantic selection of a specific case (easily assimilated by a computer).…”

Section: Technologies Of Semantic Image Retrieval -Example Of Ct Imagmentioning

confidence: 99%

“…One possible method consists in the proposed grammar formalisms for the structural analysis of images, in which the analysed image is treated as a hierarchical structure composed of so-called picture primitives. In their previous publications, the authors proposed using graph grammars to describe and model the spatial relations of coronary vascularisation reconstructions [6][7][8][9]. Grammars of this type generate a formal language in the form of graphs which can model the images considered here, and then the graphs obtained can be represented in the form of their characteristic descriptions.…”

Section: Technologies Of Semantic Image Retrieval -Example Of Ct Imagmentioning

confidence: 99%

See 2 more Smart Citations

Semantic Techniques of Image Retrieval – The Example of a Structural Analysis of Coronary Arteries

Trzupek

Ogiela

2012

Lecture Notes in Computer Science

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Part 2: WorkshopInternational audienceSpecialised medical databases currently play a major role in archiving and searching for comparable data acquired by different modalities of medical imaging. In the context of storing and searching for data in medical databases, it is of immense importance to find a method of extracting and representing the contents found in the image that would ensure the rapid access and satisfactory results of searches for image information records. What is important, this representation should also be independent of the form of the image. The wide spread of multimedia medical databases that can store not just single images but also video sequences has shown that the problem of effectively searching for images containing specific disease cases that are significant for medical diagnostics is still fraught with great difficulties. This article presents a semantic retrieval methods in medical imaging databases using graph formalisms of syntactic image recognition which contribute to solving these problems. The proposed methods although they are mainly predestined for medical applications can also provide a base for other solutions, particularly for the acquisition and sophisticated semantic analysis of complex image patterns for security and defence reasons

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Section: Technologies Of Semantic Image Retrieval -Example Of Ct Imagmentioning

confidence: 99%

Section: Technologies Of Semantic Image Retrieval -Example Of Ct Imagmentioning

confidence: 99%

Section: Technologies Of Semantic Image Retrieval -Example Of Ct Imagmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Techniques of Image Retrieval – The Example of a Structural Analysis of Coronary Arteries

Trzupek

Ogiela

2012

Lecture Notes in Computer Science

Self Cite

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“…Another possible option is extending the exposition time to 40 ms to match the interval of powerline-dependent light pulsation. Both enable to acquire the video with minimized artefacts, yet available ranges of acquisition parameters (frame rate, exposure) are limited [17].…”

Section: Setup and Acquisition Of High-quality Videosmentioning

confidence: 99%

Distant Measurement of Plethysmographic Signal in Various Lighting Conditions Using Configurable Frame-Rate Camera

Przybyło

Kańtoch

Jabłoński

et al. 2016

Metrology and Measurement Systems

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Videoplethysmography is currently recognized as a promising noninvasive heart rate measurement method advantageous for ubiquitous monitoring of humans in natural living conditions. Although the method is considered for application in several areas including telemedicine, sports and assisted living, its dependence on lighting conditions and camera performance is still not investigated enough. In this paper we report on research of various image acquisition aspects including the lighting spectrum, frame rate and compression. In the experimental part, we recorded five video sequences in various lighting conditions (fluorescent artificial light, dim daylight, infrared light, incandescent light bulb) using a programmable frame rate camera and a pulse oximeter as the reference. For a video sequence-based heart rate measurement we implemented a pulse detection algorithm based on the power spectral density, estimated using Welch's technique. The results showed that lighting conditions and selected video camera settings including compression and the sampling frequency influence the heart rate detection accuracy. The average heart rate error also varies from 0.35 beats per minute (bpm) for fluorescent light to 6.6 bpm for dim daylight.

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“…Different methods of classification of the signals were presented in the literature [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. These algorithms are based on data processing.…”

Section: Nearest Mean Classifiermentioning

confidence: 99%

Recognition of Monochrome Thermal Images of Synchronous Motor with the Application of Binarization and Nearest Mean Classifier

Głowacz

Korohoda

2014

Archives of Metallurgy and Materials

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This article discusses the recognition method of imminent failure conditions of synchronous motor. The proposed approach is based on a study of thermal images of the motor. Studies were carried out for four conditions of motor with the application of binarization and nearest mean classifier with Manhattan distance. Pattern creation process used 40 monochrome thermal images. Identification process was carried out for 160 monochrome thermal images. The experiments show that the method can be useful for protection of synchronous motor. Moreover, this method can be used to diagnose equipments in steelworks and other industrial plants.Keywords: Diagnostics, Recognition, Thermal images, Synchronous motor, nearest mean classifier Artykuł omawia metodę rozpoznawania stanów przedawaryjnych silnika synchronicznego. Proponowane podejście jest oparte na badaniu obrazów termowizyjnych silnika. Przeprowadzono badania dla czterech stanów silnika z zastosowaniem binaryzacji i klasyfikatora najbliższej średniej z metryką Manhattan. Proces tworzenia wzorców do rozpoznawania został przeprowadzony dla 40 monochromatycznych obrazów termowizyjnych. W procesie identyfikacji użyto 160 monochromatycznych obrazów termowizyjnych. Eksperymenty pokazują, że metoda może być przydatna do ochrony silników synchronicznych. Ponadto metoda może być stosowana do diagnozowania urządzeń w hutach i innych zakładach przemysłowych.

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Intelligent image content semantic description for cardiac 3D visualisations

Cited by 26 publications

References 14 publications

Semantic Techniques of Image Retrieval – The Example of a Structural Analysis of Coronary Arteries

Semantic Techniques of Image Retrieval – The Example of a Structural Analysis of Coronary Arteries

Distant Measurement of Plethysmographic Signal in Various Lighting Conditions Using Configurable Frame-Rate Camera

Recognition of Monochrome Thermal Images of Synchronous Motor with the Application of Binarization and Nearest Mean Classifier

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