Prof. Luc M. Bidaut Profile

Prof. Luc M. Bidaut

Head of School of Computer Science at Univ of Lincoln

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Publications (8)

SPIE Journal Paper | 22 June 2021

Neural architecture search of echocardiography view classifiers

Massoud Zolgharni, Neda Azarmehr, Xujiong Ye, James Howard, Elisabeth Lane, Robert Labs, Matthew Shun-Shin, Graham Cole, Luc Bidaut, Darrel Francis

JMI, Vol. 8, Issue 03, 034002, (June 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.3.034002

KEYWORDS: Echocardiography, Data modeling, Image classification, Image quality, Image resolution, Surgery, Performance modeling, Video, Image processing, Neural networks

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SPIE Journal Paper | 5 March 2019

In-vivo Barrett’s esophagus digital pathology stage classification through feature enhancement of confocal laser endomicroscopy

Noha Ghatwary, Amr Ahmed, Enrico Grisan, Hamid Jalab, Luc Bidaut, Xujiong Ye

JMI, Vol. 6, Issue 01, 014502, (March 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.014502

KEYWORDS: Image enhancement, Image filtering, Nanolithography, Esophagus, Confocal microscopy, Pathology, Endomicroscopy, Image classification, In vivo imaging, Feature extraction

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Barrett’s esophagus (BE) is a premalignant condition that has an increased risk to turn into esophageal adenocarcinoma. Classification and staging of the different changes (BE in particular) in the esophageal mucosa are challenging since they have a very similar appearance. Confocal laser endomicroscopy (CLE) is one of the newest endoscopy tools that is commonly used to identify the pathology type of the suspected area of the esophageal mucosa. However, it requires a well-trained physician to classify the image obtained from CLE. An automatic stage classification of esophageal mucosa is presented. The proposed model enhances the internal features of CLE images using an image filter that combines fractional integration with differentiation. Various features are then extracted on a multiscale level, to classify the mucosal tissue into one of its four types: normal squamous (NS), gastric metaplasia (GM), intestinal metaplasia (IM or BE), and neoplasia. These sets of features are used to train two conventional classifiers: support vector machine (SVM) and random forest. The proposed method was evaluated on a dataset of 96 patients with 557 images of different histopathology types. The SVM classifier achieved the best performance with 96.05% accuracy based on a leave-one-patient-out cross-validation. Additionally, the dataset was divided into 60% training and 40% testing; the model achieved an accuracy of 93.72% for the testing data using the SVM. The presented model showed superior performance when compared with four state-of-the-art methods. Accurate classification is essential for the intestinal metaplasia grade, which most likely develops into esophageal cancer. Not only does our method come to the aid of physicians for more accurate diagnosis by acting as a second opinion, but it also acts as a training method for junior physicians who need practice in using CLE. Consequently, this work contributes to an automatic classification that facilitates early intervention and decreases samples of required biopsy.

Proceedings Article | 13 March 2010 Paper

Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set

Guozhi Tao, Ashish Singh, Luc Bidaut

Proceedings Volume 7623, 76230V (2010) https://doi.org/10.1117/12.844529

KEYWORDS: Liver, Image segmentation, Tissues, Expectation maximization algorithms, Computed tomography, Tumors, Image enhancement, Liver cancer, Image registration, Medical imaging

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Proceedings Article | 10 March 2006 Paper

Rapid prototype modeling in a multimodality world

Luc Bidaut, John Madewell, Alan Yasko

Proceedings Volume 6141, 61411J (2006) https://doi.org/10.1117/12.653954

KEYWORDS: Bone, Surgery, 3D modeling, Tumors, Tissues, Data modeling, Visualization, Rapid manufacturing, Visual process modeling, Fused deposition modeling

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Proceedings Article | 28 May 2001 Paper

Model-based multiconstrained integration of invasive electrophysiology with other modalities

Luc Bidaut

Proceedings Volume 4319, (2001) https://doi.org/10.1117/12.428115

KEYWORDS: Electrodes, Electroencephalography, Magnetic resonance imaging, Brain, Electrophysiology, Epilepsy, Surgery, Positron emission tomography, Computed tomography, Single photon emission computed tomography

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Proceedings Article | 20 December 1999 Paper

Visible Human Slice Web Server: a first assessment

Roger Hersch, Benoit Gennart, Oscar Figueiredo, Marc Mazzariol, Joaquin Tarraga, S. Vetsch, Vincent Messerli, R. Welz, Luc Bidaut

Proceedings Volume 3964, (1999) https://doi.org/10.1117/12.373466

KEYWORDS: 3D image processing, Java, Tomography, Human-machine interfaces, Software, Visualization, Digital photography, Computing systems, Volume visualization, 3D visualizations

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Proceedings Article | 8 April 1996 Paper

Assessment of neurological (dys)function through multidimensional and fully multisensor biomedical imaging

Luc Bidaut, Roberto Pascual-Marqui, Jacqueline Delavelle, Margitta Seeck, Daniel Slosman, Daniel Ruefenacht

Proceedings Volume 2709, (1996) https://doi.org/10.1117/12.237872

KEYWORDS: Single photon emission computed tomography, Electrodes, Brain, Magnetic resonance imaging, Electroencephalography, Positron emission tomography, Visualization, Tomography, Epilepsy, Data conversion

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Proceedings Article | 1 June 1991 Paper

Composite PET and MRI for accurate localization and metabolic modeling: a very useful tool for research and clinic

Luc Bidaut

Proceedings Volume 1445, (1991) https://doi.org/10.1117/12.45203

KEYWORDS: Positron emission tomography, Magnetic resonance imaging, Tissues, Head, Data modeling, Data acquisition, Imaging systems, Brain, Data processing, Mathematical modeling

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Showing 5 of 8 publications

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