Prof. Xujiong Ye Profile

Prof. Xujiong Ye

Reader at Univ of Lincoln

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Publications (11)

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 | 15 April 2019 Open Access

Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons

Lei Zhang, Guang Yang, Xujiong Ye

JMI, Vol. 6, Issue 02, 024001, (April 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.024001

KEYWORDS: Skin, Image segmentation, Data modeling, Network architectures, Melanoma, Performance modeling, Image filtering, Convolution, Surgery, Associative arrays

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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.

SPIE Journal Paper | 17 April 2017

Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image

Lei Zhang, Nicholas Dudley, Tryphon Lambrou, Nigel Allinson, Xujiong Ye

JMI, Vol. 4, Issue 02, 024001, (April 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.2.024001

KEYWORDS: Fetus, Image quality, Image segmentation, Head, Skull, Biometrics, Quality measurement, Gaussian filters, Ultrasonography, Image quality standards

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Proceedings Article | 3 March 2017 Presentation + Paper

Differentiation of pre-ablation and post-ablation late gadolinium-enhanced cardiac MRI scans of longstanding persistent atrial fibrillation patients

Guang Yang, Xiahai Zhuang, Habib Khan, Shouvik Haldar, Eva Nyktari, Lei Li, Xujiong Ye, Greg Slabaugh, Tom Wong, Raad Mohiaddin, Jennifer Keegan, David Firmin

Proceedings Volume 10134, 101340O (2017) https://doi.org/10.1117/12.2250910

KEYWORDS: Image classification, Medical imaging, Image processing, Image segmentation, Cardiovascular magnetic resonance imaging, Heart, Atrial fibrillation, Image enhancement, Veins, Feature extraction

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Proceedings Article | 3 March 2017 Paper

Automatic grade classification of Barretts Esophagus through feature enhancement

Noha Ghatwary, Amr Ahmed, Xujiong Ye, Hamid Jalab

Proceedings Volume 10134, 1013433 (2017) https://doi.org/10.1117/12.2250364

KEYWORDS: Esophagus, Image enhancement, Endoscopy, Biopsy, Image filtering, Image processing, Image classification, Nanolithography, Feature extraction, Discrete wavelet transforms

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Proceedings Article | 24 February 2017 Presentation + Paper

Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI

Guang Yang, Xiahai Zhuang, Habib Khan, Shouvik Haldar, Eva Nyktari, Lei Li, Xujiong Ye, Greg Slabaugh, Tom Wong, Raad Mohiaddin, Jennifer Keegan, David Firmin

Proceedings Volume 10133, 1013313 (2017) https://doi.org/10.1117/12.2250926

KEYWORDS: Image segmentation, Cardiovascular magnetic resonance imaging, Visualization, Veins, Heart, Electrocardiography, Medical imaging, 3D image processing, Lithium, Medical research

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Proceedings Article | 21 March 2016 Paper

Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

Guang Yang, Xujiong Ye, Greg Slabaugh, Jennifer Keegan, Raad Mohiaddin, David Firmin

Proceedings Volume 9784, 97840L (2016) https://doi.org/10.1117/12.2207440

KEYWORDS: Medical imaging, Denoising, Super resolution, Wavelet transforms, Image compression, Discrete wavelet transforms, Image analysis, Image processing, Magnetic resonance imaging, Imaging systems, Image restoration

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Proceedings Article | 27 March 2009 Paper

Image segmentation using joint spatial-intensity-shape features: application to CT lung nodule segmentation

Xujiong Ye, Musib Siddique, Abdel Douiri, Gareth Beddoe, Greg Slabaugh

Proceedings Volume 7259, 72594V (2009) https://doi.org/10.1117/12.811151

KEYWORDS: Image segmentation, Expectation maximization algorithms, Lung, Computed tomography, Medical imaging, Image processing, Microelectromechanical systems, Image processing algorithms and systems, Tissues, Blood vessels

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Proceedings Article | 27 March 2009 Paper

Enhanced detection in CT colonography using adaptive diffusion filtering

Abdel Douiri, Musib Siddique, Xujiong Ye, Gareth Beddoe, Greg Slabaugh

Proceedings Volume 7259, 725923 (2009) https://doi.org/10.1117/12.811563

KEYWORDS: Diffusion, Computer aided diagnosis and therapy, Digital filtering, Gaussian filters, Data modeling, Image segmentation, Smoothing, Medical imaging, Computed tomography, Colon

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Proceedings Article | 26 March 2007 Paper

Fully automatic segmentation of liver from multiphase liver CT

Yalin Zheng, Xiaoyun Yang, Xujiong Ye, Xinyu Lin

Proceedings Volume 6512, 65122X (2007) https://doi.org/10.1117/12.709711

KEYWORDS: Liver, Image segmentation, Computed tomography, Diffusion, Liver cancer, Algorithm development, Binary data, Abdomen, Image processing algorithms and systems, Computer aided diagnosis and therapy

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Showing 5 of 11 publications

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