Prof. Su Ruan Profile

Prof. Su Ruan

at Laboratoire LITIS

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Publications (8)

SPIE Journal Paper | 8 January 2022

Segmentation of multicorrelated images with copula models and conditionally random fields

Jérôme Lapuyade-Lahorgue, Su Ruan

JMI, Vol. 9, Issue 01, 014001, (January 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.1.014001

KEYWORDS: Image segmentation, Tumors, Magnetic resonance imaging, Data modeling, Brain, Binary data, Image fusion, Neuroimaging, Fuzzy logic, Data fusion

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Purpose: Multisource images are interesting in medical imaging. Indeed, multisource images enable the use of complementary information of different sources such as for T1 and T2 modalities in MRI imaging. However, such multisource data can also be subject to redundancy and correlation. The question is how to efficiently fuse the multisource information without reinforcing the redundancy. We propose a method for segmenting multisource images that are statistically correlated. Approach: The method that we propose is the continuation of a prior work in which we introduce the copula model in hidden Markov fields (HMF). To achieve the multisource segmentations, we use a functional measure of dependency called “copula.” This copula is incorporated in the conditionally random fields (CRF). Contrary to HMF, where we consider a prior knowledge on the hidden states modeled by an HMF, in CRF, there is no prior information and only the distribution of the hidden states conditionally to the observations can be known. This conditional distribution depends on the data and can be modeled by an energy function composed of two terms. The first one groups the voxels having similar intensities in the same class. As for the second term, it encourages a pair of voxels to be in the same class if the difference between their intensities is not too big. Results: A comparison between HMF and CRF is performed via theory and experimentations using both simulated and real data from BRATS 2013. Moreover, our method is compared with different state-of-the-art methods, which include supervised (convolutional neural networks) and unsupervised (hierarchical MRF). Our unsupervised method gives similar results as decision trees for synthetic images and as convolutional neural networks for real images; both methods are supervised. Conclusions: We compare two statistical methods using the copula: HMF and CRF to deal with multicorrelated images. We demonstrate the interest of using copula. In both models, the copula considerably improves the results compared with individual segmentations.

Proceedings Article | 15 February 2021 Presentation + Paper

Deep learning-based multi-class COVID-19 classification with x-ray images

Zong Fan, Shenghua He, Su Ruan, Xiaowei Wang, Hua Li

Proceedings Volume 11598, 1159813 (2021) https://doi.org/10.1117/12.2582261

KEYWORDS: Image classification, X-rays, X-ray imaging, Chest imaging, Convolutional neural networks, Radiography, Network architectures

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The COVID-19 pandemic continues spreading rapidly around the world and has caused devastating outcomes towards the health of the global population. The reverse transcription-polymerase chain reaction (RT-PCR) test, as the only current gold standard for screening infected cases, yields a relatively high false positive rate and low sensitivity on asymptomatic subjects. The use of chest X-ray radiography (CXR) images coupled with deep- learning (DL) methods for image classification, represents an attractive adjunct to or replacement for RT-PCR testing. However, its usage has been widely debated over the past few months and its potential effectiveness remains unclear. A number of DL-based methods have been proposed to classify the COVID-19 cases from the normal ones, achieving satisfying high performance. However, these methods show limited performance on the multi-class classification task for COVID-19, pneumonia and normal cases, mainly due to two factors: 1) the textures in COVID-19 CXR images are extremely similar to that of pneumonia cases, and 2) there are much fewer COVID-19 cases compared to the other two classes in the public domain. To address these challenges, a novel framework is proposed to learn a deep convolutional neural network (DCNN) model for accurately classifying COVID-19 and pneumonia cases from other normal cases by the use of CXR images. In addition to training the model by use of conventional classification loss which measures classification accuracy, the proposed method innovatively employs a reconstruction loss measuring image fidelity and an adversarial loss measuring class distribution fidelity to assist in the training of the main DCNN model to extract more informative features to support multi-class classification. The experiment results on a COVID-19 dataset demonstrate the superior classification performance of the proposed method in terms of accuracy compared to other existing DL-based methods. The experiment on another cancer dataset further implies the potential of applying the proposed methods in other medical imaging applications.

SPIE Journal Paper | 10 January 2019

Multiorgan segmentation using distance-aware adversarial networks

Roger Trullo, Caroline Petitjean, Bernard Dubray, Su Ruan

JMI, Vol. 6, Issue 01, 014001, (January 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.014001

KEYWORDS: Image segmentation, Computed tomography, Esophagus, Heart, Gallium nitride, Aorta, Binary data, Medical imaging, Network architectures, Lung

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Proceedings Article | 24 June 2005 Paper

Brain tumor segmentation in MRI based on fuzzy aggregators

Yan Zhu, Qingmin Liao, Weibei Dou, Su Ruan

Proceedings Volume 5960, 596050 (2005) https://doi.org/10.1117/12.633220

KEYWORDS: Tumors, Image segmentation, Brain, Image fusion, Magnetic resonance imaging, Information fusion, Data fusion, Fuzzy logic, Pathology, Neuroimaging

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Proceedings Article | 28 May 2004 Paper

Segmentation based on information fusion applied to brain tissue on MRI

Weibei Dou, Su Ruan, Daniel Bloyet, Jean-Mac Constans, Yanping Chen

Proceedings Volume 5298, (2004) https://doi.org/10.1117/12.526789

KEYWORDS: Tissues, Fuzzy logic, Image segmentation, Tumors, Brain, Magnetic resonance imaging, Image fusion, Neuroimaging, Information fusion, Space operations

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Proceedings Article | 18 January 2004 Paper

Possibilistic-clustering-based MR brain image segmentation with accurate initialization

Qingmin Liao, Yingying Deng, Weibei Dou, Su Ruan, Daniel Bloyet

Proceedings Volume 5308, (2004) https://doi.org/10.1117/12.526800

KEYWORDS: Image segmentation, Brain, Magnetic resonance imaging, Neuroimaging, Tissues, Tumors, Fourier transforms, Image processing, Magnetism, Image analysis

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Proceedings Article | 25 September 2003 Paper

An improved image segmentation approach based on level set and mathematical morphology

Hua Li, Abderrahim Elmoataz, Jaral Fadili, Su Ruan

Proceedings Volume 5286, (2003) https://doi.org/10.1117/12.538710

KEYWORDS: Image segmentation, Image processing algorithms and systems, Medical imaging, Digital imaging, 3D image processing, Image processing, Mathematical morphology, Convolution, Interfaces, Liver

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Proceedings Article | 31 July 2002 Paper

Automatic brain tumor extraction using fuzzy information fusion

Weibei Dou, Qingmin Liao, Su Ruan, Daniel Bloyet, Jean-Mac Constans, Yanping Chen

Proceedings Volume 4875, (2002) https://doi.org/10.1117/12.477203

KEYWORDS: Tumors, Brain, Fuzzy logic, Image fusion, Magnetic resonance imaging, Tissues, Information fusion, Neuroimaging, Image registration, Image segmentation

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Showing 5 of 8 publications

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