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International Conference on Bioinformatics and Biomedical Engineering

作者： Zhang, Shao-Min Zhi, Li-Jia Zhao, Da-Zhe Zhao, Hong College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China Key Laboratory of Medical Image Computing Northeastern University Ministry of Education Shenyang Liaoning 110004 China

ISBN: (纸本)9781424450893

In this paper, we propose a novel registration algorithm based on minimal spanning tree. First, we extracted uniform sub-sampling points from image. Second, based on the feature points, in addition to using pixel intensity, we also added region based feature to include more spatial information. The proposed method is evaluated by performing registration experiments on BrainWeb database. The experimental results show that the proposed method achieves better robustness while maintaining good registration accuracy, compared to the conventional normalized mutual information (NMI) based registration method. © 2011 IEEE.

关键词： image registration

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Noninvasive prediction of pulmonary hypertension based on finite element analysis and machine learning

Noninvasive prediction of pulmonary hypertension based on fi...

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Zhao, Shuo Zhang, Haoran Wang, Dongyang Xu, Lisheng Qi, Lin School of Sino-Dutch Biomedical and Information Engineering Northeastern University Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110169 China

ISBN: (纸本)9781538680698

Geometry morphological features (GMF) of the ventricles were often used to predict the pulmonary hypertension (PH) in the clinical process. This study analyzed the relationship between right ventricular pressure (RVP) and ventricular morphological changes in simplified statistical shape models which were analyzed with finite element analysis method, and the RVP higher than 40 mmHg was adopted as a criterion to determine the presence of PH. Ten GMF features were utilized and three classifiers (decision tree, SVM and random forest) were performed to predict PH and achieved recognition accuracy of 90.7%, 91.4% and 93.5%, respectively. Through this study, the PH can be identified by morphometric features measured from ventricular images. © 2018 IEEE.

关键词： Finite element method

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The Effect of the Number of Sensor Coils on Sensitivity in Electromagnetic Tomography 4

The Effect of the Number of Sensor Coils on Sensitivity in E...

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4th International Symposium on image computing and Digital Medicine, ISICDM 2020

作者： Xu, Siqi Zhao, Qian Li, Liu Li, Nan Tan, Wenjun College of Physical Science and Technology Shenyang Normal University Shenyang China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China

ISBN: (纸本)9781450389686

Electromagnetic tomography (EMT) is a process tomography based on the principle of electromagnetic induction. It has the advantages of non-intrusive, non-contact and low harm. In the process of image reconstruction, the sensitivity matrix reflects the influence of tiny disturbance on the measured value, which is an important index of image quality. Base on the traditional "O"sensor array structure in this article, Comsol Multiphysics finite element simulation software is used to divide the measured field into several tiny elements. A simulated perturbation method is used to investigate the effect of the number of EMT sensors' midline coils on sensitivity. The sensitivity matrix of sensor arrays with 4 coils ,8 coils ,12 coils ,16 coils and 20 coils is compared by using the method of separation variables. The sensitivity matrix of different coils is imaged by Matlab software. Through comparative analysis, it is concluded that the sensitivity band shape of the coil in the same position is same in different models. All of them are distributed along the arc type between the excitation and detection coils, which presents the middle strong and gradually weakens to the outside. With the increase of the number of coils, the sensitivity band is widened, but the sensitivity value of the central part is slightly weakened. According to this characteristic, it lays a foundation for the practical application of EMT technology and image reconstruction. © 2020 Owner/Author.

关键词： image reconstruction

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A Pulmonary Artery-Vein Separation algorithm based on the relationship between sub-trees 4

A Pulmonary Artery-Vein Separation algorithm based on the re...

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4th International Symposium on image computing and Digital Medicine, ISICDM 2020

作者： Tan, Wenjun Li, Xiaoshuo Liu, Pan Tan, Wenjun College of Medicine and Biological Information Engineering Northeastern University Shenyang110016 China College of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China

ISBN: (纸本)9781450389686

This article presents a combined algorithm of pulmonary artery-vein (A/V) separation considering both global and local information, including: the transformation of geometric graph, sub-tree separation, and A/V classification. During the process, geometric graphs are firstly built based on the centerlines;A separation of the adhesion points is performed by applying the information stored in the data structure of the nodes and links in the geometric graphs;Sub-trees are then combined according to the local and global information;The combined sub-trees are growing into vascular trees using 2D area growth method;And finally, classification of the arterial and venous vascular trees based on the knowledge of anatomy. An average accuracy of 85% has been achieved of the overall process with a processing time range between 40 s to 50 s, which indicates good performance and stability of this algorithm. © 2020 ACM.

关键词： Separation

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Automated Thalamus Segmentation in MR images Using Convolutional Networks

Automated Thalamus Segmentation in MR Images Using Convoluti...

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IEEE International Conference of Safety Produce Informatization (IICSPI)

作者： Zhaoxuan Gong Chuang Sun Wei Guo Wenjun Tan Wei Zhou Guodong Zhang Computer Science Shenyang Aerospace University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Computer Science Shenyang Aerospace University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (数字)9781728177380

ISBN: (纸本)9781728177397

Thalamus segmentation in Magnetic Resonance image is a challenging task because of the high inter-patient anatomical variability in both shape and size between patients. In this work, we proposed a fast and robust segmentation of thalamus using convolutional neural networks. First, cropped image which included the thalamus are extracted. Convolutional neural network (CNN) is then trained to segment the whole thalamus region. Intensity constrained model is constructed to obtain the final segmentation. Our method performs better than other state-of-the-art methods. The experiment results show that our approach achieves the highest dice scores than other methods.

关键词： image segmentation Shape Production Thalamus Convolutional neural networks Pancreas Task analysis

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Pseudo Multi-Source Domain Extension and Selective Pseudo-Labeling for Unsupervised Domain Adaptive medical image Segmentation 48

Pseudo Multi-Source Domain Extension and Selective Pseudo-La...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Xiaokang Wang, Zhiqiang Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Xiangnan University College of Medical Imaging Laboratory and Rehabilitation Chenzhou423000 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781728163277

Unsupervised domain adaptation (UDA) attracts extra attention in medical image processing because no additional labels are required when adapting to different distributions. In this work, we propose a novel unsupervised domain adaptation framework named as Domain Expansion and PseudoLabeling (DEPL). We extend the domain of a labeled source domain data to four different distributed domains and use adversarial learning to align the image appearance level and feature level from the four different domains to the unlabeled target domain. In addition, we propose a selective pseudolabeling mechanism, namely using strong confidence pseudolabeling to boost model performance. We evaluate our model for the MR to CT adaptation segmentation task on the public dataset MMWHS. Compared to seven other state-of-the-art segmentation methods, our DEPL achieves the best Dice similarity coefficient by 82.4%, which is at least 3.9% higher than the other UDA segmentation methods. © 2023 IEEE.

关键词： Adversarial learning domain extension image segmentation Selective pseudo-labeling Unsupervised domain adaptive

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Automated Segmentation of Lateral Ventricle in MR images Using Multi-scale Feature Fusion Convolutional Neural Network 25th

Automated Segmentation of Lateral Ventricle in MR Images Usi...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Ye, Fei Wang, Zhiqiang Hu, Kai Zhu, Sheng Gao, Xieping Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China College of Medical Imaging and Inspection Xiangnan University Chenzhou423000 China

ISBN: (纸本)9783030687793

Studies have shown that the expansion of the lateral ventricle is closely related to many neurodegenerative diseases, so the segmentation of the lateral ventricle plays an important role in the diagnosis of related diseases. However, traditional segmentation methods are subjective, laborious, and time-consuming. Furthermore, due to the uneven magnetic field, irregular, small, and discontinuous shape of every single slice, the segmentation of the lateral ventricle is still a great challenge. In this paper, we propose an efficient and automatic lateral ventricle segmentation method in magnetic resonance (MR) images using a multi-scale feature fusion convolutional neural network (MFF-Net). First, we create a multi-center clinical dataset with a total of 117 patient MR scans. This dataset comes from two different hospitals and the images have different sampling intervals, different ages, and distinct image dimensions. Second, we present a new multi-scale feature fusion module (MSM) to capture different levels of feature information of lateral ventricles through various receptive fields. In particular, MSM can also extract the multi-scale lateral ventricle region feature information to solve the problem of insufficient feature extraction of small object regions with the deepening of network structure. Finally, extensive experiments have been conducted to evaluate the performance of the proposed MFF-Net. In addition, to verify the performance of the proposed method, we compare MFF-Net with seven state-of-the-art segmentation models. Both quantitative results and visual effects show that our MFF-Net outperforms other models and can achieve more accurate segmentation performance. The results also indicate that our model can be applied in clinical practice and is a feasible method for lateral ventricle segmentation. © 2021, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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Segmentation of pulmonary vessels based on MSFM method 22

Segmentation of pulmonary vessels based on MSFM method

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Wang, Xiaowei Cheng, Liying Huang, Danyang Gao, Xuanshuang Liang, Daili He, Liuye Zhang, Zhimei Li, Nan Tan, Wenjun School of Physical Science and Technology Shenyang Normal University Shenyang110034 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110189 China

ISBN: (纸本)9781728162676

Accurate segmentation of pulmonary blood vessels from CT images is of great significance for lung disease detection and segmentation of other lung structures. Manual segmentation is difficult to accurately segment vascular tissue for various reasons. Therefore, in view of the existing problems and shortcomings of the existing lung vessel segmentation method, a more efficient lung vessel segmentation algorithm is proposed, that is, the multi-template fast marching method (MSFM algorithm). Firstly, it used the hole filling and maximum inter-class variance algorithm for preprocess. In the process, the lung parenchyma is extracted from the chest CT, and then the lung blood vessels are extracted in the lung parenchymal area using the MSFM algorithm. In the extraction process, the threshold and gradient are used to limit the progress of the process and the lung blood vessels are more accurately segmented. Through experimental verification, the accuracy of lung blood vessel segmentation based on MSFM algorithm is improved. © 2021 IEEE.

关键词： Blood vessels

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An adaptive clustering algorithm of gaussian curve fitting the histogram based on K-means

An adaptive clustering algorithm of gaussian curve fitting t...

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2019 IEEE International Conferences on Ubiquitous computing and Communications and Data Science and Computational Intelligence and Smart computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Ma, Guangkun Yang, Jinzhu Song, Xiaoxu Zhao, Hong School of Computer Science and Engineering Northeastern University Shenyang China School of Software Shenyang University of Technology Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781728152097

The K-mean algorithm is simple and efficient in vascular segmentation. But in a K-means clustering algorithm, the number of clustering centers must be determined in advance and the random allocation of clustering centers seriously affects the efficiency of the algorithm. In order to solve this problem, an adaptive clustering algorithm based on K-means with the Gaussian curve fitting histogram is proposed, which is used to segment circular-like blood vessels. Firstly, the histogram outline is fitted into the Gaussian curve by the least square method and the maximum value of the curve is obtained. The number of maximal values is counted as the number of clustering centers and the maximum points are used as the central points of clustering. Also, the K-means algorithm is improved by combining the intensity and position information of pixels. The proposed method has been tested by 6 groups of medical images. The algorithm has the advantages of less iterations, fast convergence speed and getting more than 95% Dice Similarity Measure value. The algorithm can effectively solve the determination of multi-seed points and vascular segmentation in medical image segmentation. © 2019 IEEE.

关键词： K-means clustering

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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