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International Conference on image, Vision and intelligent Systems, ICIVIS 2023

作者： Zhang, Wenbo Sun, Xiaolin Ma, Shiyi Yang, Jinzhu Grzegorzek, Marcin Wang, Yuexi Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Department of Rehabilitation Shengjing Hospital of China Medical University Shenyang China

ISBN: (纸本)9789819708543

Skin cancer is a type of cancer and has emerged as one of the most prevalent types over the past decade. Early diagnosis and treatment are a key factor in the treatment of skin cancer. The traditional clinical diagnosis method of skin cancer is dermoscopy. On account of the complexity of the skin image, occasional instances of missed diagnosis and misdiagnosis arise, leading to delayed optimal treatment for patients. Two different networks, Inception-V3 and ResNet-50, are put forward in this paper. The dataset is separated into two categories, and the network performance is evaluated using evaluation indicators. Findings indicate that the ResNet50 network attains a higher accuracy rate of 88.83% compared to the Inception-V3 network's 83.17% accuracy. Additionally, the other three indicators of ResNet-50 are also surpass those of Inception-V3. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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Level Set Based on Signed Pressure Force Function and Its Application in Liver image Segmentation

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Wuhan University Journal of Natural Sciences 2011年第3期16卷 265-270页

作者： JIANG Huiyan1,2,FENG Ruijie2,3,GAO Xihe1,2 1.Software College,Northeastern University,Shenyang 110819,Liaoning,China 2.key laboratory of medical image computing of ministry of education Northeastern University,Shenyang 110819,Liaoning,China 3.Sino-Dutch Biomedical and Information Engineering School,Northeastern University,Shenyang 110819,Liaoning,China Software College Northeastern University Shenyang Liaoning China Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University Shenyang Liaoning China Sino-Dutch Biomedical and Information Engineering School Northeastern University Shenyang Liaoning China

The edge-based level set model gives no satisfactory results for images with weak edge, and the region-based model performs poorly for intensity inhomogeneity images. In this paper, we propose an improved region-based level set model that integrates both the gradient information and the region information. The proposed model defines a novel external energy term, which consists of gradient information and signed pressure forces function. In order to eliminate the re-initialization procedure of traditional level set model, an internal energy term is also introduced for the level set function to maintain signed distance function. Compared with traditional models, our model is more robust against images with weak edge and intensity inhomogeneity. Experiments on liver segmentation from abdominal CT images demonstrate the effectiveness and accuracy of the proposed method.

关键词： segmentation level set signed pressure force liver

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A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features

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Computers in Biology and Medicine 2024年 174卷 108461-108461页

作者： Diao, Zhaoshuo Jiang, Huiyan Software College Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

Background: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset. Method: This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates

关键词： Classification (of information)

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A Pulmonary Vascular Extraction Algorithm from Chest CT/ CTA images

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Journal of Healthcare Engineering 2021年第1期2021卷 5763177页

作者： Xu, Shihui Zhang, Ziming Zhou, Qinghua Shao, Wei Tan, Wenjun Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China College of Computer Science and Engineering Northeastern University Shenyang110189 China

Segmentation of pulmonary vessels in CT/CTA images can help physicians better determine the patient’s condition and treatment. However, due to the complexity of CT images, existing methods have limitations in the segmentation of pulmonary vessels. In this paper, a method based on the separation of pulmonary vessels in CT/CTA images is investigated. The method is divided into two steps: in the first step, the lung parenchyma is extracted using the Unet++ algorithm, which can effectively reduce the oversegmentation rate;in the second step, the pulmonary vessels in the lung parenchyma are extracted using nnUnet. According to the obtained lung parenchyma segmentation results, the "AND" operation is performed on the original image and the lung parenchyma segmentation results, and only the blood vessels within the lung parenchyma are segmented, which reduces the interference of external tissues and improves the segmentation accuracy. The experimental data source used CT/CTA images acquired from the partner hospital. After the experiments were performed on a total of 67 sets of images, the accuracy of CT and CTA images reached 85.1% and 87.7%, respectively. The comparison of whether to segment the lung parenchyma and with other conventional methods was also performed, and the experimental results showed that the algorithm in this paper has high accuracy. Copyright © 2021 Shihui Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Blood vessels

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BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural Network for the Segmentation of Breast Ultrasound images

BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neu...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhang, Xiang Li, Xuanya Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781665401265

In this paper, we propose a novel convolutional neural network based on boundary-guided and region-aware (BGRA-Net) for breast tumor segmentation in ultrasound images. In particular, in the encoding stage, we propose a boundary-guided module (BGM) to guide the learning of boundary features in the decoding stage by explicitly strengthening the extracted boundary information. Meanwhile, in the decoding stage, we propose a region-aware module (RAM) to integrate different levels of detailed and semantic features to improve the comprehensive representation of tumor regional features. Besides, a scale-adaptive module (SAM) is further proposed to capture the characteristics of tumors with different sizes between the encoding and decoding stages. To evaluate the effectiveness of our BGRA-Net, we conduct extensive experiments on the UDIAT dataset and compare it with eight state-of-the-art methods. The experimental results show that our BGRA-Net outperforms the state-of-the-art methods and can achieve accurate segmentation of breast tumors with ambiguous boundaries. © 2021 IEEE.

关键词： Tumors

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Research on preprocessing algorithm for PET-CT image registration

Research on preprocessing algorithm for PET-CT image registr...

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2010 International Conference on Optoelectronics and image Processing, ICOIP 2010

作者： Han, Fangfang Yang, Jinzhu Liu, Yang Zhao, Hong Sino-Dutch Biomedical and Information Engineer School Northeastern University China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University China Software Center Northeastern University Shenyang Liaoning 110179 China

ISBN: (纸本)9780769542522

PET and CT image registration is an important tool of clinical diagnosis of diseases. For PET and CT images, a preprocessing algorithm of medical image registration is proposed in this paper. The algorithm process includes image normalization, CT image adaptive threshold adjustment and automatic extraction of tissues based on morphology, edge detection and statistical analysis theory, and improved PET image interpolation based on physical spacing of pixels. The experiment results indicate that the preprocessing methods proposed in this paper have a great effect on PET and CT image registration research because of the exclusion of redundant information and the improvement of the registration algorithm efficiency. © 2010 IEEE.

关键词： image registration

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Research on Preprocessing Algorithm for PET-CT image Registration

Research on Preprocessing Algorithm for PET-CT Image Registr...

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International Conference on Digital Manufacturing and Automation (ICDMA)

作者： Fangfang Han Jinzhu Yang Yang Liu Hong Zhao Sino-Dutch Biomedical and Information Engineer School Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University Shenyang Liaoning China Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University Shenyang Liaoning China Key Laboratory of Medical Image Computing of Ministry of Education The Software Center Northeastern University Shenyang Liaoning China

关键词： Computed tomography Positron emission tomography image edge detection Interpolation image registration Morphology Humans

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Vertebra location-based multi-regional segmentation of liver CT images

Vertebra location-based multi-regional segmentation of liver...

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2010 International Conference on Biomedical Engineering and Computer Science, ICBECS 2010

作者： Jiang, Huiyan Zhang, Yanan Cheng, Qingshui Software College Northeastern University Shenyang 110819 China Key Laboratory of Medical Image Computing Northeastern University Ministry of Education Shenyang 110819 China Sino-Dutch College Northeastern University Shenyang 110819 China

ISBN: (纸本)9781424453153

Liver segmentation has always been difficult in CAD(Computer-aided diagnosis). Especially, in different layers of liver CT images there may be two or more regions, which add a more arduous problem to liver segmentation. To solve this problem, a new automatic segmentation method for multi-regional liver CT image is presented. That is, first using a preprocessing way based on multi-threshold, morphological methods as well as position information to segment the vertebra and then according to the information of a fixed vertebra location and liver, selecting several seeds for the rough segmentation of liver, meanwhile, using voting mechanism to solve the problem for the single seed falling into the trap. Finally, by using morphology to amend the rough segmentation result we could eliminate the problem of over-segmentation. Actual segmentation experiments of abdominal CT image verify the effectiveness of the algorithm proposed in this paper. ©2010 IEEE.

关键词： Location

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Niche improved particle swarm optimization on geometric constraint solving

Niche improved particle swarm optimization on geometric cons...

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12th International Conference on Computer-Aided Design and Computer Graphics, CAD/Graphics 2011

作者： Cao, Chunhong Tang, Chuan Zhao, Dazhe Zhang, Bin Han, ChunYan Collge of Information Science and Engineering Northeastern University Shenyang 110819 China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology 610059 China Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University Shenyang 110819 China

ISBN: (纸本)9780769544977

Geometric constraint problem can be transformed to an optimization problem. We can solve the problem with niche improved particle swarm. Classical particle swarm optimization is likely to be trapped into local minima as well as premature. A niche improved particle swarm optimization (NIPSO) based on niche theory was developed. After the update of the particle velocity and position, the outlier particle was identified in the NIPSO by comparing the niche number of every particle, with which the crossover and selection operators were employed sequent for those particles, whose personal best values were less than that of the outlier particle. The experiment shows that it can improve the geometric constraint solving efficiency and possess better convergence property than the compared algorithms. © 2011 IEEE.

关键词： Particle swarm optimization (PSO)

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Research on light field capturing and reconstruction algorithm based on wavelet base

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Journal of Information and Computational Science 2012年第16期9卷 4997-5004页

作者： Jiang, Huiyan Fang, Di Gao, Rui Software College of Northeastern University Shenyang China Key Laboratory of Medical Image Computing of Ministry of Education Shenyang China

In this paper, we propose a novel coded aperture and two associated algorithm for making up the defect of traditional aperture such as the depth of scene, quality and the limitation of dimension. In sampling, combining with the sampling characteristics of traditional aperture and the properties of wavelet transform, we design a coded aperture achieving multi-angle reuse acquisition of light field. Then we project the captured light field data to double tree discrete wavelet domain. For reconstruction, we propose to use the sparse characteristics of light field combine with the redundancy in both spatial and angle domain. Experimental results demonstrate the efficiency and quality of the proposed methods. © 2012 Binary Information Press.

关键词： Wavelet transforms

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