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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

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A large dataset with CNN-based segmentation analysis for land cover from large river basins 16

A large dataset with CNN-based segmentation analysis for lan...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Xiao, Chenxin Liu, Lanyu Xie, Guopan Xu, Tao University of Jinan School of Information Science and Engineering Jinan China Shandong GEO-Surveying & Mapping Institute Jinan China Shandong Provincial Territorial Spatial Ecological Restoration Center Jinan China

ISBN: (纸本)9798350330755

Land cover and segmentation play an important role to ensuring the safety of people's lives and property from floods in large river basins. Large river basins are generally located in rural and urban areas, with different distribution and diversity of feature classes. However, most of the current remote sensing image segmentation algorithms focus on general targets, for large basin specific objects such as dams and river beaches, algorithms trained on general datasets have poor performances. For land cover task in large river basins, a land segmentation dataset has been proposed, including eight categories of large river basin at sub-meter spatial resolution. The experimental results show that the dataset can effectively support the training of the large river basin segmentation algorithms. © 2023 IEEE.

关键词： Remote sensing

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A Novel Classification of Macular Split based on Multi-modal Fusion of SLO and OCT 16

A Novel Classification of Macular Split based on Multi-modal...

引用

16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Wang, Yihuan Qin, Peiwu Li, Zhide Dong, Yuhan Tsinghua University Shenzhen International Graduate School Shenzhen China

ISBN: (纸本)9798350330755

Myopia has emerged as a prevalent ocular concern worldwide. Among its complications, pathological myopia stands out as one of the primary causes of blindness, with myopic maculopathy being particularly problematic. To enhance the accuracy of classifying lesions in the macular region, this study introduces a multimodal feature fusion approach that integrates both OCT and SLO images. This method employs a dual-branch ResNet-50 architecture, the atrous spatial pyramid pooling (ASPP) module, squeeze and excitation block (SE) attention mechanism, and a cross-attention fusion module for comprehensive feature extraction and fusion. Experimental results validate the superiority of the proposed method superior in ophthalmic image classification tasks. Specifically, it has achieved an accuracy rate of 93.92% in differentiating normal eyes and macular split in Task 1, and 89.92% accuracy in the four-category classification of Task 2, which includes inner layer hole, outer layer hole, inner and outer layer hole and full-thickness macular hole. © 2023 IEEE.

关键词： Optical tomography

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Segmentation and Recognition of Lung Adenocarcinoma Cells Based on U-Net Model 16

Segmentation and Recognition of Lung Adenocarcinoma Cells Ba...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Jin, Ziyang Zhang, Qing Sun, Zhen Li, Qingli Zhejiang University ZJU-UIUC Institute Department of Electrical Engineering Haining China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing Shanghai China Four Districts for Convalescence of Air Force Hangzhou Special Service Recuperation Center Nanjing China

ISBN: (纸本)9798350330755

Lung adenocarcinoma poses a great threat to human health, and early diagnosis is very important for treatment. Currently, pathologists analyze and diagnose pathological cells by observing their distribution (normal cells, hyperplasia and cancer cells). So, accurate segmentation of lung cells is very important to help pathologists make diagnosis. However, it is a heavy workload to obtain information from whole slide images by human eye observation. And with the development of deep learning, its application in medical image is increasing. We can segment and recognize cells based on this technology. U-Net model, one of the most classic segmentation models, has obtained enormous number of achievements in pathological image processing. Therefore, in this paper, we propose a segmentation model for lung adenocarcinoma cells based on U-Net model. This model is trained by synthesizing pseudo-color images generated from three bands of hyperspectral images as input. We have conducted experiments on a home-made lung adenocarcinoma dataset and the results show that this method can get precise segmentation results. © 2023 IEEE.

关键词： Diagnosis

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Low-light image enhancement based on U-Net and fusion strategy 16

Low-light image enhancement based on U-Net and fusion strate...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Yang, Zenghui Liu, Haoran Ju, Mingye Zhang, Lin Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing China

ISBN: (纸本)9798350330755

Normal light images are a prerequisite for many visual tasks, making single image enhancement crucial in computer vision. While physical model-based approaches have demonstrated promising results in this domain at an early stage, they often produce undesired anomalies in their outputs, making it difficult to apply them universally across all scenarios. Conversely, learning-based methods yield more authentic outcomes. However, the enhancement capabilities of machine learning algorithms are limited by the lack of pairs of normal light images, and many algorithms focus on a single enhancement task, ignoring the role of high-level tasks in guiding enhancement. This paper introduces a low-light image enhancement algorithm rooted in a fusion strategy that consists of two subtasks, visibility restoration and realism improvement tasks, and combines the advantages of physical modeling and learning methods. In the visibility restoration stage, the fusion of features from upstream and downstream tasks enables our network to take full advantage of the guidance of the upstream task to the downstream task to recover results closer to nature;in the fidelity enhancement stage, the Retinex enhancer and the refinement enhancement network are introduced to enhance the fidelity of the image. Finally, the results of the two subtasks are fused to obtain the output. Experiments substantiate that the algorithm excels in terms of visual effects and objective metrics across both synthetic data and real-life scenes. © 2023 IEEE.

关键词： image enhancement

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Pixel-Level Segmentation of Handwritten and Printed Texts in Document images with Deep Learning 16

Pixel-Level Segmentation of Handwritten and Printed Texts in...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Liu, Danjing Zhou, Sun Xiamen University Department of Automation Xiamen China

ISBN: (纸本)9798350330755

Handwritten and printed texts are often mixed in some document images such as test papers, prescriptions, historical manuscripts, etc. It is essential to differentiate between handwritten and printed texts within images when performing tasks related to layout analysis, character recognition, and transcription of historical documents. Most of the previous approaches to this segmentation problem need a priori knowledge of the page layout to accurately separate characters, lines, or other text units. They are therefore not well suited to variable layouts. To overcome the requirement of a priori knowledge, this paper presents a pixel-level approach that introduces a deep learning model to distinguish between handwritten and printed texts. Considering that many deep learning models tend to lose valuable information of details, e.g., handwritten texts between the lines of printed texts, small texts, we choose the U-Net model to better preserve those fine-grained features. In addition, a few specific image enhancement techniques are employed. Median filtering is used on the original image to remove background shadows, resulting in an improved image quality;a connected component analysis algorithm is applied to the U-Net segmentation outcomes, enhancing the consistency and clarity of strokes of the recognized texts. Experimental evaluations were conducted on a dataset of test paper images. Among five popular semantic segmentation models, our segmentation approach proves to be the most effective, achieving a high Mean Intersection over Union (MIoU) score of 97.54%. © 2023 IEEE.

关键词： image enhancement

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MIPR: Automatic Segmentation Annotation of Medical images via Generative model 16

MIPR: Automatic Segmentation Annotation of Medical Images vi...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Zhu, Haiming Dai, Pingping Wang, Hao Qian, Xiang Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen China

ISBN: (纸本)9798350330755

Most state-of-the-art semantic segmentation methods in the medical domain rely on fully supervised deep learning. However, creating high-quality annotated datasets demands extensive labor and domain expertise, leading to significant time and cost investments. While previous approaches have explored semi-supervised and unsupervised learning to alleviate the scarcity of annotated data by leveraging unlabeled samples and achieving promising results, they still fall short of replicating the nuanced annotations provided by medical professionals. In this paper, drawing inspiration from self-training techniques in semi-supervised learning, we propose an innovative approach to address the issue of limited annotated data, called medical images pixel rearrangement. Our method combines image editing with the pseudo-label methodology to generate labeled image pairs. Our method supports iterative training, and with each iteration, the edited images progressively resemble the originals, and the resulting annotations align more closely with the annotations made by medical experts. This novel process allows us to derive labeled pairs of data directly from the pool of unlabeled data. The methodology is implemented through a purpose-built conditional generative model and a dedicated segmentation network. Experimental results on the ISIC18 dataset demonstrate that the annotation quality achieved by our segmentation method is not only comparable to, but potentially surpasses the quality of annotations provided by medical experts. © 2023 IEEE.

关键词： Iterative methods

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The Segmentation of Brain Tumor from MR images Based on U-Net++ Network and Attention Mechanism 16

The Segmentation of Brain Tumor from MR Images Based on U-Ne...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Wang, Yaqi Zhang, Hanzhi Ren, Hongjin Xiao, Liang Beijing University of Chemical Technology College of Information Science and Technology Beijing China

ISBN: (纸本)9798350330755

Glioma is a malignant brain tumor that seriously endangers people's lives. Automatic segmentation of brain tumor from magnetic resonance (MR) images plays an important role in accurately diagnosing this disease and formulating corresponding treatment plans. Due to the problems of blurred field of view and unclear image edges in MR images of brain tumors, segmentation using separate U-Net or U-Net++ networks often does not yield accurate results. In this paper, we propose a segmentation model that effectively combines residual module, multiscale deep supervision, convolutional block attention module (CBAM) and U-Net++, named improved CBAM-U-Net++. It can capture features at different levels and dimensions of the feature image, and at the same time circumvents the problem of the encoder degradation during the convolution process. The use of multimodal MR image data can overcome the detection limitations of unimodal, fully take into account the relevant information of the lesion region, and improve the accuracy of segmentation. Experimental results on the BraTS2020 dataset show that the proposed model can further improve the accuracy of brain tumor segmentation compared to U-Net or U-Net++ networks. © 2023 IEEE.

关键词： image segmentation

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A Handheld Reader with Optimized image Analysis for HIV Lateral Flow Test 16

A Handheld Reader with Optimized Image Analysis for HIV Late...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Dong, Xiaobin Zhu, Rui Yang, Wenbo Zhang, Lulu Qiu, Xianbo Dong, Shenghua Zhang, Xiaolei Ge, Shengxiang Xia, Ningshao Beijing University of Chemical Technology Beijing China Beijing Wantai Biological Pharmacy Enterprise Co.Ltd Beijing China Xiamen University Xiamen China

ISBN: (纸本)9798350330755

As a rapid, simple and low-cost diagnostic technology, immunochromatography has a wide range of application in point-of-care testing (POCT). Here, a handheld reader based on image analysis was developed for the detection of HIV in serum or whole blood sample using gold colloid lateral flow strips. The control and test lines from the image of the strip collected by the reader were extracted with an image analysis algorithm including image preprocessing (image crop, grayscale of image, image equalization, median filter and image denoise) and feature extraction. Based on the preprocessed image, the control line was located based on its color difference from the background, and the test line was accurately identified with both preliminary searching and precise boundary location. The performance of the reader was evaluated by experiments using a series of different concentration of HIV serum samples. Comparing to the commercial reader, comparable performance could be achieved with the handheld reader with significantly reduced size. © 2023 IEEE.

关键词： image analysis

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Radiation Safety in Nuclear Power Plants: ResNet-Based Glove image Classification 16

Radiation Safety in Nuclear Power Plants: ResNet-Based Glove...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Xiong, Kouhong Xu, Siyuan Shen, Enwei Ding, Zhengyan Zhao, Rui Nuclear Power Operations Research Institute Shanghai China East China Normal University Shanghai China The Third Research Institute of the Ministry of Public Shanghai China

ISBN: (纸本)9798350330755

Ensuring the safety of personnel working in nuclear power plants remains a paramount concern, with a primary focus on mitigating radiation exposure risks. An essential safety measure involves monitoring and verifying that employees are wearing the appropriate gloves while engaged in their tasks. This study aims to design an advanced algorithm capable of accurately identifying the glove status of personnel upon their entry into the workspace by analyzing photographs taken at the entrance. Our classification system includes six categories: false detection, indeterminate, latex glove, white glove, red glove, and no glove. To enhance data diversity and training efficacy, we employ the mixup technique in data preprocessing. Our model of choice is a 50-layer residual network, and we utilize meticulously designed training methods for optimal results. Extensive data validation demonstrates the effectiveness of our approach, achieving an impressive accuracy rate of 92.84%. This research presents a valuable tool for enhancing radiation safety measures within nuclear power plants, with the potential to significantly reduce accident risks and uphold the well-being of plant personnel. © 2023 IEEE.

关键词： Nuclear power plants

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Exploration of a Novel Method for Thyroid Nodule Ultrasound image Segmentation 17

Exploration of a Novel Method for Thyroid Nodule Ultrasound ...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： He, Kai Li, Yuan Li, Ting Sun, Xiao Wu, Mei Guo, Wei Zhao, Xian Liu, Zhi Shandong University Key Laboratory of Laser and Infrared System of the Ministry of Education Qingdao266237 China The Second Hospital Cheeloo College of Medicine Shandong University Department of Thoracic Surgery Jinan250012 China Qilu Hospital of Shandong University Department of Ultrasound Jinan250012 China School of Software Shandong University Jinan250101 China School of Information Science and Engineering Shandong University Qingdao266237 China

ISBN: (纸本)9798331507398

Ultrasound examination of thyroid nodules is currently the mainstream method for diagnosing thyroid diseases. Segmentation of thyroid nodule ultrasound images is a cru-cial task in computer-aided diagnosis of thyroid nodules and holds significant importance in the entire diagnostic process. However, due to the complex features and low contrast of thyroid ultrasound images, traditional semantic segmentation algorithms struggle to extract effective features, making it dif-ficult to accurately identify the nodule regions and complete the segmentation task. To address these issues and advance research in thyroid nodule segmentation, this paper proposes a deep learning model for accurate identification and high-quality segmentation of nodule regions in thyroid nodule segmentation. To demonstrate the effectiveness of our method, we collected ultrasound images from clinical examinations at the Second Hospital of Shandong University to serve as our experimental dataset. Evaluations conducted on this dataset show that our method achieves outstanding performance compared to existing mainstream algorithms. © 2024 IEEE.

关键词： Ultrasonic imaging

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