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7th IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Alimanov, Alnur Islam, Md Baharul Bahcesehir Univ Dept Comp Engn Istanbul Turkiye Florida Gulf Coast Univ Dept Comp & Software Engn Ft Myers FL USA

ISBN: (纸本)9798350351439;9798350351422

retinal images and vessel trees play a crucial role in aiding ophthalmologists to identify and diagnose various illnesses related to the eyes, blood vessels, and brain. However, manual retinal image segmentation is a laborious and highly skilled procedure, posing challenges in terms of both difficulty and time consumption. This study proposes a novel approach to retinal image segmentation, leveraging the Denoising Diffusion Probabilistic Model (DDPM) for precise performance. To our best knowledge, DDPM is being applied in this domain for the first time. Our approach incorporates a novel constraint to prevent DDPM from generating vessel structures that not present in the original retinal images during the segmentation process. Additionally, our model is not limited to the original DDPM size of 64 x 64 pixels. Instead, we train it to effectively segment images sized 256 x 256 pixels. This is a significant advancement since the original DDPM works exclusively with 64x64 image sizes and is primarily designed for generating random image samples. In our work, we address both limitations with a novel, efficient approach for accurate retinal image segmentation. A comprehensive evaluation of our methodology includes both quantitative and qualitative assessments. Our proposed method demonstrates competitive performance compared to state-of-the-art techniques, as indicated by both qualitative and quantitative scores. The source code of our method can be accessed at https://***/AAleka/DDPM-segmentation.

关键词： retinal image segmentation Denoising Diffusion Probabilistic Models Medical Imaging Machine Vision image Super-resolution

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IterNet plus plus : An improved model for retinal image segmentation by curvelet enhancing, guided filtering, offline hard-sample mining, and test-time augmenting

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IET image PROCESSING 2022年第13期16卷 3617-3633页

作者： Zhu, M. Zeng, K. Lin, G. Gong, Y. Hao, T. Wattanachote, K. Luo, X. Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Univ Foreign Studies Guangzhou Peoples R China South China Normal Univ Sch Comp Sci Guangzhou Peoples R China Guilin Univ Elect Technol Natl & Local Joint Engn Res Ctr Satellite Nav & L Guilin Peoples R China

In clinical medicine, the segmentation of blood vessels in retinal images is essential for subsequent analysis in clinical diagnosis. However, retinal images are often noisy and their vascular structure is relatively tiny, which poses significant challenges for vessel segmentation. To improve the performance of vessel segmentation, an improved model IterNet++ based on the architecture of IterNet is proposed. First, curvelet signal analysis is applied to enhance retinal images. Second, residual convolution (ResConv) blocks and guided filters are introduced to utilise the encoder features of previous iterations in the model to reduce overfitting. Third, offline hard-sample mining is used to improve segmentation performance by utilising training samples with low segmentation accuracy as many possible on a few-sample training set. In addition, a test-time augmentation method is applied to testing samples in test dataset during inference. Extensive experiments show that this model achieves Dice scores of 0.8313, 0.8277, and 0.8372 on DRIVE, CHASE-DB1, and STARE datasets, respectively, demonstrating the best performance compared with IterNet and other baseline models.

关键词： clinical medicine guided filters low segmentation accuracy convolutional neural nets retinal images clinical diagnosis testing samples feature extraction segmentation performance image segmentation utilising training samples test-time augmentation method blood vessels retinal image segmentation curvelet enhancing few-sample training set medical image processing signal analysis eye biomedical optical imaging improved model IterNet offline hard-sample mining image classification vessel segmentation

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Generation of Data for Training retinal image segmentation Models 10th

Generation of Data for Training Retinal Image Segmentation M...

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10th Biennial International Conference on Pattern Recognition and Machine Intelligence (PReMI)

作者： Bhuiya, Srinjoy Chakraborty, Suchandra Sadhukhan, Subhopriyo Mandal, Deba Prasad Bhandari, Dinabandhu Heritage Inst Technol Kolkata 700107 West Bengal India Indian Stat Inst Kolkata 700108 West Bengal India

ISBN: (纸本)9783031451690;9783031451706

Biomedical image segmentation requires pixel-wise labelling which is extremely time-consuming and the availability of additional training data is highly beneficial for training Deep Learning models. In addition to using Classical image augmentation, generative adversarial networks have been used to augment the training data. This work is an investigation of the usefulness of generated medical images for Deep Learning segmentation models. An attempt has been made to create a computer-generated retinal image segmentation dataset using various state-of-the-art image generative deep learning models and use that dataset to train a supervised image segmentation model. Our experiments demonstrate that the generated data can be used successfully for medical segmentation tasks and improves the model's performance over using classical augmentations.

关键词： retinal image segmentation image Generation image Augmentation image Translation

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Random color transformation for single domain generalized retinal image segmentation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期136卷

作者： Guo, Song Ji, Ke Xian Univ Architecture & Technol Coll Informat & Control Engn Xian 710055 Peoples R China Sias Univ Sch Telecommun & Intelligent Mfg Zhengzhou 451150 Peoples R China

Fundus examination, conducted through the analysis of retinal images, plays a pivotal role in aiding the diagnosis of ophthalmic diseases. Currently, deep learning models have been widely applied to retinal image analysis, specifically retinal image segmentation, including vessels, optic cups, lesions, etc., yielding promising outcomes. Nonetheless, the performance of these deep learning models experiences substantial degradation due to the domain shift between the distribution of the training images and unseen test images. In this paper, we focus on the challenging single-domain generalization (SDG), which aims to learn a generalized model on only one source domain, with the expectation that it performs well on unseen test domains. Our work is motivated by the observation that the main differences in retinal images from different domains primarily reside in their color variations, rather than changes in the shapes of objects. To this end, we present random color transformation (RCT) for SDG. RCT performs random linear transformations to each color channel of the training image. Through this approach, RCT can generate training images with rich and vibrant color representations, while preserving the structural information of objects in the images. Experiments are conducted over optic cup segmentation, retinal vessel segmentation, and diabetic retinopathy multi-lesion segmentation tasks, involving eight publicly available datasets. Experimental results show that the proposed RCT outperforms comparison SDG methods, achieving improvements of 5.9%, 1.3% and 3.6% compared the second-best method on the optic cup, vessel, and lesion segmentation tasks, respectively. The source code will be available at https://***/guomugong/RCT.

关键词： retinal image segmentation Domain generalization Color transformation Ensemble learning

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retinal image segmentation Through Valley Emphasis Thresholding of the Gabor Filter Response 12th

Retinal Image Segmentation Through Valley Emphasis Threshold...

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12th International Conference on Computational Collective Intelligence (ICCCI)

作者： Gwetu, Mandlenkosi Victor Tapamo, Jules-Raymond Viriri, Serestina Univ KwaZulu Natal Private Bag X54001 ZA-4000 Durban South Africa

ISBN: (纸本)9783030630065;9783030630072

The quest for automated diagnosis of diabetic retinopathy continues due to increasing prevalence coupled with scarcity of skilled medical experts, especially in the third world. Due to the significant role that retinal image analysis plays in such diagnosis, there is a need for effective segmentation methods that accurately isolate the various retinal components, whose attributes have diagnostic relevance. Previous work has mainly focused on improving accuracy rates without much regard for algorithm efficiency. This study explores the use of Gabor features and Valley Emphasis (VE) thresholding for efficient and effective retinal image segmentation. Pre-processing is optimized through the squared magnitude Gabor response, prior to enrolling a selective VE thresholding approach. Experiments on the DRIVE and STARE datasets demonstrate significant reduction in computational overhead coupled with a minor improvement in segmentation accuracy.

关键词： retinal image segmentation Gabor filter Valley emphasis thresholding Selective Squared magnitude

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Lightweight Attention Convolutional Neural Network for retinal Vessel image segmentation

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第3期17卷 1958-1967页

作者： Li, Xiang Jiang, Yuchen Li, Minglei Yin, Shen Harbin Inst Technol Dept Control Sci & Engn Harbin 150090 Peoples R China Tech Univ Munich Dept Elect & Comp Engn Chair Automat Control Engn D-80333 Munich Germany

retinal vessel image is an important biological information that can be used for personal identification in the social security domain, and for disease diagnosis in the medical domain. While automatic vessel image segmentation is essential, it is also a challenging task because the retinal vessels have complex topological structures, and the retinal vessels vary in size and shape. In recent years, image segmentation based on the deep learning technique has become a mainstream method. Unfortunately, the existing methods cannot make the best use of the global information, and the model complexity is high. In this article, a convolutional neural network integrated with the attention mechanism is proposed. The overall network structure consists of a basic U-Net and an attention module, and the latter is used to capture global information and to enhance features by placing it in the process of feature fusion. Experiment results on five public datasets show that the proposed scheme outperforms other existing mainstream approaches, and most of the performance indicators are in the leading positions. More importantly, the proposed method has a significant reduction in the number of parameters.

关键词： Feature extraction image segmentation Biomedical imaging Blood vessels retinal vessels Decoding Attention mechanism biometric deep learning retinal image segmentation

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retinal image segmentation using double-scale non-linear thresholding on vessel support regions

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2017年第3期2卷 109-115页

作者： Li, Qingyong Zheng, Min Li, Feng Wang, Jianzhu Geng, Yangli-ao Jiang, Haibo Beijing Jiaotong Univ Beijing Key Lab Transportat Data Anal & Min Beijing 100044 Peoples R China Cent South Univ Xiangya Hosp Dept Ophthalmol Changsha 410083 Peoples R China

retinal vessel segmentation is a critical indicator of diagnosis, screening, and treatment of cardiovascular and ophthalmologic diseases. Due to the fact that the retinal vessels usually have some tiny structures and blurred boundaries, especially with remarkable noises, it is difficult to correctly segment the vascular networks. In this study, the authors propose a double-scale non-linear thresholding method based on vessel support regions. First, the double-scale filtering method is applied to enhance the contrast between the foreground vascular and the background stuffs. Second, they segment the fine and coarse vessels by the corresponding adaptive local thresholding and fixed-ratio thresholding method. Finally, they obtain the binary segmentation by fusion of fine and coarse vessels. Experiments are conducted on the publicly available DRIVE and STARE datasets, which show the effectiveness of the proposed method on retinal vessel segmentation.

关键词： cardiovascular diseases contrast enhancement fixed-ratio thresholding method ophthalmologic diseases blood vessels image enhancement retinal image segmentation retinal vessels binary segmentation diseases eye coarse vessels adaptive local thresholding fine vessels vessel support regions double-scale filtering method image segmentation double-scale non-linear thresholding double-scale nonlinear thresholding method medical image processing retinal vessel segmentation

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Active learning of the ground truth for retinal image segmentation

Active learning of the ground truth for retinal image segmen...

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作者： Nedoshivina, Liubov LAPPEENRANTA UNIVERSITY OF TECHNOLOGY

学位级别：硕士

Diabetic retinopathy and other eye-related diseases can be diagnosed from eye fundus im- ages by medical experts who look for specific lesions in the images. Automated diagnosis methods can help medical doctors to increase the diagnosis accuracy and decrease the time needed. In order to have a proper dataset for training and evaluating the methods, a large set of images should be annotated by several experts to form the ground truth. To enable efficient utilization of expert's time, active learning is studied to accelerate the collection of the ground truth. Since one of the important steps in the retinal image diag- nosis is the blood vessels segmentation, the corresponding approaches were studied. Two approaches were implemented and extended by proposed active learning methods for se- lecting the next image to be annotated. The performance of the methods in the case of standard implementation and active learning application was compared for several retinal images databases.

关键词： computer vision active learning retinal imaging retinal image segmentation

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ATP-MLSM: angular texture pattern-multi-level set model-based retinal image segmentation approach

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2016年第4期22卷 285-313页

作者： Taneja, Arti Ranjan, Priya Ujlayan, Amit Amity Univ Amity Inst Informat Technol Noida 201303 Uttar Pradesh India Gautam Budha Univ Dept Math Greater Noida India

This paper proposes an Angular Texture Pattern (ATP)-Multi-Level Set Model (MLSM)-based retinal image segmentation approach. The location of Optical Disk (OD) is estimated by initially collecting the blood vessel region from the retinal image. Based on the identification of OD location, the bright pixel values are estimated to provide the boundary detail of OD. From this boundary detail, the Region of Interest (ROI) such as Hard Exudates (HE) is obtained in the binary form, to enable contour formation for the OD and HE. Then, the cup-to-disk ratio of the OD is calculated, and the number of HEs is counted. The severity level of the DR and Glaucoma is determined based on the cup-to-disk ratio of the OD and HE count value. The proposed algorithm is tested by using the retinal images of the DIARETDB1 and MESSIDOR database. The proposed approach achieves better performance than the existing OD segmentation methodologies.

关键词： ATP angular texture pattern diabetic retinopathy glaucoma MLSM multi-level set model optical disk retinal image segmentation

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TUnet-LBF: retinal fundus image fine segmentation model based on transformer Unet network and LBF

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期159卷 106937-106937页

作者： Zhang, Hanyu Ni, Weihan Luo, Yi Feng, Yining Song, Ruoxi Wang, Xianghai Liaoning Normal Univ Sch Geog Dalian 116029 Peoples R China Liaoning Normal Univ Sch Comp & Informat Technol Dalian 116029 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110167 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100101 Peoples R China

segmentation of retinal fundus images is a crucial part of medical diagnosis. Automatic extraction of blood vessels in low-quality retinal images remains a challenging problem. In this paper, we propose a novel two-stage model combining Transformer Unet (TUnet) and local binary energy function model (LBF), TUnet-LBF, for coarse to fine segmentation of retinal vessels. In the coarse segmentation stage, the global topological information of blood vessels is obtained by TUnet. The neural network outputs the initial contour and the probability maps, which are input to the fine segmentation stage as the priori information. In the fine segmentation stage, an energy modulated LBF model is proposed to obtain the local detail information of blood vessels. The proposed model reaches accuracy (Acc) of 0.9650, 0.9681 and 0.9708 on the public datasets DRIVE, STARE and CHA-SE_DB1 respectively. The experimental results demonstrate the effectiveness of each component in the proposed model.

关键词： retinal image segmentation Neural network Energy modulate Unet LBF model

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