Blood Vessel Segmentation from Fundus Images Using Modified U-net Convolutional Neural Network

Joshua, Afolabi O.; Nelwamondo, Fulufhelo V.; Mabuza-Hocquet, Gugulethu

doi:10.18178/joig.8.1.21-25

Cited by 48 publications

(6 citation statements)

References 23 publications

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“…In equation (7),  is inversely proportional to the contrast of neighbouring pixels, and the boundary term is solved by adjusting the value of  . When calculating the energy term, the vector k is introduced as a parameter of the GMM, and the energy function is calculated as follows ( , , , )…”

Section: Mrf_gcgmm Modelmentioning

confidence: 99%

“…Compared with the traditional 2D U-Net, which only segments the small intestine frame by frame without time information, the proposed 3D U-Net can accurately segment all frames simultaneously using time information. Joshua A O et al [7] proposes a vascular segmentation method based on improved U-net convolutional neural network (CNN) architecture. The proposed method hardly requires additional fundus image preprocessing, and does not post-process the segmented blood vessels.…”

Section: Introductionmentioning

confidence: 99%

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Brain tumor image segmentation algorithm based on hybrid model feature extraction

Wang,

2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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Aiming at the problem that there are often errors in brain tumor image segmentation due to uneven gray scale and blurred boundaries in brain MRI images, a brain tumor segmentation method based on the combination of graph cutting algorithm and MAP_MRF algorithm and cellular automata model is proposed. A MRF_GCGMM model is established to automatically select the initial seed point based on the grayscale features of the image, and then modify the state transfer function of the cellular automata to achieve accurate segmentation of the brain tumor area. Experimental results show that the proposed algorithm has a higher segmentation accuracy of brain tumor images in the BraTs dataset than that of traditional brain tumor segmentation methods.

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Section: Mrf_gcgmm Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain tumor image segmentation algorithm based on hybrid model feature extraction

Wang,

2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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“…Joshua et al, [22] pointed out that for precise and speedy detection of eye illnesses such as glaucoma, diabetic retinopathy, and macular degeneration, proper examination of retinal fundus images is critical. U-Net was chosen to perform image segmentation for the following reasons:…”

Section: Image Segmentationmentioning

confidence: 99%

Continuous Eye Disease Severity Evaluation System using Siamese Neural Networks

AbdulRaheem¹,

Oladipo²,

Ajagbe³

et al. 2023

paradigmplus

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Evaluating the severity of eye diseases using medical images is a very essential and routine task performed in medical diagnosis and treatment. Current grading systems which are largely based on discrete classification are unreliable and do reflect not the entire spectrum of eye disease severity. The unreliability of discrete classification systems for eye diseases is clear, as classification is subjective and done based on the personal opinion of various medical experts, which may vary. In a bid to solve these issues, this study proposes a system for determining the severity of eye diseases on a continuous range using a twin-convoluted neural network approach known as Siamese Neural Networks. This system is demonstrated in the domain of diabetic retinopathy. Samples of retinal fundus images from an eye clinic in India are taken as test cases to evaluate the performance of a Siamese Triplet network which attempts to find the distance between their image embedding. The outputs of the Siamese network when a reference image is juxtaposed with a collection of images with distant severity categories (negative images), as well as when two reference images are compared to each other, are found to have a positive correlation (95\%) with originally assigned severity classes. Hence, these outputs indicate a continuous range of the severity and change in eye diseases.

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“…Deep learning is a powerful tool used to analyze complex data, such as images of central nervous system [5][6][7][8][9]. A popular CNN architecture used for semantic segmentation is U-Net [10,11], which consists of an encoding and decoding path connected by skip connections. We employed U-Net based model to segment and analyze the glomeruli within the OB.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of human olfactory bulb glomeruli on its phase-contrast tomographic images with neural networks

Smolin,

Chukalina,

Bukreeva

et al. 2024

Sixteenth International Conference on Machine Vision (ICMV 2023)

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The human olfactory bulb (OB), an important part of the brain responsible for the sense of smell, is a complex structure composed of multiple layers and cell types. Studying the OB morphological structure is essential for understanding the decline in olfactory function related to aging, neurodegenerative disorders, and other pathologies. Traditional microscopy methods in which slices are stained with solutions to contrast individual elements of the morphological structure are destructive. Non-destructive high-resolution technique is the X-ray phase-contrast tomography. However, manual segmentation of the reconstructed images are time-consuming due to large amount of data and prone to errors. U-Net-based model to optimize the segmentation of OB morphological structures, focusing specifically on glomeruli, in tomographic images of the human OB is proposed. The strategy to address overfitting and enhance the model's accuracy is described. This method addresses the challenges posed by complex limited data containing abundant details, similar grayscale levels between soft tissues, and blurry image details. Additionally, it successfully overcomes the limitations of a small dataset containing images with extremely dense point clouds, preventing the models from overfitting.

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Blood Vessel Segmentation from Fundus Images Using Modified U-net Convolutional Neural Network

Cited by 48 publications

References 23 publications

Brain tumor image segmentation algorithm based on hybrid model feature extraction

Brain tumor image segmentation algorithm based on hybrid model feature extraction

Continuous Eye Disease Severity Evaluation System using Siamese Neural Networks

Segmentation of human olfactory bulb glomeruli on its phase-contrast tomographic images with neural networks

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