Image Segmentation by Using Threshold Techniques

Al-amri, Salem Saleh; Kalyankar, N. V.; Khamitkar, Santosh

doi:10.48550/arxiv.1005.4020

Cited by 18 publications

(14 citation statements)

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“…to obtain the segmentation results. Kalyankar [1] used five different thresholding algorithms to segment remote sensing satellite images and compared their segmentation effects with each other. The best performing method was the histogram and edge maximization thresholding method.…”

Section: Introductionmentioning

confidence: 99%

ResAt-UNet: A U-Shaped Network Using ResNet and Attention Module for Image Segmentation of Urban Buildings

Fan

Liu

Xia

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Architectural image segmentation refers to the extraction of architectural objects from remote sensing images. At present, most neural networks ignore the relationship between feature information, and there are problems such as model overfitting and gradient explosion. Thus, this paper proposes an improved UNet based on ResNet34 and Attention Module (ResAt-UNet) to solve the related problems. The algorithm adds a twolayer residual structure (BasicBlock) and a regional enhancement attention mechanism (Space Enhancement Area Enhancement, SEAE) to the original framework of UNet, which enhances the network depth, improves the fitting performance, and extracts small objects more accurately. The experimental results show that the network has achieved MIOU of 78.81% in the Massachusetts dataset, and the newly developed model outperforms UNet in both quantitative and qualitative aspects.

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Section: Introductionmentioning

confidence: 99%

ResAt-UNet: A U-Shaped Network Using ResNet and Attention Module for Image Segmentation of Urban Buildings

Fan

Liu

Xia

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Different image segmentation techniques used earlier that did not use the concept of the CNN are 1) Threshold-based [36]- [38], 2) Edge Detection-based [40] [41], 3) Region-based [42] [43], and 4) Clustering-based [45] methods. On the other hand, CNN-based model architectures were first introduced to perform canonical tasks related to image classification [30] that can classify the whole input image into a single label.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumour Segmentation Using S-Net and SA-Net

et al. 2023

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Image segmentation is an application area of computer vision and digital image processing that partitions a digital image into multiple image regions or segments. This process involves the extraction of a set of contours from the input digital image in such a manner that pixels belonging to a region share some common characteristics or computed properties, such as color, texture, or intensity. The application domain of image segmentation is widespread, and includes video surveillance, object detection, traffic control systems, and medical imaging. The application of image segmentation techniques in the field of medical imaging can be further subcategorized into virtual surgery simulation, diagnosis, study of anatomical structures, measurement of tissue volumes, location of tumors and other pathologies. In this study, we have proposed two new Convolutional Neural Network (CNN)-based models: (a) S-Net and (b) Attention S-Net (SA-Net) to perform image segmentation tasks in the field of medical imaging, especially to generate segmentation masks for brain tumours if present in brain Medical Resonance Imaging (MRI). Both proposed models were developed by considering U-Net as the base architecture. The newly proposed models have leveraged the concept of 'Merge Block' to infuse both the local and global context, and 'Attention Block' to focus on the region of interest having a specific object. Additionally, it uses techniques, such as data augmentation to utilize the available annotated samples more efficiently. The proposed models achieved a Dice Similarity Coefficient (DSC) measure of 0.78 and 0.80 for the High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) datasets, respectively.

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“…For medical image segmentation, semantic information is important [2]. It is known that variational or graph-based segmentation approaches [3,4,5,6,7,8] usually lack the semantic information of objects, while deep learning-based methods overcome the limitations of traditional segmentation methods and show good performance in detecting these information. However, although the deep learning-based image segmentation methods have yields promising segmentation performance, mage details may be ignored during the pooling and downsampling operations, thus reducing the accuracy of image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Learning anisotropy and asymmetry geometric features for medical image segmentation

Liu

2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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Finding contours of interest from medical images is an important task in the field of medical image analysis. The current deep learning-based image segmentation approaches have obtained promising results. However, most of these models do not take into account the anisotropy and asymmetric features which play an important role in describing the target contours. In order to address this issue, we propose new loss-function applied to the deep learning model with dense distance regression, which can benefit the edge-based features, thus able to improve the stability of the segmentation procedure and to reduce the probability of outliers in the segmentation results. The introduced loss function is embedded into the deep learning model, which can perform an end-to-end image segmentation procedure for medical images. Ablation experiments were done with other loss functions and three datasets were used to verify whether this loss function is effective. SOTA results were obtained for the proposed loss function in this paper compared to the recently designed method for reducing the boundary error.

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Image Segmentation by Using Threshold Techniques

Cited by 18 publications

References 0 publications

ResAt-UNet: A U-Shaped Network Using ResNet and Attention Module for Image Segmentation of Urban Buildings

ResAt-UNet: A U-Shaped Network Using ResNet and Attention Module for Image Segmentation of Urban Buildings

Brain Tumour Segmentation Using S-Net and SA-Net

Learning anisotropy and asymmetry geometric features for medical image segmentation

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