QUALITY ASSESSMENT FOR MULTI-RESOLUTION SEGMENTATION AND SEGMENT-ANYTHING MODEL USING WORLDVIEW-3 IMAGERY

Image segmentation is an important process in the field of medical imaging.Recently, much work has been reported in medical image segmentation. Among these techniques, finite Gaussian mixture models are considered to be more recent and accurate. However, in this approach, a number of segments that an image can be divided are taken through apriori and if these segments are not initiated properly it leads to misclassification. Hence, to overcome this disadvantage, we proposed an algorithm for Medical Image Segmentation using Hierarchical Clustering and Skew Gaussian Mixture. The experimentation is done with two different brain images and the results obtained are evaluated using both Image Quality metrics and Segmentation Quality Metrics.

Sensitivity of multiresolution segmentation to spatial extent

The purpose of the research is to analyze the efficiency of the U-net neural network architecture in decision support systems for glioma diagnostics and segmentation of brain areas affected by it on MRI images.Methods. To conduct experimental studies, a training dataset was generated and the data was normalized. A software implementation of the U-Net neural network architecture was performed using the Keras framework in the Python programming language. The neural network model was trained.Results. A series of experiments were conducted, during which error and classification matrices were obtained, the efficiency of classification of the trained neural network model for the "Tumor" and "No tumor" classes was assessed using metrics such as Recall, Precision and F1-measure, and the quality of segmentation of glioma-affected areas on the test data set was assessed. The quality of segmentation was assessed using the IoU metric, which reflects the ratio of the areas of the bounding boxes and is used to assess the accuracy of the spatial correspondence of the predicted segmented areas highlighted on the masks. Based on the results of testing the neural network model in solving the problem of segmenting brain areas affected by glioma, the average value of the IoU metric was 0.812, which is an acceptable result.Conclusion. The testing results showed that the neural network model based on the U-net architecture is able to effectively diagnose the presence of glioma with acceptable values of the classification and segmentation quality metrics, which indicates the possibility of using this neural network model in medical decision support systems for glioma diagnostics, as well as its segmentation on MRI images. However, it is advisable to refine this neural network model to reduce the number of false negative classification results, which is critically important in medical diagnostics.

Hybrid region merging method for segmentation of high-resolution remote sensing images

Rheumatoid arthritis (RA) is a chronic autoimmune disease that can cause severe joint damage and functional impairment. Ultrasound imaging has shown promise in providing real-time assessment of synovium inflammation associated with the early stages of RA. Accurate segmentation of the synovium region and quantification of inflammation-specific imaging biomarkers are crucial for assessing and grading RA. However, automatic segmentation of the synovium in 3D ultrasound is challenging due to ambiguous boundaries, variability in synovium shape, and inhomogeneous intensity distribution. In this work, we introduce a novel network architecture, Swin Transformers with Deep Attentive Features for 3D segmentation (SwinDAF3D), which integrates Swin Transformers into a Deep Attentive Features framework. The developed architecture leverages the hierarchical structure and shifted windows of Swin Transformers to capture rich, multi-scale and attentive contextual information, improving the modeling of long-range dependencies and spatial hierarchies in 3D ultrasound images. In a six-fold cross-validation study with 3D ultrasound images of RA patients' finger joints (n = 72), our SwinDAF3D model achieved the highest performance with a Dice Score (DSC) of 0.838 ± 0.013, an Intersection over Union (IoU) of 0.719 ± 0.019, and Surface Dice Score (SDSC) of 0.852 ± 0.020, compared to 3D UNet (DSC: 0.742 ± 0.025; IoU: 0.589 ± 0.031; SDSC: 0.661 ± 0.029), DAF3D (DSC: 0.813 ± 0.017; IoU: 0.689 ± 0.022; SDSC: 0.817 ± 0.013), Swin UNETR (DSC: 0.808 ± 0.025; IoU: 0.678 ± 0.032; SDSC: 0.822 ± 0.039), UNETR++ (DSC: 0.810 ± 0.014; IoU: 0.684 ± 0.018; SDSC: 0.829 ± 0.027) and TransUNet (DSC: 0.818 ± 0.013; IoU: 0.692 ± 0.017; SDSC: 0.815 ± 0.016) models. This ablation study demonstrates the effectiveness of combining a Swin Transformers feature pyramid with a deep attention mechanism, improving the segmentation accuracy of the synovium in 3D ultrasound. This advancement shows great promise in enabling more efficient and standardized RA screening using ultrasound imaging.

In this chapter, we present a method for the segmentation of scanned images of newspapers and magazines. For further Mixed Raster Content (MRC) compression, we classify blocks of an image into background, or picture areas, or text regions. The method is relatively simple and fast, because it is intended for implementation in firmware. Textural features are calculated for image blocks. We train three one-to-rest classifiers by the AdaBoost technique on a publicly available dataset. The output of the classifiers can be treated as a posteriori probability. We smooth these probabilities among adjacent blocks. After smoothing, a voting procedure sets the class for each block. We argue that the Dual Leave-Group-of-Sources-Out cross-validation scheme is beneficial for the tuning of algorithm parameters. We discuss the advantages and shortcomings of several segmentation quality metrics.

Accurate segmentation of fingernail images is essential for biomedical applications like dermatological diagnostics and nail disease assessments. This study compares traditional methods (Sobel and Canny edge detectors) with an improved method using adaptive thresholding and morphological closing for fingernail image segmentation. The methodology includes data collection, preprocessing, edge detection, segmentation, and evaluation. A dataset of 500 fingernail images (free of nail polish) was captured using a digital camera. Preprocessing involves grayscale conversion to simplify analysis and Gaussian smoothing to reduce noise while preserving key features. For segmentation, thresholding and K-means clustering isolate the fingernail from the background. Evaluation combines qualitative and quantitative analyses. Qualitative results demonstrate that the improved method consistently outperforms traditional techniques under diverse conditions. Quantitative evaluation, based on accuracy, recall, F1 score, and Intersection over Union (IoU), further supports these findings. The Sobel method achieves 0.80 accuracy, 0.77 recall, 0.87 F1 score, and 0.77 IoU. The Canny method achieves 0.82 accuracy, 0.78 recall, 0.88 F1 score, and 0.78 IoU. In contrast, the improved method achieves 0.97 accuracy, 0.98 recall, 0.99 F1 score, and 0.98 IoU. The results clearly show that the improved method, using adaptive thresholding and morphological closing, provides superior segmentation performance. Additionally, the approach remains computationally efficient, making it suitable for real-time applications in medical diagnostics.

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

We present a novel region based active learning method for semantic image segmentation, called MetaBox+. For acquisition, we train a meta regression model to estimate the segment-wise Intersection over Union (IoU) of each predicted segment of unlabeled images. This can be understood as an estimation of segment-wise prediction quality. Queried regions are supposed to minimize to competing targets, i.e., low predicted IoU values / segmentation quality and low estimated annotation costs. For estimating the latter we propose a simple but practical method for annotation cost estimation. We compare our method to entropy based methods, where we consider the entropy as uncertainty of the prediction. The comparison and analysis of the results provide insights into annotation costs as well as robustness and variance of the methods. Numerical experiments conducted with two different networks on the Cityscapes dataset clearly demonstrate a reduction of annotation effort compared to random acquisition. Noteworthily, we achieve 95%of the mean Intersection over Union (mIoU), using MetaBox+ compared to when training with the full dataset, with only 10.47% / 32.01% annotation effort for the two networks, respectively.

This study examines the impact of seabed conditions on image segmentation for seabed target images acquired via side-scan sonar during sea experiments. The dataset comprised cylindrical target images overlying on two seabed types, mud and sand, categorized accordingly. The deep learning algorithm (U-NET) was utilized for image segmentation. The analysis focused on two key factors influencing segmentation performance: the weighting method of the cross-entropy loss function and the combination of datasets categorized by seabed type for training, validation, and testing. The results revealed three key findings. First, applying equal weights to the loss function yielded better segmentation performance compared to pixel-frequency-based weighting. This improvement is indicated by Intersection over Union (IoU) for the highlight class in dataset 2 (0.41 compared to 0.37). Second, images from the mud area were easier to segment than those from the sand area. This was due to the clearer intensity contrast between the target highlight and background. This difference is indicated by the IoU for the highlight class (0.63 compared to 0.41). Finally, a network trained on a combined dataset from both seabed types improved segmentation performance. This improvement was observed in challenging conditions, such as sand areas. In comparison, a network trained on a single-seabed dataset showed lower performance. The IoU values for the highlight class in sand area images are as follows: 0.34 for training on mud, 0.41 for training on sand, and 0.45 for training on both.

This experiment conducted a standardized head-to-head evaluation of six medical image segmentation architectures-UNet, TotalSegmentator (nnU-Net-based), MONAI SegResNet, KM-UNet (KAN+SSM), UniverSeg, and KonfAI—on two cellular-level subsets (White Blood Cell and Yeaz) of MedSegBench. All models are trained under identical settings (50 epochs; batch size 8; Adam with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{lr}=2 \mathrm{e}-4$</tex>; DiceCE loss; AMP; gradient clipping =1.0) and assessed with Dice (foreground only), Intersection over Union (IoU), and surface distance. While mean Dice is identical across models (0.2420), spatial metrics separate them: MONAI achieves the best IoU (0.9005) and the lowest surface distance (3.0309), followed by UniverSeg and KM-UNet (IoU <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 0.89$</tex>). We further study crossarchitecture knowledge distillation. UNet → KM-UNet collapses spatial fidelity (IoU 0.0039; infinite surface distance) despite unchanged Dice, whereas MONAI → UniverSeg preserves partial structure (IoU 0.5637; surface distance 22.42). These results show (i) multi-metric evaluation beyond Dice is essential for judging boundary quality, and (ii) naïve distillation can fail to transfer boundary information across heterogeneous architectures; architectural compatibility and distillation design are critical for reliable deployment.

Floods are the most frequently occurring natural disasters, significantly impacting the environment and society. As part of natural disaster mitigation, the impacts could be reduced through predictive techniques using deep learning for semantic segmentation of inundation images. Therefore, this research aims to evaluate the performance of deep learning architectures in segmenting inundation images using the Flood Segmentation dataset, which comprised 290 aerial images. The following segmentation architectures, U-Net, SegNet, and LinkNet, were compared using backbones such as MobileNet, ResNet, EfficientNet, and VGG, as well as optimizers including Adam, SGD, AdaDelta, and RMSProp. Performance was assessed using Intersection over Union (IoU) score, precision, F1-score, recall, and accuracy metrics. The results showed that U-Net achieved the highest performance with IoU, precision, F1-score, recall, and accuracy of 0.767, 0.862, 0.866, 0.876, and 0.899, respectively. Regarding the backbones, MobileNet excelled with IoU, precision, F1-score, recall, and accuracy of 0.764, 0.866, 0.865, 0.869, and 0.898, respectively. The Adam optimizer outperformed others, yielding IoU, precision, F1-score, recall, and accuracy of 0.712, 0.807, 0.824, 0.873, and 0.843. In conclusion, the combination of U-Net with MobileNet backbone and Adam optimizer was the most effective architecture for flood inundation image segmentation, offering a robust foundation for prediction systems.

Accurate intracranial artery segmentation from digital subtraction angiography (DSA) is critical for neurovascular diagnosis and intervention planning. Vascular extraction, which combines preprocessing methods and deep learning models, yields a high level of results, but limited preprocessing results constrain the improvement of results. We propose a texture-based contrast enhancement preprocessing framework integrated with the nnU-Net model to improve vessel segmentation in time-sequential DSA images. The method generates a combined feature mask by fusing local contrast, local entropy, and brightness threshold maps, which is then used as input for deep learning–based segmentation. Segmentation performance was evaluated using the DIAS dataset with various standard quantitative metrics. The proposed preprocessing significantly improved segmentation across all metrics compared to both the baseline and contrast-limited adaptive histogram equalization (CLAHE). Using nnU-Net, the method achieved a Dice Similarity Coefficient (DICE) of 0.83 ± 0.20 and an Intersection over Union (IoU) of 0.72 ± 0.14, outperforming CLAHE (DICE 0.79 ± 0.41, IoU 0.70 ± 0.23) and the baseline (DICE 0.65 ± 0.15, IoU 0.47 ± 0.20). Most notably, vessel connectivity (VC) dropped by over 65% relative to unprocessed images, indicating marked improvements in VC and topological accuracy. This study demonstrates that combining texture-based preprocessing with nnU-Net delivers robust, noise-tolerant, and clinically interpretable segmentation of intracranial arteries from DSA.

U-net-based segmentation of foreign bodies and ghost images in panoramic radiographs.

Lesion segmentation in dermoscopic images significantly enhances the diagnostic performance of AI-based classification models. However, conventional methods often require pixel-level annotations, which are resource-intensive and prone to errors caused by external artifacts, such as hair and skin markings. We propose a hybrid framework called SAM-enhanced YOLO, which integrates the Segment Anything Model (SAM) with You Only Look Once (YOLO) for precise pixel-level segmentation. This method combines YOLO's efficient lesion localization with SAM's advanced zero-shot segmentation capabilities. To further validate the framework, we compared it against traditional methods, including GrabCut and Otsu's thresholding, as well as SAM used without YOLO (SAM-only). For SAM-only, lesion segmentation was initialized at the image center to simulate a typical dermoscopic imaging setup. SAM-enhanced YOLO demonstrated superior segmentation performance, achieving an Intersection over Union (IoU) of 0.738 and an F1-score (the harmonic mean of precision and recall) of 0.833, compared to 0.578 and 0.683 with SAM-only, respectively. This represents a 28% improvement in IoU and a 22% improvement in F1-score compared to SAM-only. The results were consistent across lesion shapes and contrast conditions, with SAM-enhanced YOLO exhibiting the lowest variability and highest robustness among the evaluated methods. By reducing the need for pixel-level annotations and outperforming both standalone SAM and traditional methods, SAM-enhanced YOLO provides a scalable and resource-efficient solution for dermoscopic lesion segmentation. This framework holds significant potential for improving diagnostic workflows in clinical and resource-limited settings.