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[IEEE 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM) - Guadalajara, Jalisco, Mexico (2018.11.14-2018.11.16)] 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM) - Convolutional Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Images
摘要： The recent impulse in development of artificial intelligence (AI) methodologies has simplified the application of this in multiple research areas. This simplification was not favorable before, due to the limitations in dimensionality, processing time, computational resources, among others. Working with multispectral remote sensing (RS) images, in an artificial neural network (NN) was quite complex. Due the methods used required millions of processes that took a long time to be executed and produce competitive results compared with the state of the art (SoA). Deep learning (DL) strategies have been applied to alleviate these limitations and have greatly improved the use of neural networks. Therefore, this paper presents the analysis of DL-NNs to perform semantic segmentation of multispectral RS images. Images are captured by the constellation of satellites Sentinel-2 from the European Space Agency. The objective of this research is to classify each pixel of a scene into five categories: 1-vegetation, 2-soil, 3-water, 4-clouds and 5-cloud shadows. The selection of spectral bands for the formation of input datasets for segmentation of these classes is very important. The spectral signatures of each material aid to discern among several classes. Results presented in this work, show that the AI strategy proposed offer better accuracy segmentation than other methods of the SoA in competitive processing time.
关键词： semantic segmentation,Convolutional neural networks,remote sensing,multispectral images
更新于2025-09-19 17:15:36
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[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Human Brain Tissue Segmentation in fMRI using Deep Long-Term Recurrent Convolutional Network
摘要： Accurate segmentation of different brain tissue types is an important step in the study of neuronal activities using functional magnetic resonance imaging (fMRI). Traditionally, due to the low spatial resolution of fMRI data and the absence of an automated segmentation approach, human experts often resort to superimposing fMRI data on high resolution structural MRI images for analysis. The recent advent of fMRI with higher spatial resolutions offers a new possibility of differentiating brain tissues by their spatio-temporal characteristics, without relying on the structural MRI images. In this paper, we propose a patch-wise deep learning method for segmenting human brain tissues into five types, which are gray matter, white matter, blood vessel, non-brain and cerebrospinal fluid. The proposed method achieves a classification rate of 84.04% and a Dice similarity coefficient of 76.99%, which exceed those by several other methods.
关键词： functional MRI,brain tissue segmentation,learning,long short-term memory,deep convolutional neural network
更新于2025-09-19 17:15:36
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Motion Occlusions for Automatic Generation of Relative Depth Maps
摘要： Recovering of the depth structure of a scene from monocular video content provides an important advantage in applications such as AR (placing and removing of objects) or 3D-TV and 3D cinema (2D-to-3D video conversion). In this paper, we present an automatic method to generate relative depth maps from monocular video sequences. It relies on the dynamic occlusion depth cue to recover the depth order of objects in the scene. The forward and backward motion analysis between each two consecutive frames allows the calculation of their dynamic occlusions. We estimate the motion using a modified version of the EpicFlow. Our modifications to this optical flow method made it coherent in forward-backward directions without compromising its performance. Thanks to this new feature, occlusions are simpler to calculate than the approaches used in the relevant literature. The obtained occlusions allow order deduction of the objects contained in the image. These objects are obtained using a segmentation approach which considers both color and motion. Ours results show a small improvement to the quality of the optical flow while adding the forward/backward coherence. With respect to the depth ordering our approach obtains slightly better results than the reference method while removing a computationally costly step from the processing.
关键词： depth ordering,occlusions,segmentation,relative depth map
更新于2025-09-19 17:15:36
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Evaluation on the Compactness of Supervoxels
摘要： Supervoxels are perceptually meaningful atomic spatio-temporal regions in videos, which has great potential to reduce the computational complexity of downstream video applications. Many methods have been proposed for generating supervoxels. To effectively evaluate these methods, a novel supervoxel library and benchmark called LIBSVX with seven collected metrics was recently established. In this paper, we propose a new compactness metric which measures the shape regularity of supervoxels and is served as a necessary complement to the existing metrics. To demonstrate its necessity, we first explore the relations between the new metric and existing ones. Correlation analysis shows that the new metric has a weak correlation with (i.e., nearly independent of) existing metrics, and so reflects a new characteristic of supervoxel quality. Second, we investigate two real-world video applications. Experimental results show that the new metric can effectively predict some important application performance, while most existing metrics cannot do so.
关键词： metric evaluation,Supervoxel,video segmentation,compactness
更新于2025-09-19 17:15:36
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[ACM Press the 2018 3rd International Conference - Bari, Italy (2018.10.11-2018.10.13)] Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing - ICBSP 2018 - Tissue Region Growing for Hispathology Image Segmentation
摘要： The accurate identification of the tumour tissue border is of crucial importance for histopathology image analysis. However, due to the high morphology variance in histology images, especially in border regions where cancer tissue interfere into the normal region, it is challenging even for the pathologists to define the border, not to say for the machine. In this paper, we present an innovative framework to semantically segment the tumour border area in colorectal liver metastasis (CRLM) on pixel level by integrating the features from deep convolutional networks with spatial and statistical information of the cells. With annotations from the pathologists, a two-level deep neural network including a cell-level model and a tissue-level model, is trained to classify patches from the whole slide scan image. Based on the prediction of trained models, a growing-style algorithm is proposed to finalize the segmentation by leveraging the statistical and spatial properties of the cells. Evaluated against the ground truth created by the experts, the framework demonstrates a significant improvement over a conventional deep network model on the cell-level model or the tissue model alone.
关键词： image segmentation,Histopathology image,deep learning.
更新于2025-09-19 17:15:36
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Cone Photoreceptor Cell Segmentation and Diameter Measurement on Adaptive Optics Images Using Circularly Constrained Active Contour Model
摘要： PURPOSE. Cone photoreceptor cells can be noninvasively imaged in the living human eye by using nonconfocal adaptive optics scanning ophthalmoscopy split detection. Existing metrics, such as cone density and spacing, are based on simplifying cone photoreceptors to single points. The purposes of this study were to introduce a computer-aided approach for segmentation of cone photoreceptors, to apply this technique to create a normal database of cone diameters, and to demonstrate its use in the context of existing metrics. METHODS. Cone photoreceptor segmentation is achieved through a circularly constrained active contour model (CCACM). Circular templates and image gradients attract active contours toward cone photoreceptor boundaries. Automated segmentation from in vivo human subject data was compared to ground truth established by manual segmentation. Cone diameters computed from curated data (automated segmentation followed by manual removal of errors) were compared with histology and published data. RESULTS. Overall, there was good agreement between automated and manual segmentations and between diameter measurements (n ? 5191 cones) and published histologic data across retinal eccentricities ranging from 1.35 to 6.35 mm (temporal). Interestingly, cone diameter was correlated to both cone density and cone spacing (negatively and positively, respectively; P < 0.01 for both). Application of the proposed automated segmentation to images from a patient with late-onset retinal degeneration revealed the presence of enlarged cones above individual reticular pseudodrusen (average 23.0% increase, P < 0.05). CONCLUSIONS. CCACM can accurately segment cone photoreceptors on split detection images across a range of eccentricities. Metrics derived from this automated segmentation of adaptive optics retinal images can provide new insights into retinal diseases.
关键词： nonconfocal split detection,reticular pseudodrusen,normal database,active contour model,cell segmentation
更新于2025-09-19 17:15:36
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Automated cell segmentation in FIJI? using the DRAQ5 nuclear dye
摘要： Background: Image segmentation and quantification are essential steps in quantitative cellular analysis. In this work, we present a fast, customizable, and unsupervised cell segmentation method that is based solely on Fiji (is just ImageJ)?, one of the most commonly used open-source software packages for microscopy analysis. In our method, the “leaky” fluorescence from the DNA stain DRAQ5 is used for automated nucleus detection and 2D cell segmentation. Results: Based on an evaluation with HeLa cells compared to human counting, our algorithm reached accuracy levels above 92% and sensitivity levels of 94%. 86% of the evaluated cells were segmented correctly, and the average intersection over union score of detected segmentation frames to manually segmented cells was above 0.83. Using this approach, we quantified changes in the projected cell area, circularity, and aspect ratio of THP-1 cells differentiating from monocytes to macrophages, observing significant cell growth and a transition from circular to elongated form. In a second application, we quantified changes in the projected cell area of CHO cells upon lowering the incubation temperature, a common stimulus to increase protein production in biotechnology applications, and found a stark decrease in cell area. Conclusions: Our method is straightforward and easily applicable using our staining protocol. We believe this method will help other non-image processing specialists use microscopy for quantitative image analysis.
关键词： Batch processing,Fiji,Cell segmentation,DRAQ5,Image processing,ImageJ
更新于2025-09-19 17:15:36
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Spatio-statistical optimization of image segmentation process for building footprint extraction using very high-resolution WorldView 3 satellite data
摘要： Segmentation process in building footprint extraction using object-based image analysis (OBIA) is crucial due to several factors, such as the spatial and spectral resolution of remote sensing images and the complexity of geo-objects. Consequently, the selection of suitable parameters to ensure the best segmentation quality remains a challenge. To overcome this issue, a spatio-statistical optimization technique that combines the Taguchi statistical method and a spatial plateau objective function (POF) was developed to extract building footprint from high-resolution Worldview 3 (WV3) satellite data. Initially, the Taguchi statistical method was used to design the orthogonal array of 25 experiments with three segmentation parameters, namely, scale, shape, and compactness, each having five varying factor level in the orthogonal array and the calculated POF was merged to produce main effects and interaction plots for signal-to-noise ratios (SNR), whereby the smaller-is-better and larger-is-better options of the Taguchi’s SNR were tested on each parameter to maximize their effects. After that, the segmentation quality obtained from the proposed method was assessed by comparing with the benchmark method introduced by Dragut and result indicates that the proposed method was better than the benchmark method. Subsequently, the final optimal parameters were used for segmentation process in eCognition and the image object was classified into five land cover classes (building, road, water, trees, and grass) by using a supervised non-parametric statistical learning technique, Support Vector Machine (SVM) classifier. Finally, the building features was extracted, and the detection accuracy was evaluated based on receiver operating characteristics (ROC). Result shows the area under ROC curve (AUC) of 0.804 with p < 0.0001 at 95% confidence level. This verifies that the proposed method is effective for building detection with high accuracy and the integration of Taguchi and objective function managed to determine the optimal segmentation parameters. Optimization segmentation parameters can later be applied to distinguish roof materials and conditions.
关键词： building footprint,image segmentation,spatio-statistical optimization,OBIA,Taguchi method
更新于2025-09-19 17:15:36
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Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery
摘要： Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.
关键词： accuracy,infrared,segmentation,object-based classification,orthophoto,high resolution imagery,land cover
更新于2025-09-19 17:15:36
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Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation
摘要： Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals that dominate the results of change detection if they are not removed completely from imagery. As various architectures of deep learning (DL) have been proposed and advanced quickly, their potential in perceptual tasks has been widely accepted and successfully applied to many fields. A comprehensive survey of DL in RS has been reviewed, and the RS community has been suggested to be leading researchers in DL. Based on deep residual learning, semantic image segmentation, and the concept of atrous convolution, we propose a new DL architecture, named CloudNet, with an enhanced capability of feature extraction for classifying cloud and haze from Sentinel-2 imagery, with the intention of supporting automatic change detection in LULC. To ensure the quality of the training dataset, scene classification maps of Taiwan processed by Sen2cor were visually examined and edited, resulting in a total of 12,769 sub-images with a standard size of 224 × 224 pixels, cut from the Sen2cor-corrected images and compiled in a trainset. The data augmentation technique enabled CloudNet to have stable cirrus identification capability without extensive training data. Compared to the traditional method and other DL methods, CloudNet had higher accuracy in cloud and haze classification, as well as better performance in cirrus cloud recognition. CloudNet will be incorporated into the Open Access Satellite Image Service to facilitate change detection by using Sentinel-2 imagery on a regular and automatic basis.
关键词： change detection,atrous convolution,CloudNet,cloud classification,semantic image segmentation,deep learning,land use and land cover,deep residual learning,Sentinel-2
更新于2025-09-19 17:15:36