- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Automatic detection of perforator vessels using infrared thermography in reconstructive surgery
摘要： Purpose Knowing the location of the blood vessels supplying the skin and subcutaneous tissue is a requirement during the planning of tissue transfer in reconstructive surgery. Commonly used imaging techniques such as computed tomography angiography and indocyanine green angiography expose the patient to radiation or a contrast agent, respectively. Infrared thermal imaging was evaluated with success as a non-invasive alternative. To support the interpretation of thermograms, a method to automatically detect the perforators was developed and evaluated. Methods A system consisting of a thermal camera, a PC and custom software was developed. The temperature variations of the skin surface were analysed to extract the perforator locations. A study was conducted to assess the performance of the algorithm by comparing the detection results of the algorithm with manually labelled thermal images by two clinicians of the deep inferior epigastric perforator ?ap of 20 healthy volunteers. Results The F measure, precision and recall were used to evaluate the system performance. The median F measure is 0.833, the median precision is 0.80, and the median recall is 0.907. Conclusion The results of this study showed that it is possible to automatically and reliably detect the skin perforators in thermograms despite their weak temperature signature. Infrared thermal imaging is a non-invasive and contactless approach suitable for intraoperative use. Combined with a computer-assisted tool for the automatic detection of perforator vessels, it is a relevant alternative intraoperative imaging method to the standard indocyanine green angiography.
关键词： Non-invasive imaging,Operation planning,Skin transplant,Automatic segmentation
更新于2025-09-09 09:28:46
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[IEEE 2018 1st International Cognitive Cities Conference (IC3) - Okinawa, Japan (2018.8.7-2018.8.9)] 2018 1st International Cognitive Cities Conference (IC3) - Hand Gesture Recognition Using Color-Depth Association for Smart Home
摘要： This study we propose a robust hand gesture segmentation method which associates the depth and color information with online training. Different existing methods, when the hands close to the body part or in cluttered background, our system remains valid. In the proposed method, a judging procedure of the hand point is applied to obtain more accurate result. As Kinect sensor has relatively large errors in acquisition of depth data at the edge of the object, this will lead to the wrong results with the depth threshold in coarse segmentation step. To compare with the existing systems, the proposed method is the least restrictive one in practically.
关键词： online training,Kinect sensor,Depth,ellipse model,hand gesture segmentation
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Building Detection and Segmentation Using a CNN with Automatically Generated Training Data
摘要： Significantly outperforming traditional machine learning methods, deep convolutional neural networks have gained increasing popularity in the application of image classification and segmentation. Nevertheless, deep learning-based methods usually require a large amount of training data, which is quite labor-intensive and time-demanding. To deal with the problem in generating training data, we propose in this paper a novel approach to generate image annotations by transferring labels from aerial images to UAV images and refine the annotations using a densely connected CRF model with an embedded naive Bayes classifier. The generated annotations not only present correct semantic labels, but also preserve accurate class boundaries. To validate the utility of these automatic annotations, we deploy them as training data for pixel-wise image segmentation and compare the results with the segmentation using manual annotations. Experiment results demonstrate that the automatic annotations can achieve comparable segmentation accuracy as the manual annotations.
关键词： Label propagation,Image segmentation,Automatic image annotation,Deep learning
更新于2025-09-09 09:28:46
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy*
摘要： Segmentation of microvessels measured using two-photon microscopy has been studied in the literature with limited success due to uneven intensities associated with optical imaging and shadowing effects. In this work, we address this problem using a customized version of a recently developed fully convolutional neural network, namely, FC-DensNets. To train and validate the network, manual annotations of 8 angiograms from two-photon microscopy was used. Segmentation results are then compared with that of a state-of-the-art scheme that was developed for the same purpose and also based on deep learning. Experimental results show improved performance of used FC-DenseNet in providing accurate and yet end-to-end segmentation of microvessels in two-photon microscopy.
关键词： deep learning,two-photon microscopy,FC-DenseNets,microvessels,Segmentation
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC, Canada (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - A Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks
摘要： This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level labeling of cloud regions in a Landsat 8 image. Also, a gradient-based identification approach is proposed to identify and exclude regions of snow/ice in the ground truths of the training set. We show that using the hybrid of the two methods (threshold-based and deep-learning) improves the performance of the cloud identification process without the need to manually correct automatically generated ground truths. In average the Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.
关键词： deep-learning,Landsat 8,FCN,image segmentation,U-Net,remote sensing,CNN,Cloud detection
更新于2025-09-09 09:28:46
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CoinNet: Copy Initialization Network for Multispectral Imagery Semantic Segmentation
摘要： Remote sensing imagery semantic segmentation refers to assigning a label to every pixel. Recently, deep convolutional neural networks (CNNs)-based methods have presented an impressive performance in this task. Due to the lack of sufficient labeled remote sensing images, researchers usually utilized transfer learning (TL) strategies to fine tune networks which were pretrained in huge RGB-scene data sets. Unfortunately, this manner may not work if the target images are multispectral/hyperspectral. The basic assumption of TL is that the low-level features extracted by the former layers are similar in most data sets, hence users only require to train the parameters in the last layers that are specific to different tasks. However, if one should use a pretrained deep model imagery in RGB data for multispectral /hyperspectral semantic segmentation, the structure of the input layer has to be adjusted. In this case, the first convolutional layer has to be trained using the multispectral /hyperspectral data sets which are much smaller. Apparently, the feature representation ability of the first convolutional layer will decrease and it may further harm the following layers. In this letter, we propose a new deep learning model, COpy INitialization Network (CoinNet), for multispectral imagery semantic segmentation. The major advantage of CoinNet is that it can make full use of the initial parameters in the pretrained network’s first convolutional layer. Comparison experiments on a challenging multispectral data set have demonstrated the effectiveness of the proposed improvement. The demo and a trained network will be published in our homepage.
关键词： deep convolutional network,CoinNet,transfer learning (TL),semantic segmentation
更新于2025-09-09 09:28:46
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A New Probabilistic Representation of Color Image Pixels and Its Applications
摘要： This paper proposes a novel probabilistic representation of color image pixels (PRCI) and investigates its applications to similarity construction in motion estimation and image segmentation problems. The PRCI explores the mixture representation of the input image(s) as prior information and describes a given color pixel in terms of its membership in the mixture. Such representation greatly simplifies the estimation of the probability density function from limited observations and allows us to derive a new probabilistic pixel-wise similarity measure based on the continuous domain Bhattacharyya coefficient. This yields a convenient expression of the similarity measure in terms of the pixel memberships. Furthermore, this pixel-wise similarity is extended to measure the similarity between two image regions. The usefulness of the proposed pixel/region-wise similarities is demonstrated by incorporating them respectively in a dense image descriptor-based multi-layered motion estimation problem and an unsupervised image segmentation problem. Experimental results show that i) the integration of the proposed pixel-wise similarity in dense image-descriptor construction yields improved peak signal to noise ratio performance and higher tracking accuracy in the multi-layered motion estimation problem, and ii) the proposed similarity measures give the best performance in terms of all quantitative measurements in the unsupervised superpixel-based image segmentation of the MSRC and BSD300 datasets.
关键词： Pixel-wise similarity,registration,Region-wise similarity,Image matching,and segmentation,Image descriptors,Probabilistic color representation
更新于2025-09-09 09:28:46
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Determination of optimum segmentation parameter values for extracting building from remote sensing images
摘要： Lately, with progresses in remote sensing information techniques and the growingly and unprecedented uses of its uses, remote sensing became a science that cannot be dispensed with in most ?elds and ground object extraction has turned out to be more exact. Remote sensing image of high spatial resolution gives more inconspicuous components for instance, shape, color, size. The use of the old pixel-based method of classi?cation images inevitably leads to a signi?cant sacri?cing of image classi?cation accuracy. The use of unconventional methods such as object based image analysis (OBIA) to obtain data from image of high spatial resolution becomes the focus of many researchers. The initial phase of the OBIA technique is segmentation, which is a procedure that partition an image into moderately homogeneous areas named segments. Because the conventional pixel-based method does not suit the classi?cation of remote sensing images with spatial resolution, it has been replaced by a new standard method OBIA. Choosing the parameters of segmentation is a fundamental stage in the image segmentation process, the main purpose of this research is to try to ?nd the best values or near the best values for the parameters of image segmentation. It is expected to obtain an image object that expresses the reality and therefore obtain the accuracy of the classi?cation of the satellite images if the selection of good and appropriate segmentation parameters well done. There are three parameters that have a signi?cant impact on the accuracy of the results of the segmentation must be determined their values with high precision, where they can be arranged from the lowest up, these are compactness, shape scale. Dependence on use of visual analysis alone in determining the values of these parameters is a waste of time. Consequently, in this paper, a set of segmentations was carried out utilizing the Worldview-3 image with different values for the segmentation parameters to de?ne ideal or close ideal segmentation parameters used to extracting building from remote sensing images.
关键词： Multiresolution segmentation,Optimum segmentation parameter,Segmentation quality
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - Camera-Based Semantic Enhanced Vehicle Segmentation for Planar LIDAR
摘要： Vehicle segmentation is an important step in perception for autonomous driving vehicles, providing object-level environmental understanding. Its performance directly affects other functions in the autonomous driving car, including Decision-Making and Trajectory Planning. However, this task is challenging for planar LIDAR due to its limited vertical field of view (FOV) and quality of points. In addition, directly estimating 3D location, dimensions and heading of vehicles from an image is difficult due to the limited depth information of a monocular camera. We propose a method that fuses a vision-based instance segmentation algorithm and LIDAR-based segmentation algorithm to achieve an accurate 2D bird's-eye view object segmentation. This method combines the advantages of both camera and LIDAR sensor: the camera helps to prevent over-segmentation in LIDAR, and LIDAR segmentation removes false positive areas in the interest regions in the vision results. A modified T-linkage RANSAC is applied to further remove outliers. A better segmentation also results in a better orientation estimation. We achieved a promising improvement in average absolute heading error and 2D IOU on both a reduced-resolution KITTI dataset and our Cadillac SRX planar LIDAR dataset.
关键词： autonomous driving,Vehicle segmentation,T-linkage RANSAC,fusion,camera,semantic segmentation,LIDAR
更新于2025-09-04 15:30:14
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Comparison of level set models in image segmentation
摘要： Image segmentation is one of the most important tasks in modern imaging applications, which leads to shape reconstruction, volume estimation, object detection and classification. One of the most popular active segmentation models is level set models which are used extensively as an important category of modern image segmentation technique with many different available models to tackle different image applications. Level sets are designed to overcome the topology problems during the evolution of curves in their process of segmentation while the previous algorithms cannot deal with this problem effectively. As a result, there is often considerable investigation into the performance of several level set models for a given segmentation problem. It would therefore be helpful to know the characteristics of a range of level set models before applying to a given segmentation problem. In this study, the authors review a range of level set models and their application to image segmentation work and explain in detail their properties for practical use.
关键词： topology problems,curve evolution,level set models,active segmentation,image segmentation
更新于2025-09-04 15:30:14