- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Iterative reconstruction with segmentation penalty for PET
摘要： Segmentation technique is widely accepted to reduce noise propagation from transmission scanning for positron emission tomography. The conventional routine is to sequentially perform reconstruction and segmentation. A smoothness penalty is also usually used to reduce noise, which can be imposed to both the ML and WLS estimators. In this paper we replace the smoothness penalty by a segmentation penalty that biases the object toward piecewise-homogeneous reconstruction. Two updating algorithms are developed to solve the penalized ML and WLS estimates, which monotonically decrease the cost functions. Experimental results on simulated phantom and real clinical data were both given to demonstrate the effectiveness and efficiency of the algorithms which were proposed.
关键词： space alternating descent,auxiliary function,fuzzy c-means clustering (FCM),Segmentation penalty
更新于2025-09-04 15:30:14
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[IEEE 2018 15th European Radar Conference (EuRAD) - Madrid, Spain (2018.9.26-2018.9.28)] 2018 15th European Radar Conference (EuRAD) - Modifications of the OPTICS Clustering Algorithm for Short-Range Radar Tracking Applications
摘要： Short-Range radar systems with high-resolution produce multiple detections for each target. To combine detections belonging to the same targets, cluster algorithms can be employed. However, only selected cluster algorithms (often with many adaptions) produce satisfactory output. In this paper, we present modification and application of so called Ordering Points To Identify the Clustering Structure algorithm in order to make it applicable for tracking and feature extraction using a high-resolution radar sensor.
关键词： millimeter wave radar,clustering algorithms,radar tracking
更新于2025-09-04 15:30:14
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A Novel Algorithm for Image Denoising Using DT-CWT
摘要： This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding - soft-clustering technique. The clustering techniques classify the noisy and image pixels based on the neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with RMSE to assess the quality of denoised images.
关键词： Soft-Clustering,Image Denoising,PSNR,Tap-10 Filter banks,DTCWT
更新于2025-09-04 15:30:14
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[Institution of Engineering and Technology 12th European Conference on Antennas and Propagation (EuCAP 2018) - London, UK (9-13 April 2018)] 12th European Conference on Antennas and Propagation (EuCAP 2018) - Effect of Line of Sight in Clustering Distribution of Signal Contribution at 5.8 GHz Indoor Environments
摘要： Indoor propagation at 5 GHz band is receiving new interest as 2.4 GHz band is becoming more and more saturated. In this contribution, we studied the clustering distribution generated in the delay domain, comparing both line of sight and non-line of sight conditions. The work is based on the results of a large broadband measurement campaign performed within an indoor environment. We also presented and described a comparison between both scenarios, providing insight on the behavior of such environments at 5.8 GHz.
关键词： indoor,clustering,propagation,line of sight,measurement
更新于2025-09-04 15:30:14
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Mineral identification in LWIR hyperspectral imagery applying sparse-based clustering
摘要： An assessment of mineral identification applying hyperspectral infrared imagery in laboratory conditions is presented here and strives to identify nine different minerals (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, quartz). A hyperspectral camera in Long-Wave Infrared (LWIR, 7.7–11.8 μm) with a LW-macro lens, an infragold plate, and a heating source are instruments used in the experiment. For automated identification, a Sparse Principal Component Analysis (Sparse PCA)-based K-means clustering is employed to categorise all pixel-spectra in different groups. Then the best representatives of each cluster (using spectral averaging) are chosen to compare these spectra to ASTER spectral library of JPL/NASA through spectral comparison techniques. Spectral angle mapper (SAM) and Normalized Cross Correlation (NCC) are two of such techniques, which are used herein to measure the spectral difference. In order to evaluate robustness of clustering results among the minerals spectra, we have added three levels of Gaussian and salt&pepper noise, 0%; 2%, and 4%, to input spectra which dropped the accuracy percentage from more than 84.73%, for 0% added noise, to 44.57%, for 2% average of both additive noise, and 22.21%, for 4% additive noise. The results conclusively indicate a promising performance but noise sensitive behaviour of the proposed approach.
关键词： mineral identification,Hyperspectral imagery,sparse principle components analysis,clustering
更新于2025-09-04 15:30:14
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High‐ and Ultra‐High definition of IR spectral histopathology gives an insight into chemical environment of lung metastases in breast cancer
摘要： Using high- (HD) and ultra-high-definition (UHD) of Fourier Transform Infrared (FTIR) spectroscopic imaging, we characterised spectrally pulmonary metastases in a murine model of breast cancer comparing them with histopathological results (H&E staining). This comparison showed excellent agreement between the methods in case of localisation of metastases with size below 1 mm and revealed that label-free HD and UHD IR spectral histopathology distinguish the type of neoplastic cells. We primary focused on differentiation between metastatic foci in the pleural cavity from cancer cells present in lung parenchyma and inflamed cells present in extracellular matrix of lungs due to growing of advanced metastases. In addition, a combination of unsupervised clustering and IR imaging indicated the high sensitivity of FTIR spectroscopy to identify chemical features of small macrometastases located under the pleural cavity and during epithelial–mesenchymal transition (EMT). FTIR based spectral histopathology was proved to detect not only phases of breast cancer metastasis to lungs but also to differentiate various origins of metastases seeded from breast cancer.
关键词： pulmonary metastases,high and ultra-high definition of FTIR imaging,spectral histopathology,breast cancer,unsupervised clustering
更新于2025-09-04 15:30:14
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[IEEE 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII) - Chennai (2018.3.22-2018.3.24)] 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII) - An Approach to Extract Optic-Disc from Retinal Image Using K-Means Clustering
摘要： generally, retinal picture valuation is commonly executed to appraise the diseases. In this paper, an image examination technique is implemented to extract the Retinal-Optic-Disc (ROD) to assess its condition. An approach based on the combination of Kapur’s entropy and K-means clustering is considered here to mine the optic disc region from the RGB retinal picture. During the experimental implementation, this approach is tested with the DRIVE and RIM-ONE databases. Initially, the DRIVE pictures are considered to appraise the proposed approach and is later, considered the ROD, comparative analyses with the expert’s Ground-Truths are carried out and the image similarity values are then recorded. This approach is then validated against the Otsu’s+levelset existing in the literature. All these experiments are implemented using Matlab2010. The outcome of this procedure confirms that, proposed work provides better picture similarity values compared to Otsu’s+levelset. Hence, in future, this procedure can be considered to evaluate the clinical retinal images.
关键词： Optic-disc,K-means clustering,validation.,Retinal picture,Kapur’s entropy
更新于2025-09-04 15:30:14
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Peekaboo-Where are the Objects? Structure Adjusting Superpixels
摘要： This paper addresses the search for a fast and meaningful image segmentation in the context of k-means clustering. The proposed method builds on a widely-used local version of Lloyd’s algorithm, called Simple Linear Iterative Clustering (SLIC). We propose an algorithm which extends SLIC to dynamically adjust the local search, adopting superpixel resolution dynamically to structure existent in the image, and thus provides for more meaningful superpixels in the same linear runtime as standard SLIC. The proposed method is evaluated against state-of-the-art techniques and improved boundary adherence and undersegmentation error are observed, whilst still remaining among the fastest algorithms which are tested.
关键词： Image texture analysis,Image segmentation,Clustering algorithms
更新于2025-09-04 15:30:14
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High-Dimensional Mixture Models for Unsupervised Image Denoising (HDMI)
摘要： This work addresses the problem of patch-based image denoising through the unsupervised learning of a probabilistic high-dimensional mixture model on the noisy patches. The model, called HDMI, proposes a full modeling of the process that is supposed to have generated the noisy patches. To overcome the potential estimation problems due to the high dimension of the patches, the HDMI model adopts a parsimonious modeling which assumes that the data live in group-specific subspaces of low dimensionalities. This parsimonious modeling allows us in turn to get a numerically stable computation of the conditional expectation of the image which is applied for denoising. The use of such a model also permits us to rely on model selection tools, such as BIC, to automatically determine the intrinsic dimensions of the subspaces and the variance of the noise. This yields a denoising algorithm that can be used both when the noise level is known and is unknown.
关键词： image denoising,parsimonious mixture model,model selection,high-dimensional clustering,intrinsic dimension estimation,patch-based representation
更新于2025-09-04 15:30:14
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Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization
摘要： We address the problem of scene classification from optical remote sensing (RS) images based on the paradigm of hierarchical metric learning. Ideally, supervised metric learning strategies learn a projection from a set of training data points so as to minimize intraclass variance while maximizing the interclass separability to the class label space. However, standard metric learning techniques do not incorporate the class interaction information in learning the transformation matrix, which is often considered to be a bottleneck while dealing with fine-grained visual categories. As a remedy, we propose to organize the classes in a hierarchical fashion by exploring their visual similarities and subsequently learn separate distance metric transformations for the classes present at the nonleaf nodes of the tree. We employ an iterative maximum-margin clustering strategy to obtain the hierarchical organization of the classes. Experiment results obtained on the large-scale NWPU-RESISC45 and the popular UC-Merced data sets demonstrate the efficacy of the proposed hierarchical metric learning-based RS scene recognition strategy in comparison to the standard approaches.
关键词： optical remote sensing (RS),Maximum margin clustering (MMC),metric learning
更新于2025-09-04 15:30:14