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Journal ArticleDOI

Ultrasound Image Despeckling using Local Binary Pattern Weighted Linear Filtering

TL;DR: A new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images achieves good results in reducing the noise without affecting the image content.
Abstract: Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images.

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Citations
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01 Jan 1979
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

1,758 citations

Journal ArticleDOI
TL;DR: The IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (TUFFC) now accepts color figures online with corresponding grayscale figures in print without additional charges if authors follow the multimedia manuscript submission procedure in the “Information for Contributors”.
Abstract: This page shows some examples of multimedia files. It is also available at: http://www.ieee-uffc.org/tr/mexample.pdf. For submission of multimedia manuscripts to TUFFC, please follow “Information for Contributors” at: http://www.ieeeuffc.org/tr/contrib.pdf. Multimedia Example Created by Jian-yu Lu, Editor-in-Chief, 07/07/03 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society 1. Color Figure: The IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (TUFFC) now accepts color figures online with corresponding grayscale figures in print without additional charges if authors follow the multimedia manuscript submission procedure in the “Information for Contributors”. The “color figures” icon below indicates in the print that a color version of the figure is available online. It also links to the color figure submitted originally by the authors for viewing details. Because of the sizes of color figures and the additional editorial work such as making two sets of PostScript files in which they remain identical after replacing grayscale with color, authors should request color figures online only when it is necessary. The color figure must be in JPEG (Joint Photographic Expert Group) or GIF (Graphics Interchange Format) format to reduce file size and to decrease the bandwidth usage on the TUFFC server. Fig. 1. An eight-layer printed circuit board (PCB) used primarily for multi-channel ultrasound signal reception and storage. The board was designed in the Ultrasound Lab at The University of Toledo, led by Dr. Jian-yu Lu. It is one of the many PCBs designed for a high-frame rate medical ultrasound imaging system [1], which consists of 128 linear, ±144V peak, 0.05-10MHz, and 12-bit arbitrary waveform generators for the production of ultrasound signals or for other research purposes. 2. Movies: Please click on the movie icon to see a movie. The two movies below give you an idea on the file size versus movie quality. “VCD quality” here means 1150kbits/s for video and 224kbits/s for 16-bit MP2 stereo audio at 44.1KHz sampling rate. Fig. 2. An introduction to the Ultrasound Lab at The University of Toledo. 3. Movies and Animations: Please click on the movie icons to see movies or animations. Fig. 3. Circuit design and construction of the imaging system. 4. Movies and Animation: Please click on the movie icons to see movies or animation. Movie [File size: 785KB; Format: MPEG1; Resolution: 320X240; Duration: 9 seconds]

290 citations

Book
01 Jan 2010
TL;DR: Methods and Algorithms of Digital Filtering of Signal/Image Processing and Computer Generated Holograms.
Abstract: 1. Introduction.- 2. Optical Signals and Transforms.- 3. Digital Representation of Signals.- 4. Digital Representation of Signal Transformations.- 5. Methods and Algorithms of Digital Filtering.- 6. Fast Algorithms.- 7. Statistical Methods and Algorithms.- 8. Sensor Signal Perfecting, Image Restoration, Reconstruction and Enhancement.- 9. Image Resampling and Geometrical Transformations.- 10. Signal Parameter Estimation and Measurement. Object Localization.- 11. Target Location in Clutter.- 12. Nonlinear Filters in Signal/Image Processing.- 13. Computer Generated Holograms.

72 citations

Journal ArticleDOI
TL;DR: A new orthonormal wavelet family designed to reduce noise in ultrasonic medical images, focusing on speckle noise, leads to better results than the ones obtained with the wavelets from the Daubechies, Symmlet, Coiflet and Biorthogonal families, in terms of both noise reduction and edge preservation.

26 citations

Journal Article
TL;DR: An enhanced medical decision support system for classification of Ultrasound Kidney images is developed for health care and is expected to provide support for the medical practitioners for decision making to provide an enhanced health care.
Abstract: An enhanced medical decision support system for classification of Ultrasound Kidney images is developed for health care and presented in this paper. The image enhancement was done by removing the speckle, salt and pepper noises using fuzzy c means filtering and the Gray Level Coocurrence Matrix was obtained for feature extraction. Gabor wavelets and Histogram equalization were used for the selection of texture features. The classification is done using SVM, ANN, K-NN and Hybrid classifiers and the accuracy of classification was found to be 99.6% for the SVM- ANN hybrid classifier. The developed system is expected to provide support for the medical practitioners for decision making to provide an enhanced health care.

4 citations


Cites methods from "Ultrasound Image Despeckling using ..."

  • ...[4] also proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images....

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References
More filters
Journal ArticleDOI
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Abstract: Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.

14,245 citations

Journal ArticleDOI
TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >

12,560 citations

Journal ArticleDOI
TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

5,563 citations

01 Jan 1979
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

1,758 citations

Journal ArticleDOI
TL;DR: A novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence that provides many advantages compared to the state-of-the-art.
Abstract: This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.

1,355 citations