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Conference on Medical Imaging 2011 - Image Processing

作者： Ghose, Soumya Oliver, Arnau Marti, Robert Llado, Xavier Freixenet, Jordi Vilanova, Joan C. Meriaudeau, Fabrice Univ Girona Comp Vis & Robot Grp Campus MontiliviEdifici P 4 Girona 17071 Catalonia Spain

ISBN: (纸本)9780819485045

Real-time fusion of Magnetic Resonance (MR) and Trans Rectal Ultra Sound (TRUS) images aid in the localization of malignant tissues in TRUS guided prostate biopsy. Registration performed on segmented contours of the prostate reduces computational complexity and improves the multimodal registration accuracy. However, accurate and computationally efficient segmentation of the prostate in TRUS images could be challenging in the presence of heterogeneous intensity distribution inside the prostate gland, and other imaging artifacts like speckle noise, shadow regions and low Signal to Noise Ratio (SNR). In this work, we propose to enhance the texture features of the prostate region using local binary patterns (LBP) for the propagation of a shape and appearance based statistical model to segment the prostate in a multi-resolution framework. A parametric model of the propagating contour is derived from Principal Component Analysis (PCA) of the prior shape and texture information of the prostate from the training data. The estimated parameters are then modified with the prior knowledge of the optimization space to achieve an optimal segmentation. The proposed method achieves a mean Dice Similarity Coefficient (DSC) value of 0.94 +/- 0.01 and a mean segmentation time of 0.68 +/- 0.02 seconds when validated with 70 TRUS images of 7 datasets in a leave-one-patient-out validation framework. Our method performs computationally efficient and accurate prostate segmentation in the presence of intensity heterogeneities and imaging artifacts.

关键词： Prostate Segmentation Active Appearance Model Texture local binary patterns

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Virus Texture Analysis Using local binary patterns and Radial Density Profiles

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16th Iberoamerican Congress on Pattern Recognition

作者： Kylberg, Gustaf Uppstrom, Mats Sintorn, Ida-Maria Ctr Image Anal Lagerhyddsvagen 2 SE-75105 Uppsala Sweden Vironova AB SE-11330 Stockholm Sweden

ISBN: (纸本)9783642250842

We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures.

关键词： virus morphology texture analysis local binary patterns radial density profiles

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Anti-rotation binary Descriptor for Circular Image Recognition

Anti-rotation Binary Descriptor for Circular Image Recogniti...

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Chinese Automation Congress

作者： Honglei Chen Dongbo Zhang Liangling Yi Hongzhong Tang Haixia Xu The College of Information Engineering Xiangtan University Xiangtan China The College of Information Engineering Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan China

Based on pairs of spatial symmetric patches, a novel efficient and distinctive binary descriptor was proposed in this paper. To achieve rotation invariance during feature computation, a local coordinate system was found with radial transform technology. On the basis of that, local binary patterns against rotation could be extracted. Meanwhile, the circular image was divided into a set of overlapped annular regions, and pairs of patch description were constructed with histogram within the ring. Finally the rotation invariant image description could be obtained by concatenating the ring features from inner to outer. The performance of proposed method was tested with 3 data sets. The test results showed that its recognition accuracy reached 100%, 100% and 97.07% respectively, which were superior to the results of traditional methods based on LBP feature. Moreover, the algorithm was efficient, as single point feature computation needed only 0.045ms.

关键词： Linage descriptor local binary patterns Image recognition Rotation invariance

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Breast mass classification on mammograms using radial local ternary patterns

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COMPUTERS IN BIOLOGY AND MEDICINE 2016年 72卷 43-53页

作者： Muramatsu, Chisako Hara, Takeshi Endo, Tokiko Fujita, Hiroshi Gifu Univ Grad Sch Med Dept Intelligent Image Informat 1-1 Yanagido Gifu 5011194 Japan Nagoya Med Ctr Dept Adv Diag 4-1-1 Nakaku Sannomaru Nagoya Aichi 4600001 Japan Higashi Nagoya Natl Hosp Dept Breast Surg Meito Ku 5-101 Umemorizaka Nagoya Aichi 4658620 Japan

Textural features can be useful in differentiating between benign and malignant breast lesions on mammograms. Unlike previous computerized schemes, which relied largely on shape and margin features based on manual contours of masses, textural features can be determined from regions of interest (ROIs) without precise lesion segmentation. In this study, therefore, we investigated an ROI-based feature, namely, radial local ternary patterns (RLTP), which takes into account the direction of edge patterns with respect to the center of masses for classification of ROIs for benign and malignant masses. Using an artificial neural network (ANN), support vector machine (SVM) and random forest (RF) classifiers, the classification abilities of RLTP were compared with those of the regular local ternary patterns (LW), rotation invariant uniform (RIU2) LTP, texture features based on the gray level co-occurrence matrix (GLCM), and wavelet features. The performance was evaluated with 376 ROIs including 181 malignant and 195 benign masses. The highest areas under the receiver operating characteristic curves among three classifiers were 0.90, 0.77, 0.78, 0.86, and 0.83 for RLTP, LW, RIU2-LTP, GLCM, and wavelet features, respectively. The results indicate the usefulness of the proposed texture features for distinguishing between benign and malignant lesions and the superiority of the radial patterns compared with the conventional rotation invariant patterns. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： local binary patterns local ternary patterns Texture feature Breast masses Mammograms Classification

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An investigation on the use of local multi-resolution patterns for image classification

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INFORMATION SCIENCES 2016年 361卷 1-13页

作者： Gonzalez, Elena Bianconi, Francesco Fernandez, Antonio Univ Vigo Sch Ind Engn Campus Univ Vigo 36310 Spain Univ Perugia Dept Engn Via G Duranti 93 I-06125 Perugia Italy

The subject of this study is the use of local multi-dimensional patterns for image classification. The contribution is both theoretical and experimental: on the one hand the paper introduces a complete and general mathematical model for encoding multi-resolution, rotation-invariant local patterns;on the other experimentally evaluates the use of multi resolution patterns for image classification both from an information- and performance based standpoint. The results indicate that the joint multi-resolution model proposed in the paper can actually convey an additional amount of information with respect to the marginal model;but also that the marginal model (i.e. concatenation of features computed at different resolutions) can be a good enough approximation for practical applications. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Rotation-invariant multi-resolution patterns local binary patterns Texture spectrum Image classification

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ENHANCED LBP TEXTURE FEATURES FROM TIME FREQUENCY REPRESENTATIONS FOR ACOUSTIC SCENE CLASSIFICATION

ENHANCED LBP TEXTURE FEATURES FROM TIME FREQUENCY REPRESENTA...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Abidin, Shamsiah Togneri, Roberto Sohel, Ferdous Univ Western Australia Sch Elect Elect & Comp Engn Perth WA 6009 Australia Murdoch Univ Sch Engn & Informat Technol Perth WA 6150 Australia

ISBN: (纸本)9781509041176

This paper introduces the use of local binary patterns (LBP) extracted from a time-frequency representation (TFR) for acoustic scene classification. As LBP provides a description of the global TFR texture we propose a novel zoning mechanism that provides a simple solution to extract spectrally relevant local features which better characterize the audio TFRs. To further improve the classification performance, we perform feature and score level fusion of the proposed LBP (with zoning) with histogram of gradients (HOG) of the TFR images. Our technique demonstrates an improved performance by achieving a classification accuracy of 95.2% using a fusion of time-frequency derived features.

关键词： acoustic scene local binary patterns feature extraction time-frequency analysis fusion

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A Comparison of Facial Feature Representation Methods for Automatic Facial Expression Recognition 23

A Comparison of Facial Feature Representation Methods for Au...

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Conference of South-African-Institute-of-Computer-Scientists-and-Information-Technologists (SAICSIT)

作者： Deaney, Waleed Venter, Isabella Ghaziasgar, Mehrdad Dodds, Reg Univ Western Cape Dept Comp Sci Bellville Western Cape South Africa

ISBN: (纸本)9781450352505

A machine translation system that can convert South African Sign Language video to English audio or text and vice versa in real-time would be immensely beneficial to the Deaf and hard of hearing. Sign language gestures are characterised and expressed by five distinct parameters: hand location;hand orientation;hand shape;hand movement and facial expressions. The aim of this research is to recognise facial expressions and to compare the following feature descriptors: local binary patterns;compound local binary patterns and histogram of oriented gradients in two testing environments, a subset of the BU3D-FE dataset and the CK+ dataset. The overall accuracy, accuracy across facial expression classes, robustness to test subjects, and the ability to generalise of each feature descriptor within the context of automatic facial expression recognition are analysed as part of the comparison procedure. Overall, HOG proved to be a more robust feature descriptor to the LBP and CLBP. Furthermore, the CLBP can generally be considered to be superior to the LBP, but the LBP has greater potential in terms of its ability to generalise.

关键词： sign language facial expression recognition face detection feature extraction support vector machines local binary patterns compound local binary patterns histogram of orientated gradients

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Weighty LBP: A New Selection Strategy of LBP Codes Depending on Their Information Content 19th

Weighty LBP: A New Selection Strategy of LBP Codes Depending...

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19th International Conference on Image Analysis and Processing (ICIAP)

作者： De Marsico, Maria Riccio, Daniel Sapienza Univ Rome Rome Italy Univ Naples Federico II Naples Italy

ISBN: (纸本)9783319685601;9783319685595

This paper presents a novel variation of the use of LBP codes. Similarly to Uniform LBP and local Salient patterns (LSP), it aims at both obtaining an effective texture description, and decreasing the length of the feature vectors, i.e., of the chains of LBP histograms. Instead of considering uniform codes, we rather consider the codes providing the highest "representativeness" power with respect to texture features. We identify this subset of codes by a generalized notion of entropy. This allows determining the most informative items in an homogeneous set.

关键词： local binary patterns Information content Entropy

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LBP Features for Hand-Held Ground Penetrating Radar 22

LBP Features for Hand-Held Ground Penetrating Radar

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Conference on Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII

作者： Harris, Samuel Alvey, Brenden Ho, Dominic K. C. Zare, Alina Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32601 USA

ISBN: (数字)9781510608665

ISBN: (纸本)9781510608665

Ground penetrating radar (GPR) has the ability to detect buried targets with little or no metal content. Achieving superior detection performance with a hand-held GPR can be very challenging due to the quality of the data, inconsistency of target signatures, variety of target types, and effects of a human operator. In this paper, we investigate the use of a local binary patterns (LBP) feature vector for target versus non-target discrimination from hand-held GPR data. First, a prescreener algorithm is applied to the GPR data. Then, a GPR B-scan is gathered at each prescreener alarm location and separated into several spatial and depth regions. LBP processing is applied to each spatial and depth cell individually and the LBP features from each cell are group together to form a feature vector. The resulting LBP features are invariant to amplitude scaling and represent the texture in the data. Using this feature vector, a classifier is trained to perform target versus non-target discrimination at each prescreener declaration. Experimental results illustrate the ability of the LBP features to improve detection of buried targets, especially low-metal and non-metal anti-tank and anti-personnel targets.

关键词： Hand-held ground penetrating radar local binary patterns detection classification

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A New Finger Vein Recognition Method Based on LBP and 2DPCA

A New Finger Vein Recognition Method Based on LBP and 2DPCA

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第37届中国控制会议

作者： Na Hu Hui Ma Tao Zhan College of Electronic Engineering Heilongjiang University

Finger vein recognition is an emerging biometrics technology. Using the global information of finger vein images, this paper presents a method of finger vein recognition combining the texture feature fusion algorithm of LBP operator and 2 DPCA algorithm. First of all, the texture feature of finger vein image was extracted by LBP operator, and then a two-dimensional principal component analysis is used to project the transformed image for feature extraction. Euclidean distance measures the similarity between test and training samples. Experiments in the Tianjin Intelligence Lab image database and FV-USM finger vein database show that the proposed method is effective and reliable and improves the performance of a finger-vein identification system.

关键词： finger vein recognition feature extraction local binary patterns two-dimensional principal component

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