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Detection of Modic changes in MR images of spine using local binary patterns

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2019年第1期39卷 17-29页

作者： Athertya, Jiyo S. Kumar, Gurunathan Saravana Govindaraj, Jayaraj IIT Madras Dept Engn Design 3D Imaging & Addit Mfg Lab Chennai Tamil Nadu India Indian Inst Technol Dept Engn Design Room 418 Chennai 600036 Tamil Nadu India Apollo Specialty Hosp Dept Radiol Chennai Tamil Nadu India

Background and objective: With increase in prevalence of lower back pain, fast and reliable computer aided methods for clinical diagnosis associated with the same is needed for improving the healthcare reach. The magnetic resonance images exhibit a change in signal intensity on the vertebral body close to end plates, which are termed as Modic changes (MC), and are known to be clear indicators of lower back pain. The current work deals with computer aided methods for automating the classification of signal changes between normal and degenerate cases so as to aid physicians in precise and suitable diagnosis for the ailment. Methods: In order to detect Modic changes in vertebrae, initially the vertebrae are segmented from sagittal MR T1 and T2 imaged using a semi automatic cellular automata based segmentation. This is followed by textural feature extraction using local binary patterns (LBP) and its variants. Various classifiers based on machine learning approaches using Random Forest, kNN, Bayes and SVM were evaluated for its classification performance. Since medical image dataset in general have bias towards healthy and diseased state, data augmentation techniques were also employed. Results: The implemented method is tested and validated over a dataset containing 100 patients. The proposed framework achieves an accuracy of 81% and 91.7% with and without augmentation of data respectively. A comparative study with the state of art methods reported in literature shows that the method proposed in better in terms of computational cost without any compromise on classification accuracy. Conclusion: A novel approach to identify MC in vertebrae by exploiting textural features is proposed. This shall assist radiologists in detecting abnormalities and in treatment planning. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Modic changes Vertebral degenerations local binary patterns Cellular automata Feature detection Data augmentation

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Gender Recognition with Uniform local binary patterns 26

Gender Recognition with Uniform Local Binary Patterns

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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Cerkezi, Llukman Topal, Cihan Anadolu Univ Elekt Elekt Muhendisligi Bolumu Eskisehir Turkey

ISBN: (纸本)9781538615010

Automatic gender recognition has been applied in many areas such as analyzing the structure of demographic social environments. In this paper we present a gender recognition algorithm based on uniform local binary pattern (LBP). LBP has proven to be very efficient image descriptor in many pattern recognition applications. In feature extraction step of the proposed method, a well known variant of LBP, uniform local binary pattern is utilized. Before applying LBP the face detection and alignment tasks are performed respectively. By doing alignment, the overlapping of similar part of face has been achieved. Then, we combine local uniform LBP histograms to create the final feature vector. We show that in the experiments carried out in Feret face dataset that the proposed method can achieve up to 93.57% recognition rate.

关键词： face local binary patterns gender recognition

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1-D local binary patterns BASED VAD USED INHMM-BASED IMPROVED SPEECH RECOGNITION

1-D LOCAL BINARY PATTERNS BASED VAD USED INHMM-BASED IMPROVE...

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20th European Signal Processing Conference (EUSIPCO)

作者： Zhu, Qiming Chatlani, Navin Soraghan, John J. Univ Strathclyde Ctr Excellence Signal & Image Proc CeSIP Glasgow Lanark Scotland

ISBN: (纸本)9781467310680

In this paper, 1-D local binary patterns (LBP) are proposed to be used in speech signal segmentation and voice activation detection (VAD) and combined with hidden Markov model (HMM) for advanced speech recognition. Speech is firstly de-noised by Adaptive Empirical Model Decomposition (AEMD), and then processed using LBP based VAD. The short-time energy of the speech activity detected from the VAD is finally smoothed and used as the input of the HMM recognition process. The enhanced performance of the proposed system for speech recognition is compared with other VAD techniques at different SNRs ranging from 15 dB to a robust noisy condition at -5 dB.

关键词： Speech Enhancement using Adaptive Empirical Model Decomposition (AEMD) local binary patterns Voice Activity Detection Noise Reduction Hidden Markov Model

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Gabor-Filtering-Based Completed local binary patterns for Land-Use Scene Classification 1

Gabor-Filtering-Based Completed Local Binary Patterns for La...

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IEEE First International Conference on Multimedia Big Data

作者： Chen, Chen Zhou, Libing Guo, Jianzhong Li, Wei Su, Hongjun Guo, Fangda Univ Texas Dallas Dept Elect Engn Richardson TX 75083 USA Wuhan Text Univ Sch Elect & Elect Engn Wuhan Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing Jiangsu Peoples R China Univ Pavia Dept Elect Comp & Biomed Engn Pavia Italy

ISBN: (纸本)9781479986880

Remote sensing land-use scene classification has a wide range of applications including forestry, urban-growth analysis, and weather forecasting. This paper presents an effective image representation method, Gabor-filtering-based completed local binary patterns (GCLBP), for land-use scene classification. It employs the multi-orientation Gabor filters to capture the global texture information from an input image. Then, a local operator called completed local binary patterns (CLBP) is utilized to extract the local texture features, such as edges and corners, from the Gabor feature images and the input image. The resulting CLBP histogram features are concatenated to represent an input image. Experimental results on two datasets demonstrate that the proposed method is superior to several existing methods for land-use scene classification.

关键词： Gabor filtering local binary patterns land-use sence classification extreme learning machine

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local binary patterns versus signal processing texture analysis: a study from a performance evaluation perspective

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SENSOR REVIEW 2012年第2期32卷 149-162页

作者： Ghita, Ovidiu Ilea, Dana Fernandez, Antonio Whelan, Paul Dublin City Univ Sch Elect Engn Dublin 9 Ireland Univ Vigo Sch Ind Engn Vigo 36310 Spain

Purpose - The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro-level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters. Design/methodology/approach - The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases. Findings - The most important finding resulting from this study is that although the LBP/C and the multi-channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture. Practical implications - Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding. Originality/value - This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi-channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases.

关键词： Signal processing Pattern recognition Texture classification local binary patterns Rotational invariance Gabor filters Isotropic filters

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Age estimation from a face image in a selected gender-race group based on ranked local binary patterns

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COMPLEX & INTELLIGENT SYSTEMS 2017年第2期3卷 93-104页

作者： Rybintsev, Andrey Natl Res Univ Moscow Power Engn Inst Moscow Russia

An algorithm to classify people by age from face images based on a two-stage support vector regression is proposed. Only the most significant local binary patterns are used as descriptive features of an image. The distinctive feature of the proposed approach is in the use of a sequential procedure that involves classifying images of people first by gender, then by race in each gender group and only then by age within a selected gender-race group. In order to increase the accuracy of the classification, a bootstrapping procedure (learning on "hard" examples) is used at each stage. The use of this approach has made it possible to improve an accuracy of the classification by 12% for gender, by 15% for race and by 2 years for age (the Mean Absolute Error metric) in comparison with other known algorithms.

关键词： Machine learning Image classification Age classification local binary patterns Adaboost Support vector machine Bootstrapping Support vector regression

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Classification of Thermite Welding Defects using local binary patterns and K Nearest Neighbors 5

Classification of Thermite Welding Defects using Local Binar...

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5th IEEE Annual International Conference on Information Communications Technology and Society (ICTAS)

作者： Molefe, Mohale Emmanuel Tapamo, Jules-Raymond Univ KwaZulu Natal Sch Engn Durban South Africa

ISBN: (纸本)9781728180816

Radiography is a commonly used non-destructive method for inspecting thermite weld defects in welded rails. However, manual detection and classification of weld defects in radiographic images using human expertise remain a lengthy, costly, and subjective process. This process' success rate substantially depends on the inspector's ability to detect and classify defects. The development of an automated thermite weld defect detection and classification model will significantly improve railway infrastructure monitoring. This work proposes an image processing, and machine learning-based framework to automatically detect and classify thermite weld defects, in which the Chan-Vese Active Contour Model is used to define the Region of Interest and extract the weld joint. Features in the weld joint images are extracted using the local binary patterns descriptor. The extracted features are then used to train a K Nearest Neighbor classifier. The proposed method achieved an average classification accuracy of 94 %.

关键词： Thermite weld defects Chan-Vese Active Contour Model local binary patterns K Nearest Neighbors

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BACKGROUND MODELING USING local binary patterns OF MOTION VECTOR

BACKGROUND MODELING USING LOCAL BINARY PATTERNS OF MOTION VE...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Wang, Tingting Liang, Jiuzhen Wang, Xiaolong Wang, Shizheng Jiangnan Univ Sch Internet Things Engn Wuxi Peoples R China Chinese Acad Sci R& Ctr Internet Things Beijing Peoples R China

ISBN: (纸本)9781467344050;9781467344067

Pixel-domain analysis methods are widely adopted in background modeling, some of which are not only concerned by academia but also coming into view of industry. However, as the increasing data volume of video, how to process and analysis videos in a fast and effective way has still been an intractable problem in practical applications. Under this circumstance, surveillance video analysis in the compressed domain is indeed of strategic importance from the angle of balancing visual perception and processing speed, especially in modeling background and segmenting moving objects. Therefore, a background modeling method in the compressed domain is proposed to quickly extract moving objects in this paper. Our main contributions are: 1) a method to calculate MVLBP features based on MV amplitude in the compressed domain is presented;2) a background modeling and moving objects extraction method is designed in the compressed domain based on local binary patterns of Motion Vector (MVLBP). Experimental results show that our approach gives a stable performance in a shorter time in H. 264 compressed domain.

关键词： Background modeling motion vector compressed domain local binary patterns

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Labor Induction Failure Prediction Based on B-Mode Ultrasound Image Processing Using Multiscale local binary patterns

Labor Induction Failure Prediction Based on B-Mode Ultrasoun...

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International Conference on Optoelectronics and Image Processing (ICOIP)

作者： Pablo Vasquez, O. Arana, Nestor Izaguirre, Alberto Burgos, Jorge Mondragon Univ Arrasate Mondragon Spain BioCruces Hlth Res Inst Obstet & Gynecol Serv Bilbao Spain

ISBN: (纸本)9781509008803

Labor induction is defined as the artificial onset of labor for the purpose of vaginal birth. Cesarean section is one of the potential risks of labor induction as it occurs in about 20% of the inductions. A ripe cervix (soft and distensible) is needed for a successful labor. Changes occurring during the ripening process, will affect the interaction between cervical tissues and sound waves during ultrasound transvaginal scanning and will be perceived as gray level intensity variations in the echography image. Thus, a non-invasive method using image processing of ultrasound images may help in predicting the outcome of labor induction. In this paper a set of echography images from labor induction patients is analyzed using a multiscale methodology based on local binary patterns and circular Gabor filters. Results show that it is feasible to predict the outcome of a labor induction procedure using this method with a good accuracy.

关键词： component texture analysis cervix local binary patterns

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Improved Opponent Colour local binary patterns for Colour Texture Classification 6th

Improved Opponent Colour Local Binary Patterns for Colour Te...

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6th International Workshop on Computational Color Imaging (CCIW)

作者： Bianconi, Francesco Bello-Cerezo, Raquel Napoletano, Paolo Di Maria, Francesco Univ Perugia Dept Engn Via G Duranti 93 I-06135 Perugia Italy Univ Milano Bicocca Dept Informat Syst & Commun Viale Sarca 336 I-20125 Milan Italy

ISBN: (纸本)9783319560106;9783319560090

In this paper we introduce Improved Opponent Colour local binary patterns (IOCLBP), a conceptually simple yet effective descriptor for colour texture classification. The method was experimentally validated over eight datasets of colour texture images. The results show that IOCLBP outperformed other LBP variants and was at least as effective as last generation features from Convolutional Neural Networks.

关键词： local binary patterns Image classification Colour texture Convolutional Neural Networks

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