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Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient

NEUROCOMPUTING 2017年 219卷 526-535页

作者： Abbasi, Solmaz Tajeripour, Farshad Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

Brain tumor pathology is one of the most common mortality issues considered as an essential priority for health care societies. Accurate diagnosis of the type of disorder is crucial to make a plan for remedy that can minimize the deadly results. The main purpose of segmentation and detection is to make distinction between different regions of the brain. Besides accuracy, these techniques should be implemented quickly. In this paper an automatic method for brain tumor detection in 3D images has been proposed. In the first step, the bias field correction and histogram matching are used for pre-processing of the images. In the next step, the region of interest is identified and separated from the background of the Flair image. local binary pattern in three orthogonal planes (LBP-TOP) and histogram of orientation gradients (HOG-TOP) are used as the learning features. Since 3D images are used in this research we use the idea of in local binary pattern in three orthogonal planes in order to extend histogram orientation gradients for 3D images. The random forest is then used to segment tumorous regions. We evaluate the performance of our algorithm on glioma images from BRATS 2013. Our experimental results and analyses indicate that our proposed framework is superior in detecting brain tumors in comparison with other techniques.

关键词： Medical image processing Tumor detection local binary patterns Histogram orientation gradient MRI images

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Color texture classification based on proposed impulse-noise resistant color local binary patterns and significant points selection algorithm

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SENSOR REVIEW 2017年第1期37卷 33-42页

作者： Fekriershad, Shervan Tajeripour, Farshad Shiraz Univ Dept Comp Sci & Engn Shiraz Iran

Purpose - The purpose of this paper is to propose a color-texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for this proposed approach. Design/methodology/approach - One of the efficient texture analysis operations is local binary patterns (LBP). The proposed approach includes two steps. First, a noise resistant version of color LBP is proposed to decrease its sensitivity to noise. This step is evaluated based on combination of color sensor information using AND operation. In a second step, a significant points selection algorithm is proposed to select significant LBPs. This phase decreases final computational complexity along with increasing accuracy rate. Findings - The proposed approach is evaluated using Vistex, Outex and KTH-TIPS-2a data sets. This approach has been compared with some state-of-the-art methods. It is experimentally demonstrated that the proposed approach achieves the highest accuracy. In two other experiments, results show low noise sensitivity and low computational complexity of the proposed approach in comparison with previous versions of LBP. Rotation invariant, multi-resolution and general usability are other advantages of our proposed approach. Originality/value - In the present paper, a new version of LBP is proposed originally, which is called hybrid color local binary patterns (HCLBP). HCLBP can be used in many image processing applications to extract color/texture features jointly. Also, a significant point selection algorithm is proposed for the first time to select key points of images.

关键词： Colour plane features Colour texture classification local binary patterns Noise resistant Significant point selection Visual sensor technologies

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Segmenting water and shadow regions within WorldView imagery using local binary patterns

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JOURNAL OF APPLIED REMOTE SENSING 2022年第3期16卷

作者： Tate, Christopher G. Cave, Justin Moyers, Richard L. Oak Ridge Natl Lab Geospatial Sci & Human Secur Div POB 2009 Oak Ridge TN 37831 USA Oak Ridge Natl Lab Electrificat & Energy Infrastruct Div Oak Ridge TN USA

Many shadow detection algorithms pertaining to remotely sensed imagery exist. Several of these algorithms exploit the spectral characteristics of shadows within imagery to identify shaded regions. However, these algorithms can have problems when water is also present within the imagery because water shares similar spectral characteristics with shadows. Many of these algorithms are applied to small image subsets instead of the image as a whole and frequently are applied to urban environments that require additional use of the normalized difference water index or other features to affect the removal of water from the shadow mask. This diversity of the image scene content coupled with the complexity and wide variety of environmental conditions that satellite imagery can acquire makes reliably separating shadow and water within larger images a complex problem. Thresholding various spectral indices to produce segmentation maps can be challenging when large, complex, and often imbalanced scenes are captured, which may require manual adjustment of algorithmic parameters for different image areas. We present a methodology that makes use of the near-infrared channel using a watershed segmentation algorithm, local binary pattern measurements, and a support vector machine to classify shadow and water within a full WorldView scene. Results are promising with initial accuracies above 97% for both shadow and water. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： remote sensing local binary patterns shadows water

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Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2016年第3期54卷 151-160页

作者： Wang, Yiding Duan, Qiangyu Shark, Lik-Kwan Huang, Di North China Univ Technol Sch Elect & Informat Engn Beijing 100144 Peoples R China Univ Cent Lancashire Sch Engn Appl Digital Signal & Image Proc Res Ctr Preston PR1 2HE Lancs England Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

Among biometric modalities, hand vein patterns are seen as providing an attractive method for high-level security access applications owing to high impenetrability and good user convenience. For biometric recognition based on near-infrared dorsal hand vein images, local binary patterns (LBP) have emerged as a highly effective descriptor of local image texture with high recognition performance reported. In this paper, the traditional approach with LBP applied in the spatial domain is extended to multi-radius LBP in the wavelet domain to provide a more comprehensive set of feature categories to capture grey-level variation characteristics of vein patterns, and score weighted fusion based on the relative discriminative power of each feature category is proposed to achieve higher recognition performance. The proposed methodology is shown to provide a more robust performance with a recognition rate in excess of 99% and an equal error rate significantly less than 2%.

关键词： biometrics local binary patterns score weighted fusion hand vein recognition wavelet transform

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Hyperspectral Image Classification Based on Segmented local binary patterns

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SENSING AND IMAGING 2020年第1期21卷 1-16页

作者： Ye, Zhen Dong, Rui Bai, Lin Jin, Chengxuan Nian, Yongjian Changan Univ Sch Elect & Control Engn Xian Peoples R China Xian Inst Modern Control Technol Xian Peoples R China Third Mil Med Univ Army Med Univ Coll Biomed Engn & Imaging Med Chongqing 400038 Peoples R China

Recently, local binary patterns (LBP) coupled with principal component analysis has been developed for feature extraction of hyperspectral imagery, which has shown success over traditional methods but is limited in physical meaning representation due to the noise bands existing in hyperspectral data. In order to preserve the intrinsic geometrical structure of original data, we propose a segmented LBP (SLBP) to group correlative bands and then extract spatial-spectral features from each band group. The proposed approach employs the LBP operator on independent subspaces to characterize local texture information and distinct spectral signatures, along with a decision fusion system further improving discriminant power. The proposed approach is compared with several traditional and state-of-the-art methods on two benchmark datasets (i.e., the Indian Pines dataset and the Salinas Valley dataset). Experimental results demonstrate that the proposed SLBP strategy can yield superior classification performance (96.8% for the Indian Pines dataset with an improvement of approximately 6.4% and 4.2% when compared with LBP and MELBP, respectively;98.1% for the Salinas Valley dataset with an improvement of approximately 3.5% and 1.3% compared with LBP and MELBP, respectively).

关键词： Hyperspectral classification Principal component analysis local binary patterns Decision fusion

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Recognition of Skin Lesions in Dermoscopic Images using local binary patterns and Multinomial Logistic Regression

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IEEE LATIN AMERICA TRANSACTIONS 2021年第7期20卷 2020-2028页

作者： Quero-Caiza, Washington D. Altuve, Miguel Valencian Int Univ Valencia Spain Univ Simon Bolivar Appl Biophys & Bioengn Grp Caracas Venezuela

Skin cancer, such as melanoma, affects more than 13 million people annually and causes more than 65,000 deaths. As with almost all types of cancer, it can be treated effectively if it's diagnosed in it's early stages. The development of computer-aided diagnostic tools is, therefore, a valuable and urgent need to suport the decision of dermatologists, minimize human error, and accelerate the time to diagnosis and the prognosis of patients. The present work proposes the automatic recognition of three skin lesions, namely Common Nevus, Atypical Nevus, and Melanoma, from dermoscopic images. First, a feature vector representing texture is extracted from the dermoscopic images using the local binary pattern operator (LBP). Then, a multinomial logistic regression (MRL) model is used to exploit the feature vector to perform the multiclass classification. The hyperparameters of the LBP operator, namely the radius and the pixel neighborhoods, were modified to provide the feature set to the MRL model that maximizes the classification performance. Moreover, to assure the generalizability of the model and the reproducibility of the results, a 10-iteration Monte Carlo cross-validation (MCCV) was employed, by dividing the dataset into 70% for training and 30% for testing, with different random seeds in each iteration. The optimal LBP-MRL classification model was finally embeddeb in a graphical user interface (GUI) to facilitate the interaction between the user and the classification system. An average accuracy of 90%, averge recall of 89.23%, 84.75% and 100%, and average precision of 89.95%, 88.12% and 100% were obtained, to identify Common Nevus, Atypical Nevus and Melanoma, respectively, in the MCCV strategy. It should be highlighted that the proposed classification system can perfectly differentiate melanoma from the other two skin lesions.

关键词： Cancer Melanoma Lesions Skin Image segmentation Visualization Medical diagnostic imaging Melanoma Cancer local binary patterns Logistic Regression Classification Machine Learning digital image processing Computer Vision

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Copy-move forgery detection using multiresolution local binary patterns

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FORENSIC SCIENCE INTERNATIONAL 2013年第1-3期231卷 61-72页

作者： Davarzani, Reza Yaghmaie, Khashayar Mozaffari, Saeed Tapak, Meysam Semnan Univ Dept Elect & Comp Engn Semnan Iran

Copy-move forgery is one of the most popular tampering artifacts in digital images. In this paper, we present an efficient method for copy-move forgery detection using Multiresolution local binary patterns (MLBP). The proposed method is robust to geometric distortions and illumination variations of duplicated regions. Furthermore, the proposed block-based method recovers parameters of the geometric transformations. First, the image is divided into overlapping blocks and feature vectors for each block are extracted using LBP operators. The feature vectors are sorted based on lexicographical order. Duplicated image blocks are determined in the block matching step using k-d tree for more time reduction. Finally, in order to both determine the parameters of geometric transformations and remove the possible false matches, RANSAC (RANdom SAmple Consensus) algorithm is used. Experimental results show that the proposed approach is able to precisely detect duplicated regions even after distortions such as rotation, scaling, JPEG compression, blurring and noise adding. (c) 2013 Elsevier Ireland Ltd. All rights reserved.

关键词： Digital image forensics Copy-move forgery local binary patterns Duplicated region detection

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Facial expression recognition based on local binary patterns: A comprehensive study

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IMAGE AND VISION COMPUTING 2009年第6期27卷 803-816页

作者： Shan, Caifeng Gong, Shaogang McOwan, Peter W. Philips Res Labs NL-5656 AE Eindhoven Netherlands Univ London Dept Comp Sci London E1 4NS England

Automatic facial expression analysis is an interesting and challenging problem, and impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. In this paper, we empirically evaluate facial representation based on statistical local features, local binary patterns, for person-independent facial expression recognition. Different machine learning methods are systematically examined on several databases. Extensive experiments illustrate that LBP features are effective and efficient for facial expression recognition. We further formulate Boosted-LBP to extract the most discriminant LBP features, and the best recognition performance is obtained by using Support Vector Machine classifiers with Boosted-LBP features. Moreover, we investigate LBP features for low-resolution facial expression recognition, which is a critical problem but seldom addressed in the existing work. We observe in our experiments that LBP features perform stably and robustly over a useful range of low resolutions of face images, and yield promising performance in Compressed low-resolution video sequences captured in real-world environments. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Facial expression recognition local binary patterns Support vector machine Adaboost Linear discriminant analysis Linear programming

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General rotation-invariant local binary patterns operator with application to blood vessel detection in retinal images

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PATTERN ANALYSIS AND APPLICATIONS 2014年第1期17卷 69-81页

作者： Fathi, Abdolhossein Naghsh-Nilchi, Ahmad Reza Univ Isfahan Dept Comp Engn Esfahan Iran

The local binary patterns (LBP) operator is a powerful multi-resolution micro-texture descriptor, which can be applied to many image-processing applications. However, existing LBP operators cannot use the information of non-uniform patterns efficiently. This paper presents a general extension of LBP operator to extract all uniform and non-uniform pattern types by using suitable rotation-invariant labeling scheme. Since the proposed LBP operator can extract all micro-texture structures, we combined it with artificial neural networks (ANN) to present a new supervised technique for automatic blood vessel enhancement and detection. The thin and thick blood vessels are detected by applying proper top-hat transform and length filtering on the enhanced blood vessels. The performance of the proposed method is evaluated on manually labeled images of the publicly available DRIVE and STARE databases and compared with several state-of-the-art approaches. The obtained results show the high accuracy of the proposed method on detecting thin and thick blood vessels.

关键词： local binary patterns Texture descriptor Retinal image segmentation Blood vessel detection

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Impulse-Noise Resistant Color-Texture Classification Approach Using Hybrid Color local binary patterns and Kullback-Leibler Divergence

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COMPUTER JOURNAL 2017年第11期60卷 1633-1648页

作者： Fekri-Ershad, Shervan Tajeripour, Farshad Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Shiraz Univ Dept Comp Sci Engn & IT Shiraz Iran

Texture classification is an active topic in image processing that can play an important role in many applications such as image retrieval, inspection systems, face recognition and medical image processing. There are many approaches extracting texture features in gray-level images such as local binary patterns (LBP). LBP is a nonparametric operator, which describes the local spatial structure and the local contrast of an image. One of the challenges is how to combine LBP features with color representations. Impulse noise sensitivity is another big limitation of the LBP. The use of color-texture features jointly is an efficient approach for texture classification in colored images. In this paper, an approach is proposed that consists of two phases. First, an impulse-noise resistant version of LBP is proposed, which extracts jointly color-texture features. Second, classification is evaluated based on Kullback-Leibler divergence ratio to achieve highest accuracy. The proposed approach is evaluated using Vistex, Outex, KTH-TIPS-2a data sets. Our approach has been compared with some state-of-the-art methods. It is experimentally demonstrated that the proposed approach achieves the highest accuracy. Computational complexity is computed based on the number of required operations. Low computational complexity, rotation invariant, low impulse-noise sensitivity and high usability are advantages of the proposed approach.

关键词： color-texture classification local binary patterns Kullback-Leibler divergence ratio noise resistant color plane features

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