Completed Local Binary Pattern Research Articles

Breast Cancer Detection using Local Binary Patterns

Breast cancer is a leading cause of cancer type for death among women in most of popular countries, breast cancer detection is important and challenging role in worldwide to save women’s life. Due to inexperience to detect cancer, the doctors and radio logistic can miss the abnormality, which leads to death. Mammography is the most used method for breast cancer detection used by the radiologists. In this experiment, the MIAS (Mammogram Image Analysis Society) database is used and the MIAS database consists of normal and abnormal type of 322 mammograms. The pre-processing is most important step to capture quality mammogram image for next study and processing in mammogram analysis. Texture analysis plays important role to identify normal and abnormal types. Texture feature extraction can be done by local binary patterns (LBP) operator and by using LBP we can consider only sign parameters, it may loss the some texture information. The local binary pattern is a rotation invariant approach for the texture analysis. In this experiment famous completed LBP (CLBP) method used for extracting texture features. Completed LBP considering the sign, magnitude and centre gray level values. By using the joint or hybrid distributions combine CLBP_Sign, CLBP_Magnitude and CLBP_Center gray level values.LBP is one type of Completed LBP for texture analysis, advantage of CLBP is rotation invariant. Finally extracted texture features can be trained and classified by using the SVM classifier for identifying the normal and abnormal cancer type.

Weighted locality-constrained linear coding for lesion classification in CT images.

Computed tomography is a popular imaging modality for detecting abnormalities associated with abdominal organs such as the liver, kidney and uterus. In this paper, we propose a novel weighted locality-constrained linear coding (LLC) method followed by a weighted max-pooling method to classify liver lesions into three classes: cysts, metastases, hemangiomas. We first divide the lesions into same-size patches. Then, we extract the raw features in all patches followed by Principal Components Analysis (PCA) and apply K means to obtain a single LLC dictionary. Since the interior lesion patches and the boundary patches contribute different information in the image, we assign different weights on these two types of patches to obtain the LLC codes. Moreover, a weighted max pooling approach is also proposed to further evaluate the importance of these two types of patches in feature pooling. Experiments on 109 images of liver lesions were carried out to validate the proposed method. The proposed method achieves a best lesion classification accuracy of 96.33%, which appears to be superior compared with traditional image coding methods: LLC method and Bag-of-words method (BoW) and traditional features: Local Binary Pattern (LBP) features, uniform LBP and complete LBP, demonstrating that the proposed method provides better classification.

Land-use scene classification using multi-scale completed local binary patterns

In this paper, we introduce the completed local binary patterns (CLBP) operator for the first time on remote sensing land-use scene classification. To further improve the representation power of CLBP, we propose a multi-scale CLBP (MS-CLBP) descriptor to characterize the dominant texture features in multiple resolutions. Two different kinds of implementations of MS-CLBP equipped with the kernel-based extreme learning machine are investigated and compared in terms of classification accuracy and computational complexity. The proposed approach is extensively tested on the 21-class land-use dataset and the 19-class satellite scene dataset showing a consistent increase on performance when compared to the state of the arts.

Detection of abnormalities in ultrasound lung image using multi-level RVM classification

The classification of abnormalities in ultrasound images is the monitoring tool of fluid to air passage in the lung. In this study, the adaptive median filtering technique is employed for the preprocessing step. The preprocessed image is then extracted the features by the convoluted local tetra pattern, histogram of oriented gradient, Haralick feature extraction and the complete local binary pattern. The extracted features are selected by applying particle swarm optimization and differential evolution feature selection. In the final stage, classifiers namely relevance vector machine (RVM), and multi-level RVM are employed to perform classification of the lung diseases. The diseases respiratory distress syndrome (RDS), transient tachypnea of the new born, meconium aspiration syndrome, pneumothorax, bronchiolitis, pneumonia, and lung cancer are used for training and testing. The experimental analysis exhibits better accuracy, sensitivity, specificity, pixel count and fitness value than the other existing methods. The classification accuracy of above 90% is accomplished by multi-level RVM classifier. The system has been tested with a number of ultrasound lung images and has achieved satisfactory results in classifying the lung diseases.

A Hybrid PSO-DEFS Based Feature Selection for the Identification of Diabetic Retinopathy.

Diabetic Retinopathy (DR) is an eye disease, which may cause blindness by the upsurge of insulin in blood. The major cause of visual loss in diabetic patient is macular edema. To diagnose and follow up Diabetic Macular Edema (DME), a powerful Optical Coherence Tomography (OCT) technique is used for the clinical assessment. Many existing methods found out the DME affected patients by estimating the fovea thickness. These methods have the issues of lower accuracy and higher time complexity. In order to overwhelm the above limitations, a hybrid approaches based DR detection is introduced in the proposed work. At first, the input image is preprocessed using green channel extraction and median filter. Subsequently, the features are extracted by gradient-based features like Histogram of Oriented Gradient (HOG) with Complete Local Binary Pattern (CLBP). The texture features are concentrated with various rotations to calculate the edges. We present a hybrid feature selection that combines the Particle Swarm Optimization (PSO) and Differential Evolution Feature Selection (DEFS) for minimizing the time complexity. A binary Support Vector Machine (SVM) classifier categorizes the 13 normal and 75 abnormal images from 60 patients. Finally, the patients affected by DR are further classified by Multi-Layer Perceptron (MLP). The experimental results exhibit better performance of accuracy, sensitivity, and specificity than the existing methods.

Multiresolution local binary pattern variants based texture feature extraction techniques for efficient classification of microscopic images of hardwood species

In this paper, multiresolution local binary pattern (MRLBP) variants based texture feature extraction techniques have been proposed to categorize hardwood species into its various classes. Initially, discrete wavelet transform (DWT) has been used to decompose each image up to 7 levels using Daubechies wavelet (db2) as decomposition filter. Subsequently, six texture feature extraction techniques (local binary pattern and its variants) are employed to obtain substantial features of these images at different levels. Three classifiers, namely, linear discriminant analysis (LDA), linear and radial basis function (RBF) kernel support vector machine (SVM), have been used to classify the images of hardwood species. Thereafter, classification results obtained from conventional and MRLBP variants based texture feature extraction techniques with different classifiers have been compared. For 10-fold cross validation approach, texture features acquired using discrete wavelet transform based uniform completed local binary pattern (DWTCLBPu2) feature extraction technique has produced best classification accuracy of 97.40±1.06% with linear SVM classifier. This classification accuracy has been achieved at the 3rd level of image decomposition using full feature (1416) dataset. Further, reduction in dimension of texture features (325 features) by principal component analysis (PCA) has been done and the best classification accuracy of 97.87±0.82% for DWTCLBPu2 at the 3rd level of image decomposition has been obtained using LDA classifier. The DWTCLBPu2 texture features have also established superiority among the MRLBP techniques with reduced dimension features for randomly divided database into fix training and testing ratios.

Dynamic texture recognition based on completed volume local binary pattern

Dynamic textures are the sequences of images of moving scenes having some stationary properties in time; these include sea-waves, smoke, foliage, whirlwind etc. In recent years, dynamic texture description and recognition has attracted growing attention. This paper presents a more effective completed modeling of volume local binary pattern (VLBP) to recognize dynamic textures. Due to the dependency on local binary pattern (LBP), traditional VLBP also suffers with noise sensitivity and may give the same LBP code to different structural patterns; thus limiting the discriminating power of a texture descriptor. To represent temporal textures we have used VLBP. Local region of a volume is represented using its center frame and local sign magnitude difference with the circularly symmetric neighborhood. We have proposed a new contrast operator to complement the sign information of the temporal texture. To add additional discriminative information, volume center pixel information is also fused with the sign magnitude difference of texture. By combining these features into hybrid distributions we get higher classification accuracy for rotation invariant texture classification. Experimental results on UCLA and Dyntex databases show that the proposed approach provides better performance in comparison to the existing approaches.

Automatic detection of breast cancer mitotic cells based on the combination of textural, statistical and innovative mathematical features

Automatic grading systems based on histopathological slide images are applied to various types of cancers. To date, cancer scientists and researchers have conducted many experiments to find and evaluate new and innovative automatic cancer grading systems to accelerate their therapeutic diagnoses and ultimately to enable more efficient prognoses. The previously proposed automatic or computer-aided systems for breast cancer grading, including specializing mitosis counting, suffer from various shortcomings. The most important one is their low efficiency along with high complexity due to the huge amount of features. In this paper, three types of features with more flexibility and less complexity are employed. These features are: completed local binary pattern (CLBP) as textural features, statistical moment entropy (SME) and stiffness matrix (SM) as a mathematical model which includes geometric, morphometric and shape-based features. In the proposed automatic mitosis detection method, these three types of features are fused with each other. The SM feature comprises of characteristics which are to be extracted for reliable discrimination of mitosis objects from non-mitosis ones. The evaluations are applied over histology datasets A and H provided by the Mitos-ICPR2012 contest sponsors. Employing both a nonlinear radial basis function (RBF) kernel for support vector machine (SVM) and also random forest classifiers, leads to the best efficiencies among the other competitive methods which have been proposed in the past. The results are in the form of F-measure criterion which is a basis for bioinformatics assessments and evaluation.

Classification of Emphysema Subtypes: Comparative Assessment of Local Binary Patterns and Related Texture Features

The purpose of this study was to assess usefulness of local binary patterns (LBP) and related texture features, namely completed local binary patterns (CLBP) and local ternary patterns (LTP), for the classification of emphysema subtypes on low-dose CT images. Fifty patients (34 men and 16 women; age, 67.5 ± 10.1 years) who underwent low-dose CT (60 mAs) were included. They were comprised of 17 never smokers, 13 smokers without COPD, and 20 smokers with COPD. By consensus reading of low-dose CT images from these patients, two radiologists selected 3681 nonoverlapping regions of interest (ROIs) and annotated them as one of the following three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. From these ROIs, histogram of CT densities, LBP, CLBP, and LTP were calculated, and the 3 types of texture histograms were concatenated with the CT density histogram. These 3 types of histograms (referred to as combined LBP, combined CLBP, and combined LTP) were used to classify ROI using linear support vector machine. For each type of the combined histogram, the accuracy of classification was determined by patient-based 10-fold cross validation. The best accuracy of combined LBP, combined CLBP, and combined LTP were 81.36%, 82.99%, and 83.29%, respectively. Compared to the classification accuracies obtained with combined LBP, those with combined LTP or combined CLBP were consistently improved. In conclusion, the results of this study suggest that, on low-dose CT, LTP and CLBP were more useful for the classification of emphysema subtypes than LBP.

Automated image-based protein subcellular location prediction in human reproductive tissue based on ensemble learning global and local patterns

Human reproductive system is a unique organ system owing to which humans are capable of reproducing and bearing live offsprings. From a microscopic point of view, this system process requires protein appearing on the right subcellular location at the right time. In this paper, we developed a novel protocol for protein subcellular localisation prediction from human reproductive normal tissues. According to experimental results, three conclusions can be summarised. First, the completed local binary pattern is more discriminative for describing immunohistochemistry images. Second, the proposed ensemble classifier based on support vector machine learning models has a significant improvement. Third, through three different statistical voting approaches, two proteins for male and two proteins for female were identified as the biomarkers in reproductive tissue. These promising results indicate that the developed protocol can be applied not only for accurate large-scale image-based protein subcellular localisation annotations but also for biomarker identification of human reproductive tissue.

A new automatic mass detection method for breast cancer with false positive reduction

Mass localization is a crucial problem in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, a new automatic mass detection method for breast cancer in mammographic images is proposed. Firstly, suspicious regions are located with an adaptive region growing method, named multiple concentric layers (MCL) approach. Prior knowledge is utilized by tuning parameters with training data set during the MCL step. Then, the initial regions are further refined with narrow band based active contour (NBAC), which can improve the segmentation accuracy of masses. Texture features and geometry features are extracted from the regions of interest (ROI) containing the segmented suspicious regions and the boundaries of the segmentation. The texture features are computed from gray level co-occurrence matrix (GLCM) and completed local binary pattern (CLBP). Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist׳s diagnosis. To deal with the imbalance problem regarding the number of non-masses and masses, supersampling and downsampling are incorporated. The method was evaluated on a dataset with 429 craniocaudal (CC) view images, containing 504 masses. Among them, 219 images containing 260 masses are used to optimize the parameters during MCL step, and are used to train SVM. The remaining 210 images (with 244 masses) are used to test the performance. Masses are detected with 82.4% sensitivity with 5.3 false positives per image (FPsI) with MCL, and after active contour refinement, feature analysis and classification, it obtained 1.48 FPsI at the sensitivity 78.2%. Testing on 164 normal mammographic images showed 5.18 FPsI with MCL and 1.51 FPsI after classification. Experiments on mediolateral oblique (MLO) images have also been performed, the proposed method achieved a sensitivity 75.6% at 1.38 FPsI. The method is also analyzed with free response operating characteristic (FROC) and compared with previous methods. Overall, the proposed method is a promising approach to achieve low FPsI while maintaining a high sensitivity.

Classification of mammographic lesion based in Completed Local Binary Pattern and using multiresolution representation

This paper presents a comparison of two methods for features extraction of mammograms based in completed local binary pattern (CLBP) and wavelet transform. In first part, CLBP was applied in digitized mammograms. In second part, we applied CLBP in the sub-bands obtained from the wavelet multi-resolution representation of the mammographies. In this study, we evaluated the CLBP in the image in the spatial domain and in the sub-bands obtained with wavelet transform. Then, the statistical technique of variance analysis (ANOVA) was used to reduce the number of features. Finally, the classifier Support Vector Machine (SVM) was applied in the samples. The proposed methods were tested on 720 mammographies which 240 was diagnosed as normal samples, 240 as benign lesion and 240 as malign lesion. The images were obtained randomly of the Digital Database for Screening Mammography (DDSM). The system effectiveness was evaluated using the area under the ROC curve (AUC). The experiments demonstrate that the textural feature extraction of the multi-resolution representation was more relevant with value of AUC=1.0. In our experiments, CLBP in the spatial domain resulted in value of AUC=0.89. The proposed method demonstrated promising results in the classification of different classes of mammographic lesions.

Support Local Pattern and its Application to Disparity Improvement and Texture Classification

The local binary pattern (LBP) and its variants have been widely investigated in many image processing and computer vision applications due to their robust ability to capture local image structures and their computational simplicity. The existing LBPs extract local structure information by establishing a relationship between the central pixel and its adjacent pixels. However, most LBPs miss the relationship among all of the pixels in the local region. Therefore, this paper proposes a novel model to establish this relationship by introducing a support LBP. The proposed model improves the performance of the existing LBP methods and results in lower sensitivity to illumination changes and radiometric variations. Moreover, the proposed model has been successfully investigated in two applications: disparity map generation and texture classification. For disparity map generation, the proposed model reduces the root mean square (RMS) error by 23.6% (in Baby1 dataset, Middlebury), and 16.58% (in Aloe dataset, Middlebury) as compared with the standard LBP under radiometric variation conditions. Moreover, the proposed model reduces the RMS by 28.11% as compared with the standard LBP under the Gaussian noise condition in the ESATS dataset. For texture classification applications, the proposed model improves the classification results from 96.26% to 98.13% on the Outext database, from 88.03% to 91.41% on the Xu database, and from 94.00% to 96.67% on the KTH-TIPS database as compared with the completed LBP.

Diagnosis of Alzheimer's Disease from Magnetic Resonance Images with Complete Local Binary Pattern

Diagnosis of Alzheimer's Disease from Magnetic Resonance Images with Complete Local Binary Pattern

A Novel CAD System for Mitosis detection Using Histopathology Slide Images

Histopathology slides are one of the most applicable resources for pathology studies. As observation of these kinds of slides even by skillful pathologists is a tedious and time-consuming activity, computerizing this procedure aids the experts to have faster analysis with more case studies per day. In this paper, an automatic mitosis detection system (AMDS) for breast cancer histopathological slide images is proposed. In the proposed AMDS, the general phases of an automatic image based analyzer are considered and in each phase, some special innovations are employed. In the pre-processing step to segment the input digital histopathology images more precisely, 2D anisotropic diffusion filters are applied to them. In the training segmentation phase, the histopathological slide images are segmented based on RGB contents of their pixels using maximum likelihood estimation. Then, the mitosis and non-mitosis candidates are processed and hence that their completed local binary patterns are extracted object-wise. For the classification phase, two subsequently non-linear support vector machine classifiers are trained pixel-wise and object-wise, respectively. For the evaluation of the proposed AMDS, some object and region based measures are employed. Having computed the evaluation criteria, our proposed method performs more efficient according to f-measure metric (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) than the methods proposed by other participants at Mitos-ICPR2012 contest in breast cancer histopathological images. The experimental results show the higher performance of the proposed AMDS compared with other competitive systems proposed in Mitos-ICPR2012 contest.

Image Processing Method for Automatic Discrimination of Hoverfly Species

An approach to automatic hoverfly species discrimination based on detection and extraction of vein junctions in wing venation patterns of insects is presented in the paper. The dataset used in our experiments consists of high resolution microscopic wing images of several hoverfly species collected over a relatively long period of time at different geographic locations. Junctions are detected using the combination of the well known HOG (histograms of oriented gradients) and the robust version of recently proposed CLBP (complete local binary pattern). These features are used to train an SVM classifier to detect junctions in wing images. Once the junctions are identified they are used to extract statistics characterizing the constellations of these points. Such simple features can be used to automatically discriminate four selected hoverfly species with polynomial kernel SVM and achieve high classification accuracy.

Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification?

Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.

A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects

Automatic recognition method for hot-rolled steel strip surface defects is important to the steel surface inspection system. In order to improve the recognition rate, a new, simple, yet robust feature descriptor against noise named the adjacent evaluation completed local binary patterns (AECLBPs) is proposed for defect recognition. In the proposed approach, an adjacent evaluation window which is around the neighbor is constructed to modify the threshold scheme of the completed local binary pattern (CLBP). Experimental results demonstrate that the proposed approach presents the performance of defect recognition under the influence of the feature variations of the intra-class changes, the illumination and grayscale changes. Even in the toughest situation with additive Gaussian noise, the AECLBP can still achieve the moderate recognition accuracy. In addition, the strategy of using adjacent evaluation window can also be used in other methods of local binary pattern (LBP) variants.

Sparse representation method based on Gabor and CLBP

Sparse representation method (SRM) is a state-of-the-art face recognition method. Nevertheless, SRM exploits image samples rather than image features to perform classification. As we know, the proper feature of the image can be more discriminative than the image sample itself. For example, Gabor and local binary pattern (LBP), two kinds of widely used features, have shown excellent discriminative performance in face recognition. Recently a number of experiments have shown that complete local binary pattern (CLBP) obtains a much better result than LBP in recognizing the texture images. With this paper, we propose a novel sparse representation method based on Gabor and CLBP features for face recognition. Our method first extracts the most discriminative features and then uses SRM to perform face recognition. The proposed method is composed of the following steps: the first step is to perform the histogram equalization operation on the image samples. The second step extracts the Gabor and CLBP features from the image samples. The last step uses the sparse representation method based on the combination of Gabor and CLBP features to perform classification. The rationales of our method are as follows: the first step can reduce the adverse effects caused by the variable illuminations. Both of the Gabor and CLBP features not only are very discriminative but also are complementary. A large number of experiments show the superior performance of our method. For the Feret face database, the rate of classification error of our method is 28.8% lower than that of SRM and 14.8% lower than that of LRC. For the ORL face database, the rate of classification error of our method is 9% lower than that of SRM and 9.5% lower than that of LRC.

Systematic Approach for Detecting Text in Images Using Supervised Learning

Locating text data in images automatically has been a challenging task. In this approach, we build a three stage system for text detection purpose. This system utilizes tensor voting and Completed Local Binary Pattern (CLBP) to classify text and non-text regions. While tensor voting generates the text line information, which is very useful for localizing candidate text regions, the Nearest Neighbor classifier trained on discriminative features obtained by the CLBP-based operator is used to refine the results. The whole algorithm is implemented in MATLAB and applied to all images of ICDAR 2011 Robust Reading Competition data set. Experiments show the promising performance of this method.