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A Novel local binary patterns-Based Approach and Proposed CNN Model to Diagnose Breast Cancer by Analyzing Histopathology Images

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IEEE ACCESS 2025年 13卷 39610-39620页

作者： Gul, Mehmet Sirnak Univ Fac Engn Dept Comp Engn TR-73000 Sirnak Turkiye

Breast cancer, which is among the most common types of cancer in women, is a serious disease that requires attention due to its high risk of death. In this respect, every effort made to help early diagnosis is remarkable. This article proposed two methods, one CNN-based and the other local binary pattern (LBP)-based, to perform the preliminary diagnosis process on breast cancer histopathology images with high performance. Within the scope of the study, the high performance of the two proposed methods was applied to two separate comprehensive datasets most preferred in breast cancer studies. The proposed CNN model has 20 layers and is called quad star LBP (QS-LBP) due to its star-like structure based on the proposed LBP. The histopathology images improved with the QS-LBP method were then analyzed with the most commonly used Random Forest and Optimized Forest algorithms among machine learning algorithms. The BreaKHis dataset contains images with 40X, 100X, 200X, and 400X magnification resolutions and contains approximately 7924 images. The other comprehensive dataset containing analyzed histopathology images contains approximately 278 thousand images. Both datasets examined are extremely important in terms of the significant number of breast cancer histopathology images they contain. The results obtained with the QS-LBP method developed within the scope of the study are 94.58% accuracy, 92.3% F1 score, and 97.9% AUC/ROC, respectively. The results obtained with the proposed CNN model are 98.27% accuracy, 98% F1 score, and 97% AUC/ROC, respectively. The QS-LBP method and CNN model developed within the scope of the study outperform many existing methodologies in classifying breast cancer histopathology images as benign and malignant. In addition to all these, the accuracy of both methods proposed within the scope of the article can be compared with some state-of-the-art methods.

关键词： Histopathology Cancer Accuracy Breast cancer Convolutional neural networks Feature extraction Biopsy Image enhancement Ducts Deep learning histopathology images local binary patterns convolutional neural networks BreaKHis

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local binary patterns for multi-view facial expression recognition

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COMPUTER VISION AND IMAGE UNDERSTANDING 2011年第4期115卷 541-558页

作者： Moore, S. Bowden, R. Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7JW Surrey England

Research into facial expression recognition has predominantly been applied to face images at frontal view only. Some attempts have been made to produce pose invariant facial expression classifiers. However, most of these attempts have only considered yaw variations of up to 45 degrees, where all of the face is visible. Little work has been carried out to investigate the intrinsic potential of different poses for facial expression recognition. This is largely due to the databases available, which typically capture frontal view face images only. Recent databases, BU3DFE and multi-pie, allows empirical investigation of facial expression recognition for different viewing angles. A sequential 2 stage approach is taken for pose classification and view dependent facial expression classification to investigate the effects of yaw variations from frontal to profile views. local binary patterns (LBPs) and variations of LBPs as texture descriptors are investigated. Such features allow investigation of the influence of orientation and multi-resolution analysis for multi-view facial expression recognition. The influence of pose on different facial expressions is investigated. Others factors are investigated including resolution and construction of global and local feature vectors. An appearance based approach is adopted by dividing images into sub-blocks coarsely aligned over the face. Feature vectors contain concatenated feature histograms built from each sub-block. Multi-class support vector machines are adopted to learn pose and pose dependent facial expression classifiers. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Facial expression recognition Multi-view facial expression recognition Head pose estimation local binary patterns local gabor binary patterns

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local binary patterns based on α-cutting approach

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JOURNAL OF ELECTRONIC IMAGING 2020年第4期29卷 p43021-43021页

作者： Delic, Marija Univ Novi Sad Fac Tech Sci Novi Sad Serbia

local binary patterns (LBP) are well documented in the literature as descriptors of local image texture, and their histograms have been shown to be well-performing texture features. A method for texture description that is based on the alpha-cutting approach is presented. The presented approach combines basic definitions from the fuzzy set theory with the main concept of LBP descriptors, which resulted in powerful texture features. The general method is introduced and defined and its binary, ternary, and quinary versions evaluated in tests produced excellent results in texture classification. The performance of our method is presented by an extensive evaluation on four datasets-KTH-TIPS2b, UIUC, Virus, and Brodatz. The introduced descriptors are compared with some of the classical approaches-LBP, improved LBP, local ternary pattern, including one very promising LBP variant-median robust extended LBP (MRELBP), as well as with three non-LBP methods, based on deep convolutional neural networks approaches-ScatNet, FV-AlexNet, and fisher vector based very deep VGG. Our method effectively deals with many classification challenges and exceeds most of the other approaches. It outperforms the classical approaches on all datasets, even in its simplest binary version. It outperforms the MRELBP descriptor on the UIUC, KTH-TIPS2b, and Brodatz datasets and reaches abetter classification performance than two out of the three deep learning approaches on the KTH-TIPS2b dataset. (C) 2020 SPIE and IS&T

关键词： texture descriptors local binary patterns fuzzy set alpha-cut classification

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local binary patterns Preprocessing for Face Identification/Verification Using the VanderLugt Correlator

Local Binary Patterns Preprocessing for Face Identification/...

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Conference on Optical Pattern Recognition XXV

作者： Napoleon, Thibault AlFalou, Ayman L bISEN ISEN Brest Dept Vis F-29228 Brest 2 France

ISBN: (纸本)9781628410310

The face recognition tasks can be divided into two categories: verification (i.e. compare two images in order to know if they represent the same person) and identification (i.e. find the identity of a person into the database). Several powerful face recognition methods exist, in literature, for controlled environments: constrained illumination, frontal pose, neutral expression... However, there are few reliable methods for the uncontrolled case. Optical correlation has shown its interest through relevant architectures for controlled and uncontrolled environments. Based on this architecture, we propose a novel method for verification and identification tasks under illumination variation conditions. More specifically, we optimize the performances of a correlation method against illumination changes by using and adapting the local binary patterns (LBP) description. This later is widely used in the literature to describe the texture of an image using 8 bits words. For both, target image and reference image, we begin by using a specific-Gaussian function as first step of LBP-VLC correlator. This function filters the considered image with a band-pass filter in order to extract the edges. Then we applied the adapted LBP-VLC method. To validate our new approach, we used a simple POF filter (others correlation filters can be used). The simulations are done using the YaleB and YaleB Extended databases that contain respectively 10 and 38 identities with 64 illuminations. The results obtained reach more than 94% and 92% for the verification and 93% and 90% for the identification case. These results show the good performances of our approach of LBP-correlation methods against illumination changes.

关键词： Face Recognition Illumination Invariance Vander Lugt Correlator local binary patterns

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local binary patterns for Graph Characterization 24

Local Binary Patterns for Graph Characterization

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24th International Conference on Pattern Recognition (ICPR)

作者： Jawad, Muhammad Aziz, Furqan Hancock, Edwin Inst Management Sci Peshawar Pakistan Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

ISBN: (纸本)9781538637883

In this paper we propose a novel approach for defining local binary patterns (LBP) to directly encode graph structure. LBP is a simple and widely used technique for texture analysis in static 2D images, and there is no work in the literature describing its generalisation to graphs. The proposed method (GraphLBP) is efficient and yet effective as a noise-tolerant graph-based representation. We compute the new feature representation for graphs by combining LBP with Galois Fields, using irreducible polynomials. The proposed method is scalable as it preserves the local and global properties of the graph. Experimental results show that GraphLBP can both increase the recognition accuracy and is both simpler and more computationally efficient when compared with state of the art techniques.

关键词： Graph Characterization local binary patterns Galois Fields

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Perceptually relevant grouping of image tokens on the basis of constraint propagation from local binary patterns

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JOURNAL OF ELECTRONIC IMAGING 2016年第6期25卷 1-9页

作者： Behlim, Sadaf Iqbal Syed, Tahir Qasim Malik, Muhammad Yameen Vigneron, Vincent Visual Analyt Lab Karachi Pakistan Natl Univ Comp & Emerging Sci Dept Comp Sci Sect 17D St 4Natl Highway Karachi 75030 Pakistan Univ Evry Val dEssonne 40 Rue Pelvoux F-91020 Evry France

Grouping image tokens is an intermediate step needed to arrive at meaningful image representation and summarization. Usually, perceptual cues, for instance, gestalt properties inform token grouping. However, they do not take into account structural continuities that could be derived from other tokens belonging to similar structures irrespective of their location. We propose an image representation that encodes structural constraints emerging from local binary patterns (LBP), which provides a long-distance measure of similarity but in a structurally connected way. Our representation provides a grouping of pixels or larger image tokens that is free of numeric similarity measures and could therefore be extended to nonmetric spaces. The representation lends itself nicely to ubiquitous image processing applications such as connected component labeling and segmentation. We test our proposed representation on the perceptual grouping or segmentation task on the popular Berkeley segmentation dataset (BSD500) that with respect to human segmented images achieves an average F-measure of 0.559. Our algorithm achieves a high average recall of 0.787 and is therefore well-suited to other applications such as object retrieval and category-independent object recognition. The proposed merging heuristic based on levels of singular tree component has shown promising results on the BSD500 dataset and currently ranks 12th among all benchmarked algorithms, but contrary to the others, it requires no data-driven training or specialized preprocessing. (C) 2016 SPIE and IS& T

关键词： image segmentation structural constraints propagation local binary patterns image representation learning

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Multi-Temporal Depth Motion Maps-Based local binary patterns for 3-D Human Action Recognition

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IEEE ACCESS 2017年 5卷 22590-22604页

作者： Chen, Chen Liu, Mengyuan Liu, Hong Zhang, Baochang Han, Jungong Kehtarnavaz, Nasser Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Peking Univ Shenzhen Grad Sch Beijing 100871 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beijing 100080 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England Univ Texas Dallas Dept Elect Engn Richardson TX 75080 USA

This paper presents a local spatio-temporal descriptor for action recognistion from depth video sequences, which is capable of distinguishing similar actions as well as coping with different speeds of actions. This descriptor is based on three processing stages. In the first stage, the shape and motion cues are captured from a weighted depth sequence by temporally overlapped depth segments, leading to three improved depth motion maps (DMMs) compared with the previously introduced DMMs. In the second stage, the improved DMMs are partitioned into dense patches, from which the local binary patterns histogram features are extracted to characterize local rotation invariant texture information. In the final stage, a Fisher kernel is used for generating a compact feature representation, which is then combined with a kernel-based extreme learning machine classifier. The developed solution is applied to five public domain data sets and is extensively evaluated. The results obtained demonstrate the effectiveness of this solution as compared with the existing approaches.

关键词： Action recognition depth motion maps ELM classifier local binary patterns fisher kernel

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Automatic identification of butterfly species based on local binary patterns and artificial neural network

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APPLIED SOFT COMPUTING 2015年 28卷 132-137页

作者： Kaya, Yilmaz Kayci, Lokman Uyar, Murat Siirt Univ Dept Comp Engn TR-56100 Siirt Turkey Siirt Univ Dept Biol TR-56100 Siirt Turkey Siirt Univ Dept Elect & Elect Engn TR-56100 Siirt Turkey

Butterflies are classified firstly according to their outer morphological qualities. It is required to analyze genital characters of them when classification according to outer morphological qualities is not possible. Genital characteristics of a butterfly can be determined by using various chemical substances and methods. Currently, these processes are carried out manually by preparing genital slides of the collected butterfly through some certain processes. For some groups of butterflies molecular techniques should be applied for identification which is expensive to use. In this study, a computer vision method is proposed for automatically identifying butterfly species as an alternative to conventional identification methods. The method is based on local binary pattern (LBP) and artificial neural network (ANN). A total of 50 butterfly images of five species were used for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has achieved well recognition in terms of accuracy rates for butterfly species identification. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Butterfly identification local binary patterns Texture features Artificial neural network

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X-ray Image Classification Using Random Forests with local Wavelet-Based CS-local binary patterns

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JOURNAL OF DIGITAL IMAGING 2011年第6期24卷 1141-1151页

作者： Ko, Byoung Chul Kim, Seong Hoon Nam, Jae-Yeal Keimyung Univ Dept Comp Engn Taegu 704701 South Korea

This paper presents a fast and efficient method for classifying X-ray images using random forests with proposed local wavelet-based local binary pattern (LBP) to improve image classification performance and reduce training and testing time. Most studies on local binary patterns and its modifications, including centre symmetric LBP (CS-LBP), focus on using image pixels as descriptors. To classify X-ray images, we first extract local wavelet-based CS-LBP (WCS-LBP) descriptors from local parts of the images to describe the wavelet-based texture characteristic. Then we apply the extracted feature vector to decision trees to construct random forests, which are an ensemble of random decision trees. Using the random forests with local WCS-LBP, we classified one test image into the category having the maximum posterior probability. Compared with other feature descriptors and classifiers, the proposed method shows both improved performance and faster processing time.

关键词： X-ray image classification Random forests local binary patterns Image analysis Pattern recognition Automated Decision trees Diagnostic imaging Digital image management

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Two-stage detection of north Atlantic right whale upcalls using local binary patterns and machine learning algorithms

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APPLIED ACOUSTICS 2017年 120卷 158-166页

作者： Esfahanian, Mandi Erdol, Nurgun Gerstein, Edmund Zhuang, Hanqi Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA

In this paper, we investigate the effectiveness of two-stage classification strategies in detecting north Atlantic right whale upcalls. Time-frequency measurements of data from passive acoustic monitoring devices are evaluated as images. Vocalization spectrograms are preprocessed for noise reduction and tone removal. First stage of the algorithm eliminates non-upcalls by an energy detection algorithm. In the second stage, two sets of features are extracted from the remaining signals using *** texture based methods. The former is based on-extraction of time-frequency features from upcall contours, and the latter employs a local binary Pattern operator to extract distinguishing texture features of the upcalls. Subsequently evaluation phase is carried out by using several classifiers to assess the effectiveness of both the contour-based and texture-based features for upcall detection. Comparing ROC curves of machine learning algorithms obtained from Cornell University's dataset reveals that LBP features improved performance accuracy up to 43% over time-frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve highest upcall detection rates with LBP features.(C) 2017 Elsevier Ltd. All rights reserved.

关键词： North Atlantic right whale Acoustic monitoring Upcall detection local binary patterns Classification

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