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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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A comparative study on facial expression recognition using local binary patterns, convolutional neural network and frequency neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第16期82卷 24369-24385页

作者： Kumar, Sanjeev Sagar, Vikas Punetha, Deepak GL Bajaj Inst Technol & Management Dept Master Comp Applicat Greater Noida Uttar Pradesh India Noida Inst Engn & Technol Dept Artificial Intelligence Greater Noida Uttar Pradesh India Vellore Inst Technol VIT Univ Sch Elect Engn Chennai 600127 Tamilnadu India

As the principal processing method for nonverbal intentions, Facial Expression Recognition (FER) is an important and promising topic of computer vision and artificial intelligence, as well as one of the subject areas of symmetry. This research work provides a thorough and well-organized comprehensive comparative empirical study of facial expression recognition based on a deep learning study in frequency domain, convolution neural network, and local binary patterns features. We have attained the FER by incorporating neutral, joy, anger, fear, sadness, disgust, and surprise as seven universal emotional categories. In terms of methodology, we present a broad framework for a traditional FER approach and analyze the possible technologies that can be used in each component to emphasis the contrasts and similarities. Even though there has been a lot of research done with static images, there is still a lot of work being done to develop new ways that are easier to compute and use less memory than prior methods. This research could pave the way for a new approach to facial emotion identification in terms of accuracy and high-performance.

关键词： Facial expression recognition local binary patterns Convolutional neural network Frequency neural network Feature extraction Deep learning

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Texture Classification Using Features from Multi-level local binary patterns 32

Texture Classification Using Features from Multi-level Local...

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32nd European Signal Processing Conference (EUSIPCO)

作者： Backes, Andre Ricardo Univ Fed Sao Carlos Dept Comp Sao Carlos SP Brazil

ISBN: (纸本)9789464593617;9798331519773

This paper introduces a novel texture analysis method that combines simple histogram features with the local binary patterns (LBP) approach. By utilizing LBP to derive "pattern images" from a texture image, we obtain additional information sources to be explored using histogram analysis. We evaluated different combinations of "pattern images" as well as histogram features, yielding impressive results in three benchmarking databases. Our approach achieved accuracy rates of 99.54%, 99.25%, and 92.98% for the Vistex, Brodatz, and USPTex databases, respectively. These outcomes demonstrate the ability of the proposed hybrid method to generate a highly discriminative feature vector for effective texture classification.

关键词： local binary patterns Texture analysis Feature selection

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No-Reference Image Quality Assessment Using local binary patterns: A Comprehensive Performance Evaluation 3

No-Reference Image Quality Assessment Using Local Binary Pat...

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3rd Workshop on Quality of Experience in Visual Multimedia Applications (QoEVMA)

作者： Zhou, Zihan Xu, Yong Wan, Xi Quan, Yuhui Xu, Ruotao Li, Jing Le Callet, Patrick South China Agr Univ Guangzhou Peoples R China Inst Super Robot Guangzhou Peoples R China Alibaba Grp Moku Lab Beijing Peoples R China Nantes Univ Nantes France PaZhou Lab Guangzhou Guangzhou Peoples R China Guangdong Prov Key Lab Multimodal Big Data Intell Guangzhou Peoples R China

ISBN: (纸本)9798400712043

One key in image quality assessment (IQA) is the design of image representations that can capture the changes in image structures caused by distortions. In the last decades, local binary patterns (LBP) have been proven to be a powerful tool as a statistical model for texture and local structure representation. LBP and its variants, as the texture descriptor with low computational complexity, have been applied widely and successfully in several specific applications, such as texture classification, face recognition and IQA. Generally, visible impairments alter the statistics of LBP descriptors, making it possible to measure degradation and then estimate image quality. However, only a few variants of LBP are applied in no-reference (NR) IQA and a few characteristics of LBP descriptors are explored for quality prediction, while some characteristics useful in the rest variants are ignored for IQA. Two previous studies give a review of 16/4 LBP operators and compare the performance with them separately in IQA application only on synthetic distorted data. To extend this work, we provide a review of LBP methodologies to assist the scientific community and new researchers, as well as explore more LBP descriptors in NR-IQA methods under various distortion conditions, particularly real-world cases. Specifically, we comprehensively review 30 widely-used or effective LBP-based operators, including recent variations. We then utilize a common framework for applying LBP descriptors in NR-IQA. The practicality of the reviewed descriptors is demonstrated and analyzed using experimental results under synthetic and authentic cases, indicating suitable LBP descriptors and characteristics for NR-IQA. Codes implementing the reviewed LBP operators is available at https://***/csxwan/local-binary-patterns-for-IQA.

关键词： No-Reference Image Quality Assessment local binary patterns Variants Texture Descriptors

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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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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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Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第21-23期80卷 32763-32790页

作者： Kayhan, Nasim Fekri-Ershad, Shervan Shahid Ashrafi Esfahani Univ Fac Engn Dept Comp Engn Esfahan Iran Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran

Today, large amount of data are stored in image format. Content based image retrieval from bulk databases has become an interesting research topic in last decade. Most of the recent approaches use joint of texture and color information. In most cases, the color and texture features are concatenated together and equal importance is given to each one. The human visual system, usually pays more attention to the textural properties of objects to recognize. In this paper a new approach is proposed for content based image retrieval based on weighted combination of color and texture features. Firstly, to achieve discriminant features, texture features are extracted using modified local binary patterns (MLBP) and local neighborhood differences patterns (LNDP) and filtered gray level co-occurrence matrix (GLCM). Also, quantization color histogram is used to extract color features. Next, the similarity matching is performed based on canbera distance in color and texture features separatly. Finally, a weighted decision is performed to retrieve most similar database images to the user query. The performance of the proposed approach is evaluated on Corel 1 K and Corel 10k datasets. Results show that proposed approach provide better performance than state-of-the-art methods in terms of precision and recall rate.

关键词： Content based image retrieval Feature extraction Texture analysis local binary patterns local neighborhood difference patterns Weighted combination

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A novel method for digital image copy-move forgery detection and localization using evolving cellular automata and local binary patterns

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EVOLVING SYSTEMS 2021年第2期12卷 503-517页

作者： Gani, Gulnawaz Qadir, Fasel Univ Kashmir Dept Comp Sci North Campus Baramulla 193103 Jammu & Kashmir India

Copy-Move Forgery Detection (CMFD) methods aim to forensically analyze a digital image for a possible content duplication manipulation. In the past, many block-based algorithms have been proposed for detection and localization of CMF. However, the existing solutions show limited efficacy for images compressed in JPEG and lack robustness against post-processing attacks such as noise addition, blurring, etc. To address this problem, we propose a new block-based passive method for detection and localization of CMF in this paper. Passive methods, as opposed to active methods, are used to authenticate the image content in the absence of any pre-embedded information such as watermarks. In our proposed scheme, a suspicious input image to be analyzed is first low pass filtered and converted to local binary patterns (LBP) image. The LBP texture image is then divided into overlapping blocks. Next, a compact five-dimensional feature vector is extracted from each block by employing thresholding and Cellular Automata. The set of feature vectors is sorted lexicographically to bring the copy-pasted blocks nearer to each other. Finally, the feature matching step is used to reveal the duplicate blocks. Our experimental results indicate that the proposed method performs exceptionally well relative to other state-of-art-methods, under different image manipulation scenarios.

关键词： Copy-Move Forgery Cellular Automata Passive method Thresholding local binary patterns

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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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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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