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6th Latin American Biomedical Engineering Conference (CLAIB)

作者： Serpa-Andrade, L. Robles-Bykbaev, V. Gonzalez-Delgado, L. Guevara-Segarra, G. Univ Politecn Salesiana Ctr Invest Desarrollo & Innovac Ingn GI IATa Cuenca Ecuador Univ Vigo Telemat Engn Dept AtlantTIC Res Ctr Informat & Commun Technol Vigo Spain

ISBN: (纸本)9783319131160;9783319131177

According to the World Health Organization (WHO) in developing countries around 23% of malignancies are caused by infectious agents. The infection with Helicobacter pylori (H. pylori) bacteria seems to be a major cause of stomach cancer (80%). An early diagnosis of gastritis may help decrease the risk of gastric cancer and other complications such as gastric ulcers. The aim of this paper is to evaluate the probability of providing specialists a diagnostic support tool based on computer vision. We used twenty-four endoscopic images of healthy patients and thirty-five images of patients suffering from gastritis to perform an automatic classification process. The suggested approach uses local binary patterns (LBP), texture descriptors, and certain classifiers to perform the automatic classification. The results are promising and show 83% precision in identifying the disease.

关键词： Gastritis local binary patterns presumptive diagnosis FFT

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Texture analysis of lace images using histogram and local binary patterns under rotation variation 1

Texture analysis of lace images using histogram and local bi...

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Conference on First International Image Processing, Applications and Systems

作者： Ben Soltana, Wael Porebski, Alice Vandenbroucke, Nicolas Ahmad, Adeel Hamad, Denis Lab Informat Signal & Image Cote Opale 50 Rue Ferdinand BuissonBP 719 F-62228 Calais France

ISBN: (纸本)9781479970698

The images of lace textile are particularly difficult to be analyzed in digital form using classical image processing techniques. The major reasons of this difficulty emerge from the complex nature of lace which generally has different textures in its constituents like the background and patterns. In this paper, we study the behavior of Image Histogram (HistI) and local binary patterns (LBP) on image extracts of lace in presence and absence of rotation. We further evaluate two variants of LBP;primarily the LBP Histogram (LBPB) and secondly the Fourier Transform applied on the LBP Histogram (LBPFFT). Consequently, we analyze the contribution of data fusion on feature level and score level in the different experimentations. The classification rate evaluates the discrimination degree of each descriptor via the k nearest neighbors kNN classifier. Experimental results indicate that the LBPB, LBPFFT and HistI combined at score level generate the better performance in absence of transformations. Whereas, LBPFFT and HistI combined at the same level generate the better classification rate, in the presence of rotation.

关键词： Lace textile texture analysis local binary patterns Fourier Transform feature level fusion score level fusion kNN classifier

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Gradient local binary patterns for Age Group Estimation

Gradient Local Binary Patterns for Age Group Estimation

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18th IEEE International Symposium on Consumer Electronics (ISCE)

作者： Jeon, Jae-Ho Lee, Jae-Young Bae, Ji-Sang Kim, Jong-Ok Korea Univ Dept Elect Engn Seoul South Korea

ISBN: (纸本)9781479945917

In this paper, we propose a new texture descriptor GLBP (Gradient LBP) for age group estimation. LBP exploits the only signs of the gradients between a center pixel and its surrounding pixels in a local patch and it does not reflect their magnitudes. This fails LBP to describe local signal structure in detail. Motivated by this observation, we propose so called GLBP, which considers the magnitude as well as the sign of the gradient. Experimental results show that when the proposed method is applied to age group estimation, it can achieve higher classification rate than existing well-known texture descriptors.

关键词： Age group estimation local binary patterns local texture descriptor Feature extraction

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Knee cartilage segmentation using active shape models and local binary patterns

Knee cartilage segmentation using active shape models and lo...

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Optics, Photonics, and Digital Technologies for Multimedia Applications III

作者： Gonzalez, German Escalante-Ramirez, Boris Univ Nacl Autonoma Mexico Posgrado Ingn Elect Mexico City 04510 DF Mexico Univ Nacl Autonoma Mexico Fac Ingn Dept Proccesamiento Senales Mexico City 04510 DF Mexico

ISBN: (纸本)9781628410860

Segmentation of knee cartilage has been useful for opportune diagnosis and treatment of osteoarthritis (OA). This paper presents a semiautomatic segmentation technique based on Active Shape Models (ASM) combined with local binary patterns (LBP) and its approaches to describe the surrounding texture of femoral cartilage. The proposed technique is tested on a 16-image database of different patients and it is validated through Leave-One-Out method. We compare different segmentation techniques: ASM-LBP, ASM-medianLBP, and ASM proposed by Cootes. The ASM-LBP approaches are tested with different ratios to decide which of them describes the cartilage texture better. The results show that ASM-medianLBP has better performance than ASM-LBP and ASM. Furthermore, we add a routine which improves the robustness versus two principal problems: over-segmentation and initialization.

关键词： Knee Cartilage Segmentation active shape models local binary patterns

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Neighborhood Estimated local binary patterns for Texture Classification

Neighborhood Estimated Local Binary Patterns for Texture Cla...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Song, Kechen Yan, Yunhui Northeastern Univ Sch Mech Engn & Automat Shenyang Peoples R China

ISBN: (纸本)9783038350125

A novel texture classification approach based on neighborhood estimated local binary patterns (NELBP) is proposed. In the proposed approach, the local surrounding values of neighborhood estimated are introduced to operate binary patterns. Moreover, two different and complementary descriptors (average-based descriptor and differences-based descriptor) are extracted from local patches. Contrast experiments on Outex database and CUReT database demonstrate that the proposed NELBP is more robust to Gaussian noise than the conventional LBP for texture classification. In addition, the results also show that the combined complementary descriptor playes an important role in texture classification.

关键词： local binary patterns neighborhood estimated texture classification

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Face Description with local binary patterns and local Ternary patterns: Improving Face Recognition Performance Using Similarity Feature-Based Selection and Classification Algorithm

Face Description with Local Binary Patterns and Local Ternar...

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International Symposium on Computer, Consumer and Control (IS3C)

作者： Tran, Chi-Kien Lee, Tsair-Fwu Chang, Liyun Chao, Pei-Ju Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 80778 Taiwan I Shou Univ Dept Med Imaging & Radiol Sci Kaohsiung Taiwan Kaohsiung Chang Gung Mem Hosp Dept Radiat Oncol Kaohsiung Taiwan

ISBN: (纸本)9781479952779

In recent years, the binary coding of face image features, such as local binary patterns (LBP) and local ternary patterns (LTP) have become popular in face recognition systems. These local feature descriptors provide a simple and powerful means for texture description. In this paper, we present a novel approach, which uses these descriptors to represent face images, and a similarity feature-based selection and classification algorithm to improve recognition rate. The face image is first divided into small regions from which LBP and LTP histograms are extracted and concatenated into a single feature vector. The proposed algorithm is used to select the similarity features of training set and classify the face image. The experiments are conducted on the ORL Database of Faces and the Extended Yale Face Database B. The results clearly show the superiority of the proposed algorithm.

关键词： Face recognition similarity feature local binary patterns local ternary patterns local feature

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Improved texture retrieval by combining different variants of local binary patterns 8

Improved texture retrieval by combining different variants o...

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International Conference on Electrical and Computer Engineering

作者： Mohammed, Nabeel Rana, Sohel Univ Asia Pacific Dept CSE Dhaka Bangladesh

ISBN: (纸本)9781479941667

This paper proposes the combination of different variants of local binary patterns for texture retrieval. Recent studies have shown that LBP features extracted at multiple resolutions give the best performance for a standard texture retrieval collection. Techniques have been proposed to create a multi-dimensional histogram of these features. In this study we use a simpler approach. We hypothesize that the different variants of LBP may actually extract slightly different but useful image information. If this were the case, then using them in combination will result in further improved performance, compared to using each one independently. We demonstrate this by using LBP, LBp(ri), and LBp(u2) features in combination. We take ideas from the GNU Image Finding Tool (GIFT), and use separate normalisation to ensure the different feature vector lengths do not bias the system towards one of the features. We performed experiments on the two standard texture collections. Our results demonstrate that combining the different LBP features do indeed result in improved performance. In fact, for one of the collections (Outex_TR_00000) collections, using our method give better performance than the current state-of-the-art.

关键词： Image retrieval local binary patterns Separate normalisation

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local polynomial contrast binary patterns for face recognition

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NEUROCOMPUTING 2019年 355卷 1-12页

作者： Xu, Zhen Jiang, Yinyan Wang, Yichuan Zhou, Yicong Li, Weifeng Liao, Qingmin Tsinghua Univ Grad Sch Shenzhen Dept Elect Engn Shenzhen Peoples R China Shenzhen Key Lab Informat Sci & Technol Shenzhen Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China

We propose a novel face representation model, called the polynomial contrast binary patterns (PCBP), based on the polynomial filters, for robust face recognition. It is assumed that the discrete array of pixel values comes about by sampling an underlying smooth surface in an image. The proposed method efficiently estimates the underlying local surface information, which is approximately represented as linear projection coefficients of the pixels in a local patch. The decomposition using polynomial filters can capture rich image information at multiple orientations and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each filter response image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image for dimension reduction of features. Our extensive experiments on the public FERET and LFW databases demonstrate that the non-weighted Polynomial contrast binary patterns performs better than most of methods and the weighting scheme further improves the recognition rates. WPCBP+FLD(CD) and WPCBP+FLD(HI) can achieve much competitive or even better recognition performance compared with the state-of-the-art face recognition methods. (C) 2018 Published by Elsevier B.V.

关键词： Face recognition Polynomial filters local binary patterns Surface fitting

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A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2024年第4期36卷 563-583页

作者： Yasar, Huseyin Ceylan, Murat Cebeci, Hakan Kilincer, Abidin Kanat, Fikret Koplay, Mustafa Minist Hlth Republ Turkey Gen Directorate Hlth Investments Ankara Turkiye Konya Tech Univ Fac Engn & Nat Sci Dept Elect & Elect Engn Konya Turkiye Selcuk Univ Dept Radiol Fac Med Konya Turkiye Selcuk Univ Dept Chest Dis Fac Med Konya Turkiye

A computed tomography (CT) scan is an important radiological imaging method in diagnosing pneumonia caused by SARS-CoV-2. Within the scope of the study, three classes of automatic classification - COVID-19 pneumonia, healthy, and other pneumonia - were carried out. Using deep learning as a classifier, a total of 6,377 CT images were used, including 3,364 COVID-19 pneumonia, 1,766 healthy, and 1,247 other pneumonia images. A total of seven architectures, including the most recent convolutional neural network (CNN) architectures, MobileNetV2, ResNet-101, Xception, Inceptionv3, GoogLeNet, EfficientNetB0, and DenseNet201, were used in the study. The classification results were obtained using the CT images, and they were calculated using the feature images obtained by applying local binary patterns on the CT images. The results were then combined with the help of a pipeline algorithm. The results revealed that the best overall accuracy result obtained by using CNN architectures could be improved by 4.87% with a two-step pipeline algorithm. In addition, significant improvements were achieved in all other measurement parameters within the scope of the study. At the end of the study, the highest sensitivity, specificity, accuracy, F-1 score, and Area under the Receiver Operating Characteristic Curve (AUC) values obtained for the COVID-19 pneumonia class were 0.9004, 0.8901, 0.8956, 0.9010, and 0.9600, respectively. The highest overall accuracy value was 0.8332. The most important output of the work carried out is the demonstration that the results obtained with the most successful CNN architectures used in previous studies can be significantly improved thanks to pipeline algorithms.

关键词： COVID-19 convolutional neural networks CT lung classification deep learning local binary patterns DenseNet201 Inceptionv3

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Blind Medical Image Watermarking Based on LBP-DWT for Telemedicine Applications

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2025年 1-26页

作者： Hebbache, Khaled Aiadi, Oussama Khaldi, Belal Benziane, Ali Kasdi Merbah Univ Comp Sci Dept Ghardaia RdBP511 Ouargla 30000 Algeria Lab Intelligence Artificielle & Technol Informat L Ouargla 30000 Algeria Univ Ziane Achour Fac Sci & Technol Djelfa Algeria

With the increasing use of telemedicine in healthcare, the security and integrity of medical images during transmission have become critical. This paper presents a novel blind watermarking scheme local binary Pattern-Discrete Wavelet Transform (LBP-DWT) for medical images based on local binary patterns and the *** Transform in frequency domain. We take advantage of the LBP, which is computationally fast, to improve the watermark's resistance to the different kinds of attacks, and maintain the overall visual quality of the watermarked images. In addition, the DWT could offer a high trade-off between robustness and imperceptibility due to the multi-resolution analysis it provides. During embedding, the LL band (approximation coefficients) of the DWT is selected and divided into 3 x 3 blocks. The resulting LBP codes are then XORed with the embedding bits and hidden in the corresponding blocks using the Least Significant Bit technique. Note that the Arnold transform is used during the embedding step to scramble the watermark, which is then vectorized based on the ZigZag fashion to improve the security of the proposed scheme. To evaluate the performance of the proposed method, extensive experiments are conducted on a dataset of medical images. The watermarked images are tested against various attacks, including compression, noise addition, and cropping. The obtained results demonstrate the effectiveness of the proposed techniques.

关键词： Watermarking Blind Telemedicine Discrete wavelet transform local binary patterns

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