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Automated Adrenal Gland Disease Classes Using Patch-Based center symmetric local binary pattern Technique with CT Images

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JOURNAL OF DIGITAL IMAGING 2023年第3期36卷 879-892页

作者： Sut, Suat Kamil Koc, Mustafa Zorlu, Gokhan Serhatlioglu, Ihsan Barua, Prabal Datta Dogan, Sengul Baygin, Mehmet Tuncer, Turker Tan, Ru-San Acharya, U. Rajendra Adiyaman Training & Res Hosp Dept Radiol Adiyaman Turkiye Firat Univ Fac Med Dept Radiol Elazig Turkiye Firat Univ Fac Med Dept Biophys Elazig Turkiye Univ Southern Queensland Sch Business Informat Syst Toowoomba Qld 4350 Australia Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Firat Univ Coll Technol Dept Digital Forens Engn Elazig Turkiye Ardahan Univ Coll Engn Dept Comp Engn Ardahan Turkiye Natl Heart Ctr Dept Cardiol Singapore Singapore Duke NUS Med Sch Singapore Singapore Ngee Ann Polytech Dept Elect & Comp Engn Singapore 599489 Singapore SUSS Univ Sch Sci & Technol Dept Biomed Engn Singapore Singapore Asia Univ Dept Biomed Informat & Med Engn Taichung Taiwan

Incidental adrenal masses are seen in 5% of abdominal computed tomography (CT) examinations. Accurate discrimination of the possible differential diagnoses has important therapeutic and prognostic significance. A new handcrafted machine learning method has been developed for the automated and accurate classification of adrenal gland CT images. A new dataset comprising 759 adrenal gland CT image slices from 96 subjects were analyzed. Experts had labeled the collected images into four classes: normal, pheochromocytoma, lipid-poor adenoma, and metastasis. The images were preprocessed, resized, and the image features were extracted using the center symmetric local binary pattern (CS-LBP) method. CT images were next divided into 16 x 16 fixed-size patches, and further feature extraction using CS-LBP was performed on these patches. Next, extracted features were selected using neighborhood component analysis (NCA) to obtain the most meaningful ones for downstream classification. Finally, the selected features were classified using k-nearest neighbor (kNN), support vector machine (SVM), and neural network (NN) classifiers to obtain the optimum performing model. Our proposed method obtained an accuracy of 99.87%, 99.21%, and 98.81% with kNN, SVM, and NN classifiers, respectively. Hence, the kNN classifier yielded the highest classification results with no pathological image misclassified as normal. Our developed fixed patch CS-LBP-based automatic classification of adrenal gland pathologies on CT images is highly accurate and has low time complexity O(w x h + k) . It has the potential to be used for screening of adrenal gland disease classes with CT images.

关键词： Adrenal gland center symmetric local binary pattern Neighborhood component analysis Classification

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Effective Compression of center symmetric local binary pattern

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MEHRAN UNIVERSITY RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY 2017年第2期36卷 209-224页

作者： Baber, Junaid Bakhtyar, Maheen Ullah, Ihsan Rehman, Mariam Khalid, Muhammad Univ Balochistan Dept Comp Sci & Informat Technol Quetta Pakistan Lahore Coll Women Univ Dept Comp Sci Lahore Pakistan

In this paper, we propose simple and effective compression of CSLBP (center symmetric local binary pattern) descriptors, which is a textured based operator and mostly used as key point descriptor. With default parameters for computation, it is 256-length descriptor for each keypoint or affine patch. CSLBP is an extended form of LBP (local binary patterns). The calculation of CSLBP descriptor is effective, robust, and straightforward for different image transformations for instance;image blurring and illumination alteration. However, an improvement in time and space consumption of CSLBP can be attained by means of simple compression. For this reason, CSLBP is a smart choice for smart phones as well as large databases. We reduce the descriptor length (dimensions) upto 50% without applying any techniques of dimensionality reduction like PCA (Principle Component Analysis) or LDA (Linear Discriminant Analysis). The compressed CSLBP descriptor is denoted as C-CSLBP. The performance of C-CSLBP is evaluated on state-of-the-art datasets using standard metrics. It is quantitatively shown by experiments that C-CSLBP is equivalently effective compared to CSLBP despite of reduced dimensions.

关键词： Dimensionality Reduction center symmetric local binary pattern Q-CSLBP Image Retrieval Descriptor

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Intramuscular EMG classifier for detecting myopathy and neuropathy

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2023年第2期33卷 659-669页

作者： Jose, Shobha Selvaraj, Thomas George Samuel, Kenneth Philip, Jobin T. Jothiraj, Sairamya Nanjappan Pandian, Subathra Muthu Swamy Handiru, Vikram Shenoy Suviseshamuthu, Easter S. Karunya Inst Technol & Sci Sch Engn & Technol Coimbatore Tamil Nadu India Eastern Univ Dept Biol St Davids PA USA Kessler Fdn Ctr Mobil & Rehabil Engn Res W Orange NJ 07052 USA Rutgers State Univ Rutgers New Jersey Med Sch Dept Phys Med & Rehabil Newark NJ USA

This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one-dimensional (1$$ 1 $$-D) signals. In this approach, the 1$$ 1 $$-D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer-Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated 10$$ 10 $$-fold cross-validation, CSLBP-Combined-Classifiers-BMMV (CSLBP-CC-BMMV) achieved an average classification accuracy of 92.80$$ 92.80 $$%, 94.25$$ 94.25 $$%, and 93.71$$ 93.71 $$% for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP-CC-BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.

关键词： center symmetric local binary pattern classification electromyography majority voting neuromuscular disorders

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Facial Expression Recognition Algorithm Based on local Feature Extraction 4

Facial Expression Recognition Algorithm Based on Local Featu...

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4th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Du, Huabing Chen, Yaqi Shu, Zhong School of Electronic Information Engineering Jingchu University of Technology Hubei Jingmen China Jingmen Rongmedia Network Technology Co. Ltd Hubei Jingmen China

ISBN: (纸本)9798350359589

This paper employs the center symmetric local binary pattern (CSLBP) algorithm for extracting fine features in facial expression recognition. Additionally, the Rotation Invariant local Phase Quantization (RILPQ) algorithm is utilized to capture accurate features for recognizing blurry facial expressions. The proposed approach achieves comprehensive and detailed extraction of different facial expression features. By constructing a multi-nonlinear SVM classifier structure model, the system accomplishes the classification of facial expression features and facilitates facial recognition. The experimental statistical data indicates that our algorithm is most suitable for a 9×9 pixel block grid. The accuracy of facial expression feature extraction is around 92%, and the accuracy of facial expression classification and recognition is also approximately 92%. For images with an 8×8 feature dimension, the algorithm's processing time is approximately 0.23 seconds. © 2024 IEEE.

关键词： center symmetric local binary pattern Facial Expression Recognition Image Feature Extraction Nonlinear Support Vector Machine Classifier Rotation Invariant local Phase Quantizatio

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Comparative Study of Various Feature Extraction Techniques for Pedestrian Detection 9th

Comparative Study of Various Feature Extraction Techniques f...

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9th International Conference of Information and Communication Technology [ICICT]

作者： Hiranmai, M. Krupa, Niranjana B. Nagaraj, H. K. PES Univ Bengaluru 85 India Robert Bosch Engn & Business Solut Private Ltd Bengaluru India

This paper presents, feature extraction techniques such as center symmetric local binary pattern (CSLBP), extended CSLBP (XCSLBP), speeded-up robust feature (SURF) with 64 and 128 feature descriptors and histograms of oriented gradients (HOG) applied on a set of images from INRIA person database, to detect pedestrians. About fifteen feature sets created using different combinations of the aforementioned methods are compared using two detectors, random forest (RF) and support vector machine (SVM). Performance validation is done based on the accuracy, precision, recall and space required for storing feature vectors. Experimental results have shown that CSLBP and the novel XCSLBP+CSLBP feature sets yield 100% accuracy, when used with RF classifier, whereas, the novel SURF-128+XCSLBP combination and SVM linear classifier gave 99.2% accuracy in detecting pedestrians. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Pedestrian detection center symmetric local binary pattern speeded up robust feature extended center symmetric local binary pattern HOG

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center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年 32卷 224-236页

作者： Verma, Manisha Raman, Balasubramanian Indian Inst Technol Roorkee Dept Math Roorkee Uttar Pradesh India Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India

Content based image retrieval is a common problem for a large image database. Many methods have been proposed for image retrieval for some particular type of datasets. In the proposed work, a new image retrieval technique has been introduced. This technique is useful for different kind of dataset. In the proposed method, center symmetric local binary pattern has been extracted from the original image to obtain the local information. Co-occurrence of pixel pairs in local pattern map have been observed in different directions and distances using gray level co-occurrence matrix. Earlier methods have utilized histogram to extract the frequency information of local pattern map but co-occurrence of pixel pairs is more robust than frequency of patterns. The proposed method is tested on three different category of images, i.e., texture, face and medical image database and compared with typical state-of-the-art local patterns. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Content based image retrieval local binary pattern center symmetric local binary pattern Gray level co-occurrence matrix Feature vector Texture feature MIT VisTex texture database Brodatz texture database ORL face database OASIS MRI image database

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Comparative Study of Various Feature Extraction Techniques for Pedestrian Detection

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Procedia Computer Science 2019年 154卷 622-628页

作者： M Hiranmai Niranjana Krupa B H K Nagaraj PES UNIVERSITY Bengaluru-85 India Robert Bosch Engineering and Business Solutions Private Limited Bengaluru India

关键词： Pedestrian detection center symmetric local binary pattern speeded up robust feature extended center symmetric local binary pattern HOG

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A Technique for Face Verification across Age Progression with Large Age Gap 4

A Technique for Face Verification across Age Progression wit...

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4th IEEE International Conference on Signal Processing, Computing and Control (ISPCC)

作者： Verma, Garima Jindal, Alka Gupta, Savita Kaur, Lakhwinder PEC Univ Technol Comp Sci & Engn Chandigarh India UIET Comp Sci & Engn Chandigarh India Punjabi Univ Dept Comp Engn Patiala Punjab India

ISBN: (纸本)9781509058389

Advancements in the field of biometrics have led to the development of face identification and verification systems. The problem of face verification faces many challenges;however, the challenge of progressing age for verification of human faces remains to be the most challenging of all. The work in the field of face verification across age progression is limited and the field requires more attention and research. This work proposes a technique for face verification which can handle large age gaps. The proposed technique extracts center symmetric local binary patterns (CSLBP) to represent the features of the face image. CSLBP has been widely used as a texture descriptor in face recognition. The performance of CSLBP features when combined with weighted K-Nearest Neighbor classifier is best when compared with Support Vector Machine and Tree based classifiers. This work studies the performance of the proposed method on increasing age gaps. Usually, the performance of any face verification system deteriorates or stagnates as the age gap increases;however, the performance of the proposed method improves as the age gap increases. The experiments are performed on MORPH database.

关键词： Face Verification large age gaps aging center symmetric local binary pattern k-nearest neighbor

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Breast Cancer Detection using Non-invasive Method for Real Time Dataset 5

Breast Cancer Detection using Non-invasive Method for Real T...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Hiremath, Basavaraj Prasannakumar, S. C. Praneethi, K. Jain Univ Bangalore Karnataka India MSRIT Bangalore Karnataka India RV Coll Engn Dept Elect & Instrumentat Engn Bangalore Karnataka India MSRTH Bangalore Karnataka India

ISBN: (纸本)9781509020287

Breast Cancer is one of the most horrible and dangerous diseases that affect women health. This paper aims to detect the breast cancer in a non-invasive manner with the help of mammograms and enables advanced characterization of the lesion using following steps: mammogram enhancement using adaptive median filter, cancer area detection using seed value based segmentation, extraction of CSLBP and GLDM features and finally, classification of cancer using RBF-SVM. The Algorithm is evaluated on real time mammogram breast dataset consisting of 249 images and for the considered dataset, accuracy is found to be 95.18%

关键词： Region of Interest Adaptive Median Filter center symmetric local binary pattern GLDM SVM Classifier

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