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Burn characterization using object-oriented hyperspectral image classification

JOURNAL OF BIOPHOTONICS 2022年第11期15卷 e202200106页

作者： Parasca, Sorin Viorel Calin, Mihaela Antonina Carol Davila Univ Med & Pharm Bucharest Bucharest Romania Emergency Clin Hosp Plast Reconstruct Surg & Burn Bucharest Romania Natl Inst Res & Dev Optoelect INOE 2000 409 Atomistilor StPOB MG5 Magurele 077125 Ilfov Romania

This paper presents a new approach based on hyperspectral imaging combined with an object-oriented classification method that allows the generation of burn depth classification maps facilitating easier characterization of burns. Hyperspectral images of 14 patients diagnosed with burns on the upper and lower limbs were acquired using a pushbroom hyperspectral imaging system. The images were analyzed using an object-oriented classification approach that uses objects with specific spectral, textural and spatial attributes as the minimum unit for classifying information. The method performance was evaluated in terms of overall accuracy, sensitivity, precision and specificity computed from the confusion matrix. The results revealed that the approach proposed in this study performed well in differentiating burn classes with a high level of overall accuracy (95.99% +/- 0.60%), precision (97.30% +/- 2.46%), sensitivity (97.23% +/- 3.02%) and specificity (98.02% +/- 1.98%). In conclusion, the object-based approach for burns hyperspectral images classification can provide maps that can help surgeons identify with better precision different depths of burn wounds.

关键词： burn depth precision segmentation sensitivity specificity support vector machine classifier

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Data Mining Applications in Understanding Electricity Consumers' Behavior: A Case Study of Tulkarm District, Palestine

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ENERGIES 2019年第22期12卷

作者： AbuBaker, Maher An Najah Natl Univ Dept Management Informat Syst Fac Engn & Informat Technol Nablus Palestine An Najah Natl Univ POB 7 Nablus Palestine

This paper presents a comprehensive data analysis and visualization of electricity consumers' prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers' prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers' behavior in electricity consumption and their behavior in charging their electricity meter's smart cards in terms of quantities charged and charging frequencies in different time periods, areas and tariffs are used. Understanding consumers' behavior will support planning and decision making at strategic, tactical and operational levels. This analysis is useful for predicting and forecasting future demand with a certain degree of accuracy. Monthly, weekly, daily and hourly time periods are covered in the analysis. Outliers detection using visualization tools such as box plot is applied. K-means unsupervised machine learning clustering algorithm is implemented. The support vector machine classification method is applied. As a result of this study, electricity consumers' behavior in different areas, tariffs and timing periods is understood and presented by numbers and graphs and new electricity consumer segmentation is proposed.

关键词： data mining data visualization K-means clustering support vector machine classifier principal components analysis elbow method silhouette analysis

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A fast and accurate algorithm for eye opening or closing detection based on local maximum vertical derivative pattern

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第6期24卷 5124-5134页

作者： Tafreshi, Marzieh Fotouhi, Ali Mohammad Tafresh Univ Dept Elect Engn Tafresh *** Iran

In this paper, a fast and accurate algorithm is proposed to recognize open and closed eye states. In the proposed algorithm, firstly, a hierarchical preprocessing stage is used to detect eye areas. This stage employs Haar features to detect face area, color and intensity mappings to extract eye candidate areas, and some simple geometrical relations for final decision of the eye regions. In the second stage of algorithm for detecting eye state, a new proposed descriptor based on histogram of local maximum vertical derivative pattern (LMVDP) in eye areas is extracted and applied to support vector machine (SVM) classifier. Proposed descriptor, while having low computational complexity, is well defined to describe eye features and hence can distinguish well between open and closed eyes. Experimental results on test images show that the proposed algorithm can correctly detect the eye state by the ratio of 98.2%, which is higher than other similar algorithms.

关键词： Eye detection eye state recognition local maximum vertical derivative pattern support vector machine classifier

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An efficient image segmentation and classification of lung lesions in pet and CT image fusion using DTWT incorporated SVM

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MICROPROCESSORS AND MICROSYSTEMS 2021年 82卷

作者： Priya, R. Mohana Venkatesan, P. Sri Chandrasekharendra Saraswathi Viswa Mahavidya Dept Elect & Commun Engn Kanchipuram Tamil Nadu India

Automatic segmentation and classification of fused lung Computed Tomography (CT) and Positron Emission Tomography (PET) images is presented. This system consists of four basic stages: 1). Lung image fusion process;2). Segmentation of fused lung CT/PET images;3). Post pre-processing;4). Classification of fused lung images. In the first step, the lung image fusion process is made by deep learning method. At first, the input CT/PET images are decomposed by Dual Tree m-band Wavelet Transform (DTWT). The coefficients of DTWT are fused by deep learning method. This fused image of CT/PET is the input for the following steps. In the next step, the fused CT/PET images are decomposed by DTWT. It produces lower and higher frequency sub-band coefficients. Then the lower frequency components are set to zero. Then higher frequency components are used for reconstruction. Then the clustering-based thresholding method is used for segmentation. In post pre-processing step, the unwanted small regions are removed by morphological operations. Then the lung region is detected. At last, in the classification step, the features are extracted by the intensity and texture-based features. These features are classified by hybrid classifiers like support vector machine (SVM) are used. The performance of the system has a higher classification accuracy of 99% using SVM classifier.

关键词： Computed tomography Positron emission tomography Fused lung image Dual tree m-band wavelet transform Thresholding support vector machine classifier

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Computer-aided skin cancer diagnosis based on a New meta-heuristic algorithm combined with support vector method

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年 68卷

作者： Bi, Dezhong Zhu, Dongxia Sheykhahmad, Fatima Rashid Qiao, Mingqi Shandong Univ Tradit Chinese Med Inst Tradit Chinese Med 4655 Univ RdUniv Sci Pk Jinan 250355 Shandong Peoples R China Jinan City Peoples Hosp Jinan Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

Skin cancer has grown significantly over the past decades, and the importance of its initial treatment is increasing day by day. This study aims to use a computer-aided diagnosis automatic system for the exact diagnosis of cancerous cases. In the present study, after applying pre-processing operations including contrast enhancement, image thresholding, and mathematical morphology for highlighting the important areas, feature extraction based on 20 different features has been applied. Then, for decreasing the system complexity, a new improved version of the world cup optimization algorithm is utilized for the features pruning. After optimal selection of the features, the injected into a support vector machine as a classifier to determine the cancerous areas. Experimental results are compared by different new algorithms including the classic SVM method, hybrid optimized neural networks based on gray wolf optimizer and particle swarm optimization algorithm, and also convolutional neural network. Results obtained for skin lesions images from two different databases indicate that the proposed method has higher efficiency than other published methods, demonstrating even better performance than the well-known classification method, the convolutional neural networks. The proposed method presents the highest correct detection rate - 92.64 for ACS images and 87.5 for ISIC images with very low false acceptance rates (4.41 % and 9 % for ACS and ISIC images, respectively) and even less false rejection rates, this is, 2.94 % on ACS images and 3.5 % on ISIC images. These results prove that the proposed method may be reliably used to diagnose skin cancer through dermoscopy images.

关键词： Computer-aided diagnosis Skin cancer Kapur image thresholding Feature extraction Feature classification support vector machine classifier Chaotic world cup optimization algorithm

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Image Retrieval Method Combining Bayes and SVM classifier Based on Relevance Feedback with Application to Small-scale Datasets

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2022年第4期29卷 1236-1246页

作者： Lai, Siyu Yang, Qinghua He, Wenjin Zhu, Yuanzhong Wang, Juan North Sichuan Med Coll Dept Med Imaging Nanchong Peoples R China China West Normal Univ Coll Comp Sci Shida Rd Nanchong 637002 Sichuan Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small- and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 - Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small- and medium-scale CBIR systems with inadequate samples.

关键词： Bayes classifier image retrieval relevance feedback support vector machine classifier transfer learning

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Extraction of Urban Objects in Cloud Shadows on the basis of Fusion of Airborne LiDAR and Hyperspectral Data

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REMOTE SENSING 2019年第6期11卷

作者： Man, Qixia Dong, Pinliang Shandong Normal Univ Coll Geog & Environm Jinan 250014 Shandong Peoples R China Univ North Texas Dept Geog & Environm Denton TX 76203 USA

Feature extraction in cloud shadows is a difficult problem in the field of optical remote sensing. The key to solving this problem is to improve the accuracy of classification algorithms by fusing multi-source remotely sensed data. Hyperspectral data have rich spectral information but highly suffer from cloud shadows, whereas light detection and ranging (LiDAR) data can be acquired from beneath clouds to provide accurate height information. In this study, fused airborne LiDAR and hyperspectral data were used to extract urban objects in cloud shadows using the following steps: (1) a series of LiDAR and hyperspectral metrics were extracted and selected;(2) cloud shadows were extracted;(3) the new proposed approach was used by combining a pixel-based support vector machine (SVM) and object-based classifiers to extract urban objects in cloud shadows;(4) a pixel-based SVM classifier was used for the classification of the whole study area with the selected metrics;(5) a decision-fusion strategy was employed to get the final results for the whole study area;(6) accuracy assessment was conducted. Compared with the SVM classification results, the decision-fusion results of the combined SVM and object-based classifiers show that the overall classification accuracy is improved by 5.00% (from 87.30% to 92.30%). The experimental results confirm that the proposed method is very effective for urban object extraction in cloud shadows and thus improve urban applications such as urban green land management, land use analysis, and impervious surface assessment.

关键词： LiDAR data hyperspectral data shadow extraction decision fusion multi-resolution object-based classifier support vector machine classifier

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Evaluation and comparison of different machine learning approaches to auditory spectro-temporal receptive field estimation

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BMC Neuroscience 2011年第1期12卷 1-2页

作者： Arne F Meyer Jörn Anemüller Jan P Diepenbrock Frank W Ohl Medical Physics Department of Phyics Carl-von-Ossietzky University Oldenburg Germany Leibniz Institute for Neurobiology and Institute of Biology Otto-von-Guericke University Magdeburg Germany

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Activity recognition with cooperative radar systems at C and K band

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JOURNAL OF ENGINEERING-JOE 2019年第20期2019卷 7100-7104页

作者： Shrestha, Aman Li, Haobo Fioranelli, Francesco Le Kernec, Julien Univ Glasgow Sch Engn Commun Sensing & Imaging Grp Glasgow Lanark Scotland Univ Elect Sci & Technol China Sch Informat & Elect Chengdu Sichuan Peoples R China

Remote health monitoring is a key component in the future of healthcare with predictive and fall risk estimation applications required in great need and with urgency. Radar, through the exploitation of the micro-Doppler effect, is able to generate signatures that can be classified automatically. In this work, features from two different radar systems operating at C band and K band have been used together co-operatively to classify ten indoor human activities with data from 20 subjects with a support vector machine classifier. Feature selection has been applied to remove redundancies and find a set of salient features for the radar systems, individually and in the fused scenario. Using the aforementioned methods, we show improvements in the classification accuracy for the systems from 75 and 70% for the radar systems individually, up to 89% when fused.

关键词： health care Biology and medical computing activity recognition radar imaging Doppler radar pattern classification cooperative communication radar computing cooperative radar systems Knowledge engineering techniques feature selection feature extraction Radar equipment, systems and applications support vector machine classifier Electrical engineering computing salient features Image recognition risk estimation applications microDoppler effect indoor human activities remote health monitoring support vector machines Other topics in statistics

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Target classification using Renyi entropy features of cyclic bispectrum

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JOURNAL OF ENGINEERING-JOE 2019年第21期2019卷 7717-7720页

作者： Wang, Cai Li, Yan Gao, Meiguo Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China

In this study, a target classification method is proposed based on a third-order cyclic statistics technique. The authors introduce cyclic bispectrum (CBS) to reveal the non-linear cyclic nature contained by the micro-Doppler signal, and it is observed that the non-zero peaks generated by some cyclic non-linear nature form unique distribution patterns on CBS slices for different targets. Then, a Renyi entropy is calculated for each CBS slice to measure the information content and thus achieve an entropy sequence. Subsequently, considering the entropy sequence as a feature vector, the support vector machine classifier is used to perform the target classification. Experimental results based on real measured data validate the effectiveness of the method.

关键词： entropy feature extraction support vector machines signal classification pattern classification statistical analysis target classification method third-order cyclic statistics technique microDoppler signal nonzero peaks cyclic nonlinear nature form unique distribution patterns CBS slice entropy sequence feature vector Renyi entropy features cyclic bispectrum support vector machine classifier

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