Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1056
Статья научная
This work introduces the novelty as an application of histogram-based bins approach with statistical moments for detecting and classifying malaria using blood smear images into parasitized and uninfected cell images and the rising disease of COVID-19/Normal lung images. Proposed algorithms greatly vary as compared to the previous work. This work aims to improve accuracy in detection and classification and reduce feature vector dimensionality. It focuses on detailed image contents extracted into 8 bins by considering the significance of the R, G, and B color component relationship in the formation of each pixel. The texture features are represented by the first four moments for each of the three colors separately. This leads to the generation of 12 features vectors, each of size 8 components for each image in the database. Feature dimensionality reduction is achieved by applying different feature selection techniques to obtain desired optimum feature space. The comprehensive feature analysis presented here identifies many useful findings in order to validate the contribution of each image content uniquely in detection and classification. The proposed approach experimented with two image datasets: the malaria dataset obtained from the National Library of Medicine (NLM) and the lung image dataset acquired from the Radiography Database from Kaggle. The performance of work presented here is evaluated and compared with previous work with the same set of parameters, namely precision, recall, F1 score, and the AUC. We have achieved and improved the performances compared to previous work and also achieved better results even for the COVID-19 dataset.
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Capsaicin-induced Thermal Enhancement on Target Tissues in Hyperthermia
Статья научная
Local thermal enhancement in target tissue is of great interest in tumor hyperthermia. In this study, we proposed a brand-new thermal enhancement protocol for tumor hyperthermia using heat generated from thermoge-nesis evoked by capsaicin, which can safely deliver a totally localized heating to target tissue. A healthy male volunteer was recruited, whose partial areas of the dorsum of hand and posterior aspect of forearm were smeared with 1% (w/w) capsaicin solution, to determine the increase of ther-mogenesis in human body. In addition, animal experiments on healthy Kunming (KM) mice (20-22g) were performed to test the feasibility and efficacy of capsaicin-induced thermal enhancement. These KM mice were first locally smeared with, subcutaneous or intraperitoneal injected of the same capsaicin solution, and then heated by near infrared laser. Preliminary experiments on the volunteer showed an effec-tive temperature increase in the skin area. Animal experi-ments indicated that distinct thermal enhancement in heat-ing effect, and that the thermal enhancement induced by intraperitoneal injection of capsaicin is more obvious than the other two ways. Thus capsaicin can be used as a poten-tial therapeutic adjuvant to locally enhance heating effects in target tissue during tumor hyperthermia.
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Central Moment and Multinomial Based Sub Image Clipped Histogram Equalization for Image Enhancement
Статья научная
The visual appearance of a digital image can be improved through image enhancement algorithm by reducing the noise in an image, improving the color, brightness and contrast of an image for more analysis. This paper introduces an image enhancement algorithm. The image histogram is processed through multinomial curvature fitting function to reduces the number of pixels for each intensity value through minimizing the sum of squared residuals. Then resampling is done to smooth out the computed data. After then histogram clipping threshold is computed by central moment processed on the resampled data value to restrict the over enhancement rate. Histogram is equally divided into two sub histograms. The sub histograms are equalized by transfer function to merged the sub images into one output image. The output image is further improved by reducing the environmental haze effect by applying Matlab imreducehaze method, which gives the final output image. Matlab simulation results demonstrate that the proposed method outperforms than other compared methods in terms of both quantitative and qualitative performance evaluation applied on colorfulness based PCQI (C-PCQI), and blind image quality measure of enhanced images (BIQME) image quality metrics.
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Change Energy Image for Gait Recognition: An Approach Based on Symbolic Representation
Статья научная
Gait can be identified by observing static and dynamic parts of human body. In this paper a variant of gait energy image called change energy images (CEI) are generated to capture detailed static and dynamic information of human gait. Radon transform is applied to CEI in four different directions (vertical, horizontal and two opposite cross sections) considering four different angles to compute discriminative feature values. The extracted features are represented in the form of interval –valued type symbolic data. The proposed method is capable of recognizing an individual when he/she have variations in their gait due to different clothes they wear, in different normal conditions and carrying a bag. A similarity measure suitable for the proposed gait representation is explored for the purpose of establishing similarity match for gait recognition. Experiments are conducted on CASIA database B and the results have shown better recognition performance compared to some of the existing methods.
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Channel Estimation of massive MIMO Using Code Shift Keying Pilot Symbols (CSK-PS)
Статья научная
The increasing demand for bandwidth by mobile users in wireless communication becomes a challenging issue to the research community. Several theories and models have been proposed to mitigate this issue. The most effective and commonly used approach to resolve the demand shortage of bandwidth is the massive Multi-Input and Multi-Output (MIMO) approach in which the number of transmitting and receiving antennas is placed at the base station (BS) to fulfill the issue of bandwidth. However, this technique suffers from various issues in estimating the channel due to interference, beamforming, and pilot contamination. In this paper, a novel channel estimation technique is being proposed using Code Shifting Keying symbols as pilot signals (CSK-PS) to minimize the pilot contamination. These signals are used as reference signals and the received signal is detected. The presented approach reduces the interference (pilot contamination) and improves the channel estimation in massive MIMO networks by using the modified expected propagation estimation method (MEPE). The presented approach is validated using mat-lab.
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Chaotic Behavior of Heart Rate Signals during Chi and Kundalini Meditation
Статья научная
Nonlinear dynamics has been introduced to the analysis of biological data and increasingly recognized to be functionally relevant. The aim of this study is to quantify and compare the contribution of nonlinear and chaotic dynamics of human heart rate variability during two forms of meditation: (i) Chinese Chi (or Qigong) meditation and (ii) Kundalini Yoga meditation. For this purpose, Poincare plots, Lyapunov exponents and Hurst exponents of heart rate variability signals were analyzed. In this study, we examined the different behavior of heart rate signals during two specific meditation techniques. The results show that heart rate signals became more periodic and their chaotic behavior was decreased in both techniques of meditation. Therefore, nonlinear chaotic indices may serve as a quantitative measure for psychophysiological states such as meditation. In addition, different forms of meditation appear to differentially alter specific components of heart rate signals.
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Chaotic Pixel Value Differencing
Статья научная
Pixel Value Differencing (PVD) is a spatial steganography technique that is area sensitive and considers complete visual invisibility while data hiding. While Least Significant Bit Approach (LSB) still remains the most popular technique and is simplest in approach its simplicity makes it vulnerable against steganalysis. Our proposed technique is an enhancement over traditional Pixel Value Differencing. We have added a layer of security using chaotic encryption approaches. Also some PVD based hybrid techniques are compared and analyzed to draw conclusions on the basis of various statistical measures.
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Chilli Dryness and Ripening Stages Assessment Using Machine Vision
Статья научная
The quality of chilli is prime concern for farmers, traders and chilli processing industries. The effective determination of chilli dryness and ripening stages are important factors in determining its quality and chilli shelf life with respect to manual estimation of ripening/dryness that are complex and time consuming. Chilli dryness and ripeness prediction at post-harvest stage by non-destructive machine vision technologies have potential of fair valuation for chilli produce for the chilli stalk holders. Chilli pericarp color values calculated from RGB, HSV and CIE-L*a*b* color space, texture properties using edge-wrinkles parameters are described by histogram of oriented gradients (HOG). LDA(linear discriminant analysis), RF(random-forest) and SVM(support vector machine) classifiers are analysed for performance accuracy for chilli dryness identification and chilli ripening stages using the machine vision. The chilli dryness identification accuracies of 83%, 85.4% and 83.5% are achieved using chilli color and HOG features with LDA, Random Forest and SVM classifiers respectively. Chilli ripening stage identification with combined chilli feature set of {color, HOG, SURF and LBP} using Support Vector Machine (SVM) average classifier accuracy is 90.56% across four chilli ripening stages. This work is simple with rapid, intelligent and high accuracy of chilli dryness and ripening identification by using machine vision approach has prospect in real-time chilli quality monitoring and grading. The results yielded were promising quality measurements compared previous studies.
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Classification and Recognition of Printed Hindi Characters Using Artificial Neural Networks
Статья научная
Character Recognition is one of the important tasks in Pattern Recognition. The complexity of the character recognition problem depends on the character set to be recognized. Neural Network is one of the most widely used and popular techniques for character recognition problem. This paper discusses the classification and recognition of printed Hindi Vowels and Consonants using Artificial Neural Networks. The vowels and consonants in Hindi characters can be divided in to sub groups based on certain significant characteristics. For each group, a separate network is designed and trained to recognize the characters which belong to that group. When a test character is given, appropriate neural network is invoked to recognize the character in that group, based on the features in that character. The accuracy of the network is analyzed by giving various test patterns to the system.
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Classification of Alzheimer Disease using Gabor Texture Feature of Hippocampus Region
Статья научная
The aim of this research is to propose a methodology to classify the subjects into Alzheimer disease and normal control on the basis of visual features from hippocampus region. All three dimensional MRI images were spatially normalized to the MNI/ICBM atlas space. Then, hippocampus region was extracted from brain structural MRI images, followed by application of two dimensional Gabor filter in three scales and eight orientations for texture computation. Texture features were represented on slice by slice basis by mean and standard deviation of magnitude of Gabor response. Classification between Alzheimer disease and normal control was performed with linear support vector machine. This study analyzes the performance of Gabor texture feature along each projection (axial, coronal and sagittal) separately as well as combination of all projections. The experimental results from both single projection (axial) as well as combination of all projections (axial, coronal and sagittal), demonstrated better classification performance over other existing method. Hence, this methodology could be used as diagnostic measure for the detection of Alzheimer disease.
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Classification of Brain Activity in Emotional States Using HOS Analysis
Статья научная
This paper proposes an emotion recognition system using EEG signals and higher order spectra. A visual induction based acquisition protocol is designed for recording the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) under two emotional states of participants, calm-neutral and negatively exited. After pre-processing the signals, higher order spectra are employed to extract the features for classifying human emotions. We used Genetic Algorithm (GA) and Support vector machine (SVM) for optimum features selection for the classifier. In this research, we achieved an average accuracy of 82.32% for the two emotional states using Linear Discriminant Analysis (LDA) classifier. We concluded that, HOS analysis could be an accurate tool in the assessment of human emotional states. We achieved to same results compared to our previous studies.
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Статья научная
Epilepsy is a chronic brain disorder which affects normal neuronal activity of the brain. It results in sudden repeated episodes of higher electrical activity due to sensory disturbance. Electroencephalogram (EEG) plays an important role in the diagnosis of epilepsy. Currently, manual observation of EEG is done by experienced neurologist to diagnose epilepsy and related disorders. However, automated system is a promising method for seizure detection and diagnosis. The EEG signals recorded from the patient’s scalp are preprocessed, and classified as seizure and non-seizure based on the extracted signal features. The procedure significantly eliminates the error involved in manual observation. Due to non-linear nature of EEG, joint time-frequency methods are used to analyse the EEG signals. This paper proposes a EEG feature extraction technique using Dual Tree Complex Wavelet Transform (DTℂWT) to overcome the problem of shift variance in DWT. The estimation of improved multi-scale Permutation Entropy (IMPmEn) is done for the level-3 subband of DTℂWT. The performance of the Least Squares Support Vector Machine (LS-SVM) classifier is tested using these features and highest classification accuracy of 99.87 % is obtained on the real time EEG database.
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Classification of FNIRS Using Wigner-ville Distribution and CNN
Статья научная
Consumers undergo an intellectual burden when working with technological programs. Mostly in situations of several activities. For instance, while communicating when driving with the navigation device. It is not necessary to divert users from their primary duties in such circumstances. In memory cycles and related workload, the pre-frontal cortex (PFC) has a significant role to play. In this study, we have used data from 10 participants to evaluate the task behaviors in PFC with usable near-infrared spectroscopy (fNIRS), which is a non-invasive imaging modality. In classification, CNN research has been state of the art. This has undermined the need to extract features manually. In order to assess the mental workload, we implemented a time-frequency approach with CNN approach. Rather than traditional CNN network we used ResNet50 pretrained network here. Application of Wigner-Ville Distribution in Functional Imaging is introduced here. The proposed CNN approach achieves a considerable average improvement relative to conventional methods. The results across differences in time window length are benchmarked. Satisfactory result obtained with twenty five second window for which the CNN yields 98% correct classification where traditional CNN achieved 89% accuracy.
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Classification of High Blood Pressure Persons Vs Normal Blood Pressure Persons Using Voice Analysis
Статья научная
The human voice is remarkable, complex and delicate. All parts of the body play some role in voice production and may be responsible for voice dysfunction. The larynx contains muscles that are surrounded by blood vessels connected to circulatory system. The pressure of blood in these vessels should be related with dynamic variation of vocal cord parameters. These parameters are directly related with acoustic properties of speech. Acoustic voice analysis can be used to characterize the pathological voices. This paper presents the classification of high blood pressure and normal with the aid of voice signal recorded from the patients. Various features have been extracted from the voice signal of healthy persons and persons suffering from high blood pressure. Simulation results show differences in the parameter values of healthy and pathological persons. Then an optimum feature vector is prepared and kmean classification algorithm was implemented for data classification. The 79% classification efficiency was obtained.
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Classification of Mammogram Abnormalities Using Pseudo Zernike Moments and SVM
Статья научная
The most common malignancy observed among Indian women is the breast cancer. However, the cancer is detectable earlier by means of mammograms. Computer Aided Diagnostic (CAD) techniques are the boon to medical industry and these techniques intend to support the physicians in diagnosis. In this paper, a novel CAD system for the detection and classification of the abnormalities in the mammogram is presented. The proposed work is organized into four important phases and they are pre-processing, segmentation, feature extraction and classification. The pre-processing phase intends to remove unwanted noise and make the mammograms suitable for the next process. The segmentation phase aims to extract the areas of interest to proceed with further process. Feature extraction is the most important phase, which is meant for extracting the texture features from the area of interest. This work employs pseudo zernike moments for extracting features, owing to the noise resistance power and description ability. Finally, Support Vector Machine (SVM) is employed as the classifier, so as to distinguish between the malignant and normal mammograms. The performance of the proposed work is evaluated by several experimentations and the results are satisfactory in terms of accuracy, specificity and sensitivity.
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Classification of Mammograms into Normal, Benign and Malignant based on Fractal Features
Статья научная
Modern life style of women has made them more vulnerable to breast cancer and it is considered as the largest cause of mortality among women. This paper presents a novel method to classify mammograms into normal ones, with benign and malignant microcalcifications, and with malignant and benign tumors using fractal features derived from fractal dimension. Here, three fractal dimension estimation methods such as Differential Box Counting (DBC), Triangular Prism Surface Area (TPSA) and Blanket methods are used for computing the six fractal features utilized for the classification. The new fractal feature f6 obtained using TPSA method is found to be the best with 100% classification accuracy. The average value of f6 is found to be 0.1110, 0.2875, 0.4743, 0.5271 and 0.8558, for normal, benign masses, benign and malignant microcalcifications and malignant masses respectively. The classification performance of the different features was analyzed using the Receiver Operating Characteristics (ROC).
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Статья научная
Diabetic retinopathy is a severe complication retinal disease caused by advanced diabetes mellitus. Long suffering of this disease without threatment may cause blindness. Therefore, early detection of diabetic retinopathy is very important to prevent to become proliferative. One indication that a patient has diabetic retinopathy is the existence of hard exudates besides other indications such as microaneurysms and hemorrhages. In this study, the existence of hard exudates is applied to classify the moderate and severe grading of non-proliferative diabetic retinopathy in retinal fundus images. The hard exudates are segmented using K-means clustering. The segmented regions are extracted to obtain a feature vector which consists of the areas, the perimeters, the number of centroids and its standard deviation. Using three different classifiers, i.e. soft margin Support Vector Machine, Multilayer Perceptron, and Radial Basis Function Network, we achieve the accuracy of 89.29%, 91.07%, and 85.71% respectively, for 56 training data and 56 testing data of retinal images.
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Classification of masses in digital mammograms using firefly based optimization
Статья научная
Breast cancer is one of the leading causes of death in women all over the world. Computer based diagnosis system assists radiologist in the effective treatment of breast cancer. To design an efficient classification system for masses in digital mammograms, we have to use efficient algorithms for feature selection to reduce the feature space of mammogram classification problem. The proposed study explores the use of Firefly algorithm to select a subset of features. Artificial neural network and support vector machine classifiers are employed to evaluate fitness of the selected features. Features selected by Firefly algorithm are used to classify masses into benign and malignant, using artificial neural network and support vector machine classifiers. The proposed method employed over 651 mammograms obtained from the Database of Digitized Screen-film Mammograms. Classification results show that Firefly algorithm with artificial neural network is superior to Firefly algorithm with support vector machine. Artificial neural network achieves accuracy of 95.23% with 94.43% sensitivity, 93.94% specificity and area under curve Az=0.965±0.008. On the other hand, support vector machine classifier achieves an accuracy of 92.47% with 96.14% sensitivity, 88.53% specificity and area under curve Az=0.951±0.009.Results obtained with Firefly algorithm shows that it will be useful for effective treatment of breast cancer.
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Classification of textures based on noise resistant fundamental units of complete texton matrix
Статья научная
One of the popular descriptor for texture classification is the local binary pattern (LBP). LBP and its variants derives local texture features effectively. This paper integrates the significant local features derived from uniform LBPs(ULBP) and threshold based conversion factor non-uniform (NULBP) with complete textons. This integrated approach represents the complete local structural features of the image. The ULBPs are proposed to overcome the wide histograms of LBP. The ULBP contains fundamental aspects of local features. The LBP is more prone to noise and this may transform ULBP into NULBP and this degrades the overall classification rate. To addresses this, this paper initially transforms back, the ULBPs that are converted in to NULBPs due to noise using a threshold based conversion factor and derives noise resistant fundamental texture (NRFT) image. In the literature texton co-occurrence matrix(TCM) and multi texton histogram (MTH) are derived on a 2x2 window. The main disadvantage of the above texton groups is they fail in representing complete textons. In this paper we have integrated our earlier approach “complete texton matrix (CTM)” [16] on NRFT images. This paper computes the gray level co-occurrence matrix (GLCM) features on the proposed NRFCTM (noise resistant fundamental complete texton matrix) and the features are given to machine learning classifiers for a precise classification. The proposed method is tested on the popular databases of texture classification and classification results are compared with existing methods.
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Clustering Belief Functions using Extended Agglomerative Algorithm
Статья научная
Clustering belief functions is not easy because of uncertainty and the unknown number of clusters. To overcome this problem, we extend agglomerative algorithm for clustering belief functions. By this extended algorithm, belief distance is taken as dissimilarity measure between two belief functions, and the complete-link algorithm is selected to calculate the dissimilarity between two clusters. Before every merging of two clusters, consistency test is executed. Only when the two clusters are consistent, they can merge, otherwise, dissimilarity between them is set to the largest value, which prevents them from merging and assists to determine the number of final clusters. Typical illustration shows same promising results. Firstly, the extended algorithm itself can determine the number of clusters instead of needing to set it in advance. Secondly, the extended algorithm can deal with belief functions with hidden conflict. At last, the algorithm extended is robust.
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