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Hybrid WCA–SCA and modified FRFCM technique for enhancement and segmentation of brain tumor from magnetic resonance images
Abstract
Enhancement of image plays an important and vital role in preprocessing the magnetic resonance images (MRI). At the same time, image segmentation techniques are also essential to detect and remove the noise to enhance the quality of MRI to detect the infected regions of the brain tumor. This paper presents a novel image enhancement technique for preprocessing of brain tumor MRI by hybridizing the Water Cycle Algorithm (WCA) and Sine Cosine Algorithm (SCA). The WCA is based on the process of water cycle in rivers and streams flow in the ocean whereas the SCA follows the cyclic form of sine and cosine trigonometric functions, which permits a search agent to be transposed around the desired solution. In fact, the Fuzzy [Formula: see text] means-based segmentation algorithms have proved their ability in automatic detection of the tumor and help doctors and radiologist to diagnose the type of tumor from the MRI, but, some of the FCM-based algorithms fail to remove the required amount of noise from the MRI which restrict doctors to have better segmentation accuracy. A modified fast and robust FCM (MFRFCM) segmentation technique has been proposed to sharpen and remove noise from MRI to detect the brain tumor to have improved accuracy. In this research work, Dataset-255 is considered from the Harvard medical school. The results from the proposed hybrid WCA-SCA technique are compared with WCA, SCA and comparison results are presented. The hybrid WCA+SCA image enhancement technique attains an accuracy of 99.25% for benign tumor and 98.52% for malignant tumor. Further, the results of modified Fast and Robust FCM (MFRFCM) segmentation results are compared with the conventional FCM-based segmentation algorithms. It is observed that the proposed hybrid WCA-SCA image enhancement technique and modified FRFCM Segmentation outperform in terms of computational time and performance accuracy in contrast to the other algorithms.
... With more computational time, the aforementioned research yields a range of accuracy levels. Te sine cosine algorithm (SCA) was proposed in [37,38] for the optimization problems. Mishra et al. [39] proposed the classifcation of brain tumors using the modifed SCA for local linear radial basis function neural network. ...
... Te PSO, HS, and SCA are the optimization techniques utilized for the weight optimization of several machine learning models. Te models such as PNN [40], SVM [31], "local linear radial basis function neural network (LLRBFNN)" [41], feed-forward neural network [37], ELM [44], and CNN [46,47] were proposed for brain tumor classifcations from MRI images with diferent metaheuristic optimization techniques. Te MHS-SCA is the new algorithm proposed for weight optimization of the ELM model in our research. ...
... Input Image Noisy Image Detected Tumor Figure 13: Segmentation of brain tumor using FLICM. [47], and COVID-19 [37] datasets. Te performance results for diferent datasets are presented in Table 11. ...
Brain cancer deaths are significantly increased in all categories of aged persons due to the abnormal growth of brain tumor tissues in the brain. The death rate can be controlled by accurate early stage brain tumor diagnosis. The detection and classification of brain tumors play a crucial role in early diagnosis and treatment planning. Brain tumor detection and classification have become challenging and time-consuming for domain-specific radiologists and pathologists in medical image analysis. So, automatic detection and classification are essential to reduce the time of diagnosis. In recent years, machine learning classifiers have played an essential role in automatically classifying brain tumors. In this research, an approach based on an improved fuzzy factor fuzzy local information C means (IFF-FLICM) segmentation and hybrid modified harmony search and sine cosine algorithm (MHS-SCA) optimized extreme learning machine (ELM) is proposed for brain tumor detection and classification. The IFF-FLICM algorithm is utilized to accurately segment the brain’s magnetic resonance (MR) images to identify the tumor regions. The Mexican hat wavelet transform is employed for feature extraction from the segmented images. The extracted features from the segmented regions are fed into the MHS-SCA-ELM classifier for classification. The MHS-SCA is proposed to optimize the weights of the ELM model to improve the classification performance. Five distinct multimodal and unimodal benchmark functions are considered for optimization to demonstrate the robustness of the proposed MHS-SCA optimization technique. The image Dataset-255 is considered for this study. The quality measures such as SSIM and PSNR are considered for segmentation. The proposed IFF-FLICM segmentation achieved a peak signal-to-noise ratio (PSNR) of 37.24 dB and a structural similarity index (SSIM) of 0.9823. The proposed MHS-SCA-based ELM model achieved a sensitivity, specificity, and accuracy of 98.78%, 99.23%, and 99.12%. The classification performance results of the proposed MHS-SCA-ELM model are compared with MHS-ELM, SCA-ELM, and PSO-ELM models, and the comparison results are presented.
... Motivated by the above deficiencies, we are proposing a hybrid WCA-PSO optimization of the EELM model for the classification of seizure, non-seizure, and normal categories of EEG signals. To validate the proposed WCA-PSO algorithm, three benchmark functions are utilized and compared with existing PSO [20], WCA [20], SCA [21], and ABC [22] optimization algorithms. The objective of the research is to develop a hybrid WCA-PSO weightoptimized ensemble extreme learning machine model for the classification of seizure and non-seizure EEG signals to improve the classification performance. ...
... Motivated by the above deficiencies, we are proposing a hybrid WCA-PSO optimization of the EELM model for the classification of seizure, non-seizure, and normal categories of EEG signals. To validate the proposed WCA-PSO algorithm, three benchmark functions are utilized and compared with existing PSO [20], WCA [20], SCA [21], and ABC [22] optimization algorithms. The objective of the research is to develop a hybrid WCA-PSO weightoptimized ensemble extreme learning machine model for the classification of seizure and non-seizure EEG signals to improve the classification performance. ...
... The WCA [20] algorithm is based on the directions of the flow of streams and rivers which is merged into the sea with the process of water cycle. The water cycle algorithm starts with the rain fall or precipitation occurrences by generation of streams of population or design variables. ...
Epilepsy seizures are sudden, chaotic neurological functions. The complexity of the brain is revealed via electroencephalography (EEG). Visual examination-based EEG signal analysis is time-consuming, expensive, and difficult. Epilepsy-related mortality is a serious concern. In the diagnostic procedure, computer-assisted diagnosis approaches for precise and automatic detection and classification of epileptic seizures play a crucial role. Due to the classifier's high processing time requirements caused by its mathematical complexity and computational time, we propose a hybrid water cycle algorithm (WCA)–particle swarm optimization (PSO) optimized ensemble extreme learning machine (EELM) classification of seizures to improve the classification performance of the classifier. Firstly, we use feature extraction by utilizing the wavelet transform. The extracted features are aligned as input to the WCA–PSO–EELM for classification. The particle swarm optimization (PSO) algorithm is used to initialize the optimization variables of a WCA algorithm, and the WCA algorithm is used to optimize the input weight of the ELM (i.e., the WCA–PSO–ELM (WPELM)) for classification of seizure and non-seizure EEG signals. University of Bonn database is used for the experiment. The performance measures sensitivity, specificity, and accuracy are considered and achieved 98.78%, 99.23%, and 99.12%, that is, higher than those of other conventional algorithms. To validate the robustness of the WCA–PSO algorithm, three benchmark functions are considered for optimization. The comparison results are presented to visualize the uniqueness of the proposed WCA–PSO–EELM classifier. From the comparison results, it was observed that the proposed WCA–PSO–EELM model outperformed in classifying the seizure and non-seizure EEG signals.
... Machine learning algorithms were able to recognize the wide size variations among the masses [21], but they were unable to offer a suitable scale for the variety of masses. Some of the metaheuristic algorithms such as the water cycle algorithm (WCA) [22], sine cosine algorithm (SCA) [23], Teaching and Learning based optimization (TLBO) [23], artificial bee colony (ABC) [24], particle swarm optimization [25], APSO algorithm [25], harmony search (HS) algorithm [26], etc., were proposed for optimization of weights of the machine learning models, to improve the performance of the classification. In this research, we have proposed a parametric improvement to the WCA algorithm and hybridized with APSO algorithm to improve the classification of the EELM model. ...
... Machine learning algorithms were able to recognize the wide size variations among the masses [21], but they were unable to offer a suitable scale for the variety of masses. Some of the metaheuristic algorithms such as the water cycle algorithm (WCA) [22], sine cosine algorithm (SCA) [23], Teaching and Learning based optimization (TLBO) [23], artificial bee colony (ABC) [24], particle swarm optimization [25], APSO algorithm [25], harmony search (HS) algorithm [26], etc., were proposed for optimization of weights of the machine learning models, to improve the performance of the classification. In this research, we have proposed a parametric improvement to the WCA algorithm and hybridized with APSO algorithm to improve the classification of the EELM model. ...
... In the particle swarm optimization (PSO) [23,24] algorithm, the velocity update equation is given by And the position update equation is given by where the learning factors β 1 and β 2 indicating the local and global position weight coefficients,C 1 , C 2 are the random values taken in-between [0 1], and κ is inertia coefficient. The complexity of the PSO has been reduced by reducing the parameter variations and APSO [36] algorithm has evolved, the velocity equations of the PSO are modified as ...
Breast cancer deaths are increasing rapidly due to the abnormal growth of breast cells in the women's milk duct. Manual cancer diagnosis from mammogram images is also difficult for radiologists and medical practitioners. This paper proposes a novel metaheuristic algorithm-based machine learning model and Fuzzy C Means-based segmentation technique for the classification and detection of breast cancer from mammogram images. At first instance, the fuzzy factor improved fast and robust fuzzy c means (FFI-FRFCM) segmentation is proposed for the segmentation by modifying the member partition matrix of the FRFCM technique. Secondly, a hybrid improved water cycle algorithm-Accelerated particle swarm optimization (IWCA-APSO) optimization, is proposed for weight optimization of the ensemble extreme learning machine (EELM) model. Three benchmark functions are taken for optimization to demonstrate the proposed hybrid IWCA-APSO algorithm's uniqueness. With the INbreast dataset, the IWCA-APSO-based EELM classification shown the sensitivity, specificity, accuracy, and computational time as 99.67%, 99.71%, 99.36%, and 23.8751 s respectively. The proposed IWCA-APSO-based EELM model performs better than the traditional models at classifying breast cancer.
... Sine and cosine algorithm (SCA) [25,26] is an optimization technique where the tuning parameters are utilized for convergence. However, the capacity of SCA optimization is limited due to the proper tuning of parameters to maximize of the optimization performance. ...
... To make convergence faster, one of the parameters has been modified from α 1 to α 11 . According to the sine cosine algorithm [26], the position equation is updated as ...
... Motivated by the robust performance of the optimization capability, we have employed the MSCA-PSO to optimize the weights of the Deep CNN fully connected layer. To validate the robustness of the hybrid MSCA-PSO algorithm, three Benchmark Functions [18] are considered, which are mentioned in Table 2, and the results are presented with WCA (Water Cycle Algorithm) [26,27], SCA [26], and PSO [25] optimization algorithms in Fig. 4a-c. It is observed from Fig. 4a-c that the MSCA-PSO is providing better optimization among all the three benchmark functions. ...
Classification of diseases in fruits is a comparatively multifaceted problem due to varieties, which include irregular shapes, colors, and textures. Deep learning plays a vital role in the classification of diseases from fruits. The agricultural productivity of fruit highly depends on the economic growth of Ethiopia. The production of avocado fruit enriches the economy of farmers in Ethiopia. Improper care causes severe possessions on avocado fruit productivity. The automatic classification of avocado diseases from the healthy fruit will reduce the extensive work of monitoring the big farms. This research presents a Modified Sine Cosine Algorithm–Particle Swarm Optimization (MSCA–PSO)-based MobileNetV2 Convolutional Neural Network (CNN) model for detecting and classifying avocado fruit diseases into healthy and non-healthy diseases from the images. This research considers the Avocado fruit disease image database as input to the proposed model. It is proposed to identify and detect the diseases by taking the high-resolution image database “Fruits 360 dataset” and real images collected from different agricultural farms in Ethiopia. Further, to show the robustness of the proposed hybrid MSCA–PSO algorithm, three benchmark functions, such as the Rastrigin function, Griewanks’s function, and Sphere function, are considered, and the results are presented. The proposed Modified SCA–PSO-based MobileNetV2 convolutional neural network model obtained an accuracy of 98.42% compared to the conventional CNN models, and the comparison results are presented.
... In recent years, many swarm intelligence algorithms have been applied to image segmentation. For example, particle swarm optimization (PSO) [4], ant colony optimization (ACO) [5], Sparrow Search Algorithm (SSA) [6], Gravity Search Algorithm (GSA) [7], firefly algorithm (FA) [8], cuckoo search (CS) [9], grey wolf optimizer (GWO) [10], whale optimization algorithm (WOA) [11], sine cosine algorithm (SCA) [12] fruit fly optimization algorithm (FOA) [13], and so on, which can greatly improve the efficiency and accuracy of image segmentation, so it has been widely used in various fields, such as agriculture, medicine, aerospace, and so on. ...
In view of the problems that the dragonfly algorithm has, such as that it easily falls into the local optimal solution and the optimization accuracy is low, an improved Dragonfly Algorithm (IDA) is proposed and applied to Otsu multi-threshold image segmentation. Firstly, an elite-opposition-based learning optimization is utilized to enhance the diversity of the initial population of dragonflies, laying the foundation for subsequent algorithm iterations. Secondly, an enhanced sine cosine strategy is introduced to prevent the algorithm from falling into local optima, thereby improving its ability to escape from local optima. Then, an adaptive t-distribution strategy is incorporated to enhance the balance between global exploration and local search, thereby improving the convergence speed of the algorithm. To evaluate the performance of this algorithm, we use eight international benchmark functions to test the performance of the IDA algorithm and compare it with the sparrow search algorithm (SSA), sine cosine algorithm (SCA) and dragonfly algorithm (DA). The experiments show that the algorithm performs better in terms of convergence speed and accuracy. At the same time, the Otsu method is employed to determine the optimal threshold, a series of experiments are carried out on six images provided by Berkeley University, and the results are compared with the other three algorithms. From the experimental results, the peak signal-to-noise ratio index (PSNR) and structural similarity index (SSIM) based on the IDA algorithm method are better than other optimization algorithms. The experimental results indicate that the application of Otsu multi-threshold segmentation based on the IDA algorithm is potential and meaningful.
... For the enhancement of the images, the sine cosine algorithm (SCA) has been proposed and compared with the PSO and APSO techniques [34,41]. From Fig. 3 and Fig. 4, it is observed that the fitness value for SCA is 0.4318 and 0.4412 for Benign tumor image1 and image-2, which indicates better image enhancement than the other PSO and APSO methods. ...
Abstract: Image segmentation is an essential technique of brain tumor MRI image processing for automated diagnosis of an image by partitioning it into distinct regions referred to as a set of pixels. The classification of the tumor affected and non-tumor becomes an arduous task for radiologists. This paper presents a novel image enhancement based on the SCA (Sine Cosine Algorithm) optimization technique for the improvement of image quality. The improved FLICM (Fuzzy Local Information C Means) segmentation technique is proposed to detect the affected regions of brain tumor from the MRI brain tumor images and reduction of noise from the MRI images by introducing a fuzzy factor to the objective function. The SCA weight-optimized Wavelet-Extreme Learning Machine (SCA-WELM) model is also proposed for the classification of benign tumors and malignant tumors from MRI brain images. In the first instance, the enhanced images are undergone improved FLICM Segmentation. In the second phase, the segmented images are utilized for feature extraction. The GLCM feature extraction technique is considered for feature extraction. The extracted features are aligned as input to the SCA-WELM model for the classification of benign and malignant tumors. Dataset-255 is considered for evaluating the proposed classification. An accuracy of 99.12% is achieved by the improved FLICM segmentation technique. The classification performance of the SCA-WELM is measured by sensitivity, specificity, accuracy, and computational time and achieved 0.98, 0.99, 99.21%, and 97.2576 seconds respectively. The comparison results of SVM (Support Vector Machine), ELM, SCA-ELM, and proposed SCA-WELM models are presented to show the robustness of the proposed SCA-WELM classification model.
In biomedical research, the brain signal analysis occupies an important space in recent days. Mostly Epileptic seizure detection is a challenging task for all brain signal researcher. In this paper ensemble approach is considered for seizure classification and detection. It is essential of early detection for the patient to save the life. Initially Wavelet transform is used to extract the relevant features. As the feature dimension is high, features are reduced using linear discriminant analysis (LDA). The metaheuristic algorithms named as Water cycle algorithm and accelerated particle swarm optimization (APSO) are integrated to optimize the weights of ensemble extreme learning machine (EELM) for classification. The features are aligned as input to WCA-APSO based EELM model. To validate the proposed integrated algorithm three benchmark functions are utilized for optimization to exhibit the uniqueness. The data is taken from university of BONN database for experimentation. The performance is measured with the parameters like sensitivity, Specificity, and Accuracy are obtained as 99.32%, 99.68%, and 99.16%, The result found superior as compared to earlier methods and outperforms the classification of Epileptic seizure signals.
A novel modified adaptive sine cosine optimization algorithm (MASCA) integrated with particle swarm optimization (PSO) based local linear radial basis function neural network (LLRBFNN) model has been proposed for automatic brain tumor detection and classification. In the process of segmentation, the fuzzy C means algorithm based techniques drastically fails to remove noise from the magnetic resonance images. So, for reduction of noise and smoothening of brain tumor magnetic resonance image an improved fast and robust fuzzy c means algorithm segmentation algorithm has been proposed in this research work. The gray level co-occurrence matrix technique has been employed to extract features from brain tumor magnetic resonance images and the extracted features are fed as input to the proposed modified ASCA–PSO based LLRBFNN model for classification of benign and malignant tumors. In this research work the LLRBFNN model’s weights are optimized by using proposed MASCA–PSO algorithm which provides a unique solution to get rid of the hectic task of radiologist from manual detection. The classification accuracy results obtained from sine cosine optimization algorithm, PSO and adaptive sine cosine optimization algorithm integrated with particle swarm optimization based LLRBFNN models are compared with the proposed MASCA–PSO based LLRBFNN model. It is observed that the result obtained from the proposed model shows better classification accuracy results as compared to the other LLRBFNN based models.
As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information normally leads to a high computational complexity, arising out of an iterative calculation of the distance between the spatial neighbors of pixels and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM, is proposed in this paper. Firstly, the local spatial information of images is incorporated into our FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Secondly, the modification of a pixel's membership, based on the distance between the spatial neighbors of the pixel and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of the pixel's membership. Compared to state-of-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster since it is unnecessary to compute the distance between the neighbors of pixels and clustering centers. In addition, it is efficient for noisy image segmentation because spatial filters are able to improve pixels' membership efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm not only achieves better results, but also requires less time than state-of-the-art algorithms for image segmentation.
In certain medical images, it is possible to achieve contrast enhancement at different levels, in order to highlight different structures present therein. This could be useful to medical specialists to perform more specific diagnoses, in chest radiographs and mammograms, where it is possible to highlight different details when contrast is enhanced. Parameter tuning for Contrast Limited Adaptive Histogram Equalization (CLAHE) using a multi-objective meta-heuristic (SMPSO) is proposed, where the objective functions are the maximization of the amount of information available (via Entropy) and minimization of distortion in the resulting images (Structural Similarity Index, SSIM) simultaneously. The results show that our approach calculates a set of non-dominated solutions or Pareto Set, which represents images with different contrast levels and different levels of commitment between Entropy and Structural Similarity Index. Particularly, these objective functions are contradictory. These enhanced images provide useful information for decision making of specialists.
This paper presents an automated and accurate computer-aided diagnosis (CAD) system for brain
magnetic resonance (MR) image classification. The system first utilizes two-dimensional discrete wavelet
transform (2D DWT) for extracting features from the images. After feature vector normalization, probabilistic
principal component analysis (PPCA) is employed to reduce the dimensionality of the feature
vector. The reduced features are applied to the classifier to categorize MR images into normal and
abnormal. This scheme uses an AdaBoost algorithm with random forests as its base classifier. Three
benchmark MR image datasets, Dataset-66, Dataset-160, and Dataset-255, have been used to validate the
proposed system. A 5 �x 5-fold stratified cross validation scheme is used to enhance the generalization
capability of the proposed scheme. Simulation results are compared with the existing schemes and it is
observed that the proposed scheme outperforms others in all the three datasets
a b s t r a c t In this study a novel optimization method is presented, the so called mine blast algorithm (MBA). The fundamental concepts and ideas of MBA are derived from the explosion of mine bombs in real world. The efficiency of the proposed optimizer is tested via the optimization of several truss structures with discrete variables and its performance is compared with several well-known metaheuristic algorithms. The results show that MBA is able to provide faster convergence rate and also manages to achieve better optimal solutions compared to other efficient optimizers.
This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ¿(g), ¿(s), etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.
In 1997, we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM constrains the typicality values so that the sum over all data points of typicalities to a cluster is one. The row sum constraint produces unrealistic typicality values for large data sets. In this paper, we propose a new model called possibilistic-fuzzy c-means (PFCM) model. PFCM produces memberships and possibilities simultaneously, along with the usual point prototypes or cluster centers for each cluster. PFCM is a hybridization of possibilistic c-means (PCM) and fuzzy c-means (FCM) that often avoids various problems of PCM, FCM and FPCM. PFCM solves the noise sensitivity defect of FCM, overcomes the coincident clusters problem of PCM and eliminates the row sum constraints of FPCM. We derive the first-order necessary conditions for extrema of the PFCM objective function, and use them as the basis for a standard alternating optimization approach to finding local minima of the PFCM objective functional. Several numerical examples are given that compare FCM and PCM to PFCM. Our examples show that PFCM compares favorably to both of the previous models. Since PFCM prototypes are less sensitive to outliers and can avoid coincident clusters, PFCM is a strong candidate for fuzzy rule-based system identification.
This article proposes a computer-aided diagnostic system for feature-based selection classification (CAD-FSC) to detect arrhythmia, atrial fibrillation and normal sinus rhythm. The CAD-FSC methodology encompasses of ECG signal processing phases: ECG pre-processing, R-peak detection, feature extraction, feature selection and ECG classification. Digital filters are used to pre-process the ECG signal and the R-peak is detected by using the Pan-Tompkin’s algorithm. The heart rate variability (HRV) features are extracted in time and frequency domains. Among them, the prominent features are selected with analysis of variance (ANOVA) using Statistical Package for the Social Sciences (SPSS) tool. Cubic support vector machine (C-SVM), coarse Gaussian support vector machine (CG-SVM), cubic k-nearest neighbor (C-kNN) and weighted k-nearest neighbor (W-kNN) classifiers are utilized to validate the CAD-FSC system for three-stage classification. The C-SVM outperforms all other classifiers by giving higher overall accuracy of 98.4% after feature selection of time domain and frequency domain.
In this paper, we propose an automated approach that combines the generalized discriminant analysis (GDA) as feature reduction scheme with radial basis function (RBF) kernel and the online sequential extreme learning machine (OSELM) having Sigmoid, Hardlim, RBF and Sine activation function as binary classifier for detection of congestive heart failure (CHF) and coronary artery disease (CAD). For this analysis, 13 nonlinear features as Correlation Dimension (CD), Detrended Fluctuation Analysis (DFA) as DFA-[Formula: see text]1 and DFA-[Formula: see text]2, Bubble Entropy (BBEn), Sample Entropy (SampEn), Dispersion Entropy (DISEn), Lempel–Ziv Complexity (LZ), Sinai Entropy (SIEn), Improved Multiscale Permutation Entropy (IMPE), Hurst Exponent (HE), Permutation Entropy (PE), Approximate Entropy (ApEn) and Standard Deviation (SD1/SD2) were extracted from Heart Rate Variability (HRV) signals. For validation of proposed method, HRV data were obtained from standard database of normal sinus rhythm (NSR), CHF and CAD subjects. Numerical experiments were done on the combination of database sets such as NSR-CAD, CHF-CAD and NSR-CHF subjects. The simulation results show a clear difference in combination of database sets by using GDA having RBF, Gaussian kernel function and OSELM binary classifier having Sigmoid, RBF and Sine activation function and achieved an accuracy of 98.17% for NSR-CAD, 100% for NSR-CHF and CAD-CHF subjects.
We investigate a new preprocessing approach for MRI glioblastoma brain tumors. Based on combined denoising technique (bilateral filter) and contrast-enhancement technique (automatic contrast stretching based on image statistical information), the proposed approach offers competitive results while preserving the tumor region's edges and original image's brightness. In order to evaluate the proposed approach's performance, quantitative evaluation has been realized through the Multimodal Brain Tumor Segmentation (BraTS 2015) dataset. A comparative study between the proposed method and four state-of-the art preprocessing algorithm attests that the proposed approach could yield a competitive performance for magnetic resonance brain glioblastomas tumor preprocessing. In fact, the result of this step of image preprocessing is very crucial for the efficiency of the remaining brain image processing steps: i.e., segmentation, classification, and reconstruction.
Cardiac diseases are major reason of death in the world populace and the numeral of cases is upsurging every year. Due to cardiac artery disease (CAD), the strength of heart muscles becomes weak and heart pumping is disturbed which may eventually lead to abnormal heart beat and heart failure. Therefore, the beginning stage detection of CAD and cardiac heart failure (CHF) are of prime importance. In this work, we have used a non-invasive diagnosis method as higher order spectra (HOS) for assessment of cardiac diseases. The method indicates whether or not a cardiac heart disease is present, by assessing the cardiac health of subjects using extracted features from heart rate variability (HRV) signals. This assessment is based on 10 spectra nonlinear features. These features were extracted from HRV signals by using the HOS method. For this study, the R-R interval data (i.e. HRV signals) were taken from the standard database of cardiac heart failure (CHF), CAD patients, healthy young (YNG) and Self recorded of healthy young (SELF_YNG) subjects. Statistical assessments were performed on the group of database sets as YNG-CAD, YNG-CHF, SELF_YNG-CAD and Self_YNG-CHF subjects. A Wilcoxon rank sum test (p-value) was used to statistically compare the features extracted by HOS for group of data sets. It indicates whether or not the same features of individual classes of HRV data sets are dissimilar. The results depicted that the all features are very significant (p<0.0001) except the phase entropy (PHE) feature which is not significant for CAD-CHF, SELF_YNG-CAD and SELF_YNG-CHF group of subjects. While in the case of YNG-CAD group of subjects, features like first-order spectral moment of amplitudes of diagonal elements (H3), PHE and logarithmic amplitudes of diagonal elements (H2) are significant (p<0.001) and excluding these features, the remaining features are very significant except MM and H1 which are not significant. The results also depicted that the mean value of sum of logarithmic amplitude (H1), H2, normalized entropy (P1), normalized squared entropy (P2) and PHE features of healthy YNG subjects are having higher values than that of CAD and CHF patients. While weighted center of bi-spectrum (WCOB2) and FLAT spectrum features are lower than CAD and CHF patients compared to YNG subjects. In case of CAD and CHF patients, all the features of CAD patients are having higher values compared to CHF except P1, P2 and WCOB1.
To improve the performance of segmentation for the images corrupted by noise, many variants of standard fuzzy C-means (FCM) clustering algorithm have been proposed that incorporate the local spatial neighbourhood information to perform image segmentation. Among them, the kernel weighted fuzzy local information C-means (KWFLICM) algorithm gives robust to noise image segmentation results by using local spatial image neighbourhood information, it is limited to one-dimensional input data i.e. image intensity. In this paper, we propose a generalisation of KWFLICM (GKWFLICM) that is applicable to M-dimensional input data sets. The proposed algorithm incorporates neighbourhood information among the M-dimensional data, which mitigates the disadvantages of the standard FCM clustering algorithm (sensitive to noise and outliers, poor performance for differently sized clusters and for different density clusters) and greatly improves the clustering performance. Experiments have been performed on several noisy and non-noisy data sets, as well as natural and real-world images, to demonstrate the effectiveness, efficiency, and robustness to noise of the GKWFLICM algorithm by comparing it to kernel fuzzy C-means (KFCM), kernel possibilistic fuzzy C-means (KPFCM), fuzzy local information C-means (FLICM), and KWFLICM.
In this article, a new methodology for denoising of Rician noise in Magnetic Resonance Images (MRI) is presented. MRI imaging creates a distinctive view into the interior of a human body and has become an essential tool of clinical diagnosis. However, Rician noise is a type of artifact inherent to the acquisition process of the magnitude MRI image, making diagnosis difficult. We proposed a moment-based Rician noise reduction technique in anisotropic diffusion filtering. We extend the work of the classical anisotropic diffusion filter and have customized it to remove Rician noise in the magnitude MRI image in 3D domain space. Our proposed scheme shows better results against various quality measures in terms of noise removal and edge preservation while retaining fine textures.
A review on the recent segmentation and tumor grade classification techniques of brain Magnetic Resonance (MR) Images is the objective of this paper. The requisite for early detection of a brain tumor and its grade is the motivation for this study. In Magnetic Resonance Imaging (MRI), the tumor might appear clear but physicians need quantification of the tumor area for further treatment. This is where the digital image processing methodologies along with machine learning aid further diagnosis, treatment, prior and post-surgical procedures, synergizing between the radiologist and computer. These hybrid techniques provide a second opinion and assistance to radiologists in understanding medical images hence improving diagnostic accuracy. This article aims to retrospect the current trends in segmentation and classification relevant to tumor infected human brain MR images with a target on gliomas which include astrocytoma. The methodologies used for extraction and grading of tumors which can be integrated into the standard clinical imaging protocols are elucidated. Lastly, a crucial assessment of the state of the art, future developments and trends are dissertated.
This article introduces a new contrast enhancement algorithm of tone-preserving entropy maximization. Its design objective is to present the maximal amount of information content in the enhanced image, or being optimal in an information theoretical sense, while preventing the loss of tone continuity. The resulting optimization problem can be graph theoretically modeled as the construction of the K-edges maximum-weight path, and it can be solved efficiently by dynamic programming. Moreover, the proposed algorithm is made more effective by being combined with a preprocess of image restoration that aims to correct quantization errors caused by the analog-to-digital conversion of image signals. Empirical evidences are provided to demonstrate the superior visual quality obtained by the new image enhancement algorithm.
Adding spatial penalty terms in fuzzy c-means (FCM) models is an important approach for reducing the noise effects in the process of image segmentation. Though these algorithms have improved the robustness to noises in a certain extent, they still have some shortcomings. First, they are usually very sensitive to the parameters which are supposed to be tuned according to noise intensities. Second, in the case of inhomogeneous noises, using a constant parameter for different image regions is obviously unreasonable and usually leads to an unideal segmentation result. For overcoming these drawbacks, a noise detecting-based adaptive FCM for image segmentation is proposed in this study. Two image filtering methods, playing the roles of denoising and maintaining detail information are utilised in the new algorithm. The parameters for balancing these two parts are computed by measuring the variance of grey-level values in each neighbourhood. Numerical experiments on both synthetic and real-world image data show that the new algorithm is effective and efficient.
Histogram equalization or histogram specification is a widely-used method for image enhancement. In 2005, Wang and Ye used histogram specification to propose an image enhancement method based on variational calculus. However, their method often produces over-enhanced or unnatural images, especially when the input histogram has some high peaks around the middle of the intensity interval. Extending Wang and Ye’s approach, this paper proposes a new image enhancement method called MEDHS (Maximum Entropy Distribution based Histogram Specification), which uses the Gaussian distribution to maximize the entropy and preserve the mean brightness. Specifically, the mean of the Gaussian distribution is equal to the brightness mean of the input image, and the variance of the Gaussian distribution is chosen to maximize the entropy of the output image. Experimental results show that compared to the existing methods, our method preserves the mean brightness more accurately and generates more natural looking images.
Fuzzy c-means clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. Aimed at the problems existed in the FCM clustering algorithm, a kernel-based fuzzy c-means (KFCM) is clustering algorithm is proposed to optimize fuzzy c-means clustering, based on the Genetic Algorithm (GA) optimization which is combined of the improved genetic algorithm and the kernel technique (GAKFCM). In this algorithm, the improved adaptive genetic algorithm is used to optimize the initial clustering center firstly, and then the KFCM algorithm is availed to guide the categorization, so as to improve the clustering performance of the FCM algorithm. In the paper, Matlab is used to realize the simulation, and the performance of FCM algorithm, KFCM algorithm and GAKFCM algorithm is testified by test datasets. The results proved that the GAKFCM algorithm proposed overcomes FCM's defects efficiently and improves the clustering performance greatly.
This paper presents a modified TLBO (teaching–learning-based optimization) approach for the local linear radial basis function neural network (LLRBFNN) model to classify multiple power signal disturbances. Cumulative sum average filter has been designed for localization and feature extraction of multiple power signal disturbances. The extracted features are fed as inputs to the modified TLBO-based LLRBFNN for classification. The performance of the proposed modified TLBO-based LLRBFNN model is compared with the conventional model in terms of convergence speed and classification accuracy. Also, an extreme learning machine (ELM) approach is used to optimize the performance of the proposed LLRBFNN and is compared with the TLBO method in classifying the multiple power signal disturbances. The classification results reveal that although the TLBO approach produces slightly better accuracy in comparison with the ELM approach, the latter is much faster in implementation, thus making it suitable for processing large quantum of power signal disturbance data.
Abstract
Neuromyelitis optica (NMO) is an autoimmune disorder of the central nervous system that usually presents with acute myelitis and/or optic neuritis. Recently some brain magnetic resonance imaging (MRI) findings have been described in NMO that are important in the differential diagnosis. Pencil-thin, leptomeningeal and cloud-like enhancement may be specific to NMO. These patterns are usually seen during relapses. Recognizing these lesions and enhancement patterns may expedite the diagnosis and allows early effective treatment. The purpose of this article is to review the latest knowledge and to share our experience with the contrast enhancement patterns of NMO brain lesions.
This paper presents an improved fuzzy C-means algorithm (FCM) for image segmentation by introducing a trade-off weighted fuzzy factor and kernel metric. The trade-off weighted fuzzy factor depends on space distance of all neighbor pixels and their gray level difference simultaneously. By using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore, the trade-off weighted fuzzy factor and the kernel distance measure are both parameter-free. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of the type of noise.
This paper presents a new algorithm for fuzzy segmentation of MR brain images. Starting from the standard FCM [1] and its bias-corrected version BCFCM [2] algorithm, by splitting up the two major steps of the latter, and by introducing a new factor γ, the amount of required calculations is considerably reduced. The algorithm provides good-quality segmented brain images a very quick way, which makes it an excellent tool to support virtual brain endoscopy. This research has been supported by the Hungarian National Research Fund, Grants No. OTKA T042990 and T029830.
Dynamic contrast enhanced MRI (DCE-MRI) is an emerging imaging protocol in locating, identifying and characterizing breast cancer. However, due to image artifacts in MR, pixel intensity alone cannot accurately characterize the tissue properties. We propose a robust method based on the temporal sequence of textural change and wavelet transform for pixel-by-pixel classification. We first segment the breast region using an active contour model. We then compute textural change on pixel blocks. We apply a three-scale discrete wavelet transform on the texture temporal sequence to further extract frequency features. We employ a progressive feature selection scheme and a committee of support vector machines for the classification. We trained the system on ten cases and tested it on eight independent test cases. Receiver-operating characteristics (ROC) analysis shows that the texture temporal sequence (Az: 0.966 and 0.949 in training and test) is much more effective than the intensity sequence (Az: 0.871 and 0.868 in training and test). The wavelet transform further improves the classification performance (Az: 0.989 and 0.984 in training and test).
We propose a new general method for segmenting brain tumors in 3D magnetic resonance images. Our method is applicable to different types of tumors. First, the brain is segmented using a new approach, robust to the presence of tumors. Then a first tumor detection is performed, based on selecting asymmetric areas with respect to the approximate brain symmetry plane and fuzzy classification. Its result constitutes the initialization of a segmentation method based on a combination of a deformable model and spatial relations, leading to a precise segmentation of the tumors. Imprecision and variability are taken into account at all levels, using appropriate fuzzy models. The results obtained on different types of tumors have been evaluated by comparison with manual segmentations.
Recently, the possibilistic C-means algorithm (PCM) was proposed
to address the drawbacks associated with the constrained memberships
used in algorithms such as the fuzzy C-means (FCM). In this issue, Barni
et al. (1996) report a difficulty they faced while applying the PCM, and
note that it exhibits an undesirable tendency to converge to
coincidental clusters. The purpose of this paper is not just to address
the issues raised by Barni et al., but to go further and analytically
examines the underlying principles of the PCM and the possibilistic
approach, in general. We analyze the data sets used by Barni et al. and
interpret the results reported by them in the light of our
findings
The clustering problem is cast in the framework of possibility
theory. The approach differs from the existing clustering methods in
that the resulting partition of the data can be interpreted as a
possibilistic partition, and the membership values can be interpreted as
degrees of possibility of the points belonging to the classes, i.e., the
compatibilities of the points with the class prototypes. An appropriate
objective function whose minimum will characterize a good possibilistic
partition of the data is constructed, and the membership and prototype
update equations are derived from necessary conditions for minimization
of the criterion function. The advantages of the resulting family of
possibilistic algorithms are illustrated by several examples