Brain tumor segmentation and classification via adaptive CLFAHE with hybrid classification

This article exploits a new brain tumor classification model that includes five steps like (a) denoising, (b) skull stripping, (c) segmentation, (d) feature extraction and (e) classification. Initially, the image is subjected under the denoising process, where the noise removal procedure is carried out by employing the entropy-based trilateral filter. Then, the denoised image is applied to the skull stripping process via Otsu thresholding and morphology segmentation. Subsequently, the next step is the segmentation, where the image is segmented by deploying the adaptive CLFAHE (contrast limited fuzzy adaptive histogram equalization) technique. Once the segmentation is completed, gray-level co-occurrence matrix (GLCM) based features are extracted. Finally, the extracted features are processed under hybrid classification model to attain enhanced classification rate. Here, hybrid classification hybrids two classifiers namely deep belief network (DBN) and Bayesian regularization classifier. The vital contribution of this research work exists in the optimal selection of hidden neurons in the DBN. Along with this, the membership function (bounding limits) of fuzzy logic is optimally selected. For this, a new lion exploration based whale optimization (LE-WO) algorithm is proposed in this article that hybrids the concept of (lion algorithm) LA and (whale optimization algorithm) WOA. Finally, the performance of proposed LE-WO is compared over the other methods in terms of accuracy, sensitivity, specificity, precision, negative predictive value (NPV), F1 _ score and Matthews correlation coefficient (MCC), False positive rate (FPR), False negative rate (FNR) and false discovery rate (FDR) and proves the betterments of proposed work. From the outcomes, the accuracy measure of proposed model at 60th population size is 1.98%, 1.81%, 1.32%, 3.46% and 0.75% better than PSO, FF, GWO, WOA and LA, respectively. Similarly, in 80th population size, the performance of the implemented model is 4.47%, 5.04%, 3.96%, 6.29% and 1.37% superior to PSO, FF, GWO, WOA and LA, respectively. Thus, the betterment of the adopted scheme is validated in an effective manner.     

Fuzzy relevance vector machine based classification of lung nodules in computed tomography images

Lung cancer is a critical disease with growing death rate, hence, the faster identification and treatment of lung cancer is essential. In medical image processing, the traditional methods like support vector machine, relevance vector machine for classifying cancer tissues are less sensitive to false data and required optimal improvement in classification accuracy. The proposed system of accurate lung cancer classification is obtained by a hybrid fuzzy relevance vector machine (FRVM) classifier with correlation negation ant colony optimization (CNACO) algorithm. This system provides enhanced accuracy and sensitivity by implementing two stages of feature extraction, image thresholding, and tumor segmentation, with a novel feature selection and tumor classification algorithm. The best features are selected by the proposed CNACO algorithm. The selected features are labeled and classified by FRVM classifier. The proposed classification scheme is validated on lung image database consortium and image database resource initiative public database and obtained accuracy of about 98.75%.     

A new images segmentation method based on modified particle swarm optimization algorithm

The partitioning of an image into several constituent components is called image segmentation. Many approaches have been developed; one of them is the particle swarm optimization (PSO) algorithm, which is widely used. PSO algorithm is one of the most recent stochastic optimization strategies. In this article, a new efficient technique for the magnetic resonance imaging (MRI) brain images segmentation thematic based on PSO is proposed. The proposed algorithm presents an improved variant of PSO, which is particularly designed for optimal segmentation and it is called modified particle swarm optimization. The fitness function is used to evaluate all the particle swarm in order to arrange them in a descending order. The algorithm is evaluated by performance measures such as run time execution and the quality of the image after segmentation. The performance of the segmentation process is demonstrated by using a defined set of benchmark images and compared against conventional PSO, genetic algorithm, and PSO with Mahalanobis distance based segmentation methods. Then we applied our method on MRI brain image to determinate normal and pathological tissues. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 265–271, 2013     

Deep learning and optimization algorithms for automatic breast cancer detection

Breast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer-aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state-of-the-art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy.     

MRI brain image classification—a hybrid approach

The present article proposes a novel computer‐aided diagnosis (CAD) technique for the classification of the magnetic resonance brain images. The current method adopt color converted hybrid clustering segmentation algorithm with hybrid feature selection approach based on IGSFFS (Information gain and Sequential Forward Floating Search) and Multi‐Class Support Vector Machine (MC‐SVM) classifier technique to segregate the magnetic resonance brain images into three categories namely normal, benign and malignant. The proposed hybrid evolutionary segmentation algorithm which is the combination of WFF(weighted firefly) and K‐means algorithm called WFF‐K‐means and modified cuckoo search (MCS) and K‐means algorithm called MCS‐K‐means, which can find better cluster partition in brain tumor datasets and also overcome local optima problems in K‐means clustering algorithm. The experimental results show that the performance of the proposed algorithm is better than other algorithms such as PSO‐K‐means, color converted K‐means, FCM and other traditional approaches. The multiple feature set comprises color, texture and shape features derived from the segmented image. These features are then fed into a MC‐SVM classifier with hybrid feature selection algorithm, trained with data labeled by experts, enabling the detection of brain images at high accuracy levels. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. The proposed method provides highest classification accuracy of greater than 98% with high sensitivity and specificity rates of greater than 95% for the proposed diagnostic model and this shows the promise of the approach. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 226–244, 2015     

A new approach to simulate characterization of particulate matter employing support vector machines

This paper, for the first time, applies the support vector machines (SVMs) paradigm to identify the optimal segmentation algorithm for physical characterization of particulate matter. Size of the particles is an essential component of physical characterization as larger particles get filtered through nose and throat while smaller particles have detrimental effect on human health. Typical particulate characterization processes involve image reading, preprocessing, segmentation, feature extraction, and representation. Of these various steps, knowledge based selection of optimal image segmentation algorithm (from existing segmentation algorithms) is the key for accurately analyzing the captured images of fine particulate matter. Motivated by the emerging machine-learning concepts, we present a new framework for automating the selection of optimal image segmentation algorithm employing SVMs trained and validated with image feature data. Results show that the SVM method accurately predicts the best segmentation algorithm. As well, an image processing algorithm based on Sobel edge detection is developed and illustrated.     

An Improved Unsupervised Image Segmentation Method Based on Multi-Objective Particle Swarm Optimization Clustering Algorithm

Most image segmentation methods based on clustering algorithms use single-objective function to implement image segmentation. To avoid the defect, this paper proposes a new image segmentation method based on a multi-objective particle swarm optimization (PSO) clustering algorithm. This unsupervised algorithm not only offers a new similarity computing approach based on electromagnetic forces, but also obtains the proper number of clusters which is determined by scale-space theory. It is experimentally demonstrated that the applicability and effectiveness of the proposed multi-objective PSO clustering algorithm.     

Fitness adaptive deer hunting-based region growing and recurrent neural network for melanoma skin cancer detection

This proposal aims to enhance the accuracy of a dermoscopic skin cancer diagnosis with the aid of novel deep learning architecture. The proposed skin cancer detection model involves four main steps: (a) preprocessing, (b) segmentation, (c) feature extraction, and (d) classification. The dermoscopic images initially subjected to a preprocessing step that includes image enhancement and hair removal. After preprocessing, the segmentation of lesion is deployed by an optimized region growing algorithm. In the feature extraction phase, local features, color morphology features, and morphological transformation-based features are extracted. Moreover, the classification phase uses a modified deep learning algorithm by merging the optimization concept into recurrent neural network (RNN). As the main contribution, the region growing and RNN improved by the modified deer hunting optimization algorithm (DHOA) termed as Fitness Adaptive DHOA (FA-DHOA). Finally, the analysis has been performed to verify the effectiveness of the proposed method.     

Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T₂-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate. In contrast to other state-of-the-art methods, namely Adaptive Wind Driven Optimization (AWDO), Adaptive Bacterial Foraging (ABF) and Particle Swarm Optimization (PSO), the proposed algorithm has been found to be better at producing the best objective function, Peak Signal-to-Noise Ratio (PSNR), Standard Deviation (STD) and lower computational time values. Further, it was observed thatthe segmented image gives greater detail when the threshold level increases. Moreover, the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus, these images will lead to better segments of gray, white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.     

Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non-Glioma image based on the optimized set of features. Furthermore, energy-based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy.     

Image segmentation using multiresolution wavelet analysis and expectation-maximization (EM) algorithm for digital mammography

This article presents a novel algorithm for image segmentation via the use of the multiresolution wavelet analysis and the expectation maximization (EM) algorithm. The development of a multiresolution wavelet feature extraction scheme is based on the Gaussian Markov random field (GMRF) assumption in mammographic image modeling. Mammographic images are hierarchically decomposed into different resolutions. In general, larger breast lesions are characterized by coarser resolutions, whereas higher resolutions show finer and more detailed anatomical structures. These hierarchical variations in the anatomical features displayed by multiresolution decomposition are further quantified through the application of the Gaussian Markov random field. Because of its uniqueness in locality, adaptive features based on the nonstationary assumption of GMRF are defined for each pixel of the mammogram. Fibroadenomas are then segmented via the fuzzy C-means algorithm using these localized features. Subsequently, the segmentation results are further enhanced via the introduction of a maximum a posteriori (MAP) segmentation estimation scheme based on the Bayesian learning paradigm. Gibbs priors or Gibbs random fields have also been incorporated into the learning scheme of the present research with very effective outcomes. In this article, the EM algorithm for MAP estimation is formulated. The EM algorithm provides an iterative and computationally simple algorithm based on the incomplete data concept. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 491–504, 1997     

Forty-Sixth Annual Exhibition of the Institute of Physics and the Physical Society

Abstract

The frequency histogram of connected elements (FHCE) is a recently proposed algorithm that has successfully been applied in various medical image segmentation tasks. The FHCE is based on the idea that most pixels belong to the same class as their neighbouring pixels. However, the FHCE performance relies to a great extent on the optimal selection of a threshold parameter. Since evaluating segmentation results is a highly subjective process, a collection of threshold values must typically be examined. No algorithm has been proposed to automate the determination of the threshold parameter value of the FHCE. This study presents a method based on the fuzzy C-means clustering algorithm, designed to automatically generate optimal threshold values for the FHCE. This new approach was applied as a part of a structured sequence of image processing steps in order to facilitate segmentation of microcalcifications in digitized mammograms. A unique threshold value was generated for each mammogram, taking into account the different grey-level patterns based on different compositions of various breast tissues in it. The segmentation algorithm was tested on 100 mammograms (50 collected from the Mammographic Image Analysis Society and 50 normal mammograms onto which a number of simulated microcalcifications were generated). The algorithm was able to detect subtle microcalcifications with sensitivity ranging from 93 to 98%, False alarm ratio from 3 to 5% and false negatives variability from 2 to 3%.     

A complete automated algorithm for segmentation of tissues and identification of tumor region in T1, T2, and FLAIR brain images using optimization and clustering techniques

Tissues in brain are the most complicated parts of our body, a clear examination and study are therefore required by a radiologist to identify the pathologies. Normal magnetic resonance (MR) scanner is capable of producing brain images with bounded tissues, where unique and segregated views of the tissues are required. A distinguished view upon the images is manually impossible and can be subjected to operator errors. With the assistance of a soft computing technique, an automated unique segmentation upon the brain tissues along with the identification of the tumor region can be effectively done. These functionalities assist the radiologist extensively. Several soft computing techniques have been proposed and one such technique which is being proposed is PSO‐based FCM algorithm. The results of the proposed algorithm is compared with fuzzy C‐means (FCM) and particle swarm optimization (PSO) algorithms using comparison factors such as mean square error (MSE), peak signal to noise ratio (PSNR), entropy (energy function), Jaccard (Tanimoto Coefficient) index, dice overlap index and memory requirement for processing the algorithm. The efficiency of the PSO‐FCM algorithm is verified using the comparison factors.     

Improved cuckoo search with particle swarm optimization for classification of compressed images

The need for a general purpose Content Based Image Retrieval (CBIR) system for huge image databases has attracted information-technology researchers and institutions for CBIR techniques development. These techniques include image feature extraction, segmentation, feature mapping, representation, semantics, indexing and storage, image similarity-distance measurement and retrieval making CBIR system development a challenge. Since medical images are large in size running to megabits of data they are compressed to reduce their size for storage and transmission. This paper investigates medical image retrieval problem for compressed images. An improved image classification algorithm for CBIR is proposed. In the proposed method, RAW images are compressed using Haar wavelet. Features are extracted using Gabor filter and Sobel edge detector. The extracted features are classified using Partial Recurrent Neural Network (PRNN). Since training parameters in Neural Network are NP hard, a hybrid Particle Swarm Optimization (PSO) – Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.     

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.     

Earlier detection of cancer regions from MR image features and SVM classifiers

In this article, we examine the use of several segmentation algorithms for medical image classification. This work detects the cancer region from magnetic resonance (MR) images in earlier stage. This is accomplished in three stages. In first stage, four kinds of region‐based segmentation techniques are used such as K‐means clustering algorithm, expectation–maximization algorithm, partial swarm optimization algorithm, and fuzzy c‐means algorithm. In second stage, 18 texture features are extracting using gray level co‐occurrence matrix (GLCM). In stage three, classification is based on multi‐class support vector machine (SVM) classifier. Finally, the performance analysis of SVM classifier is analyzed using the four types of segmentation algorithm for a group of 200 patients (32—Glioma, 32—Meningioma, 44—Metastasis, 8—Astrocytoma, 72—Normal). The experimental results indicate that EM is an efficient segmentation method with 100% accuracy. In SVM, quadratic and RBF (σ = 0.5) kernel methods provide the highest classification accuracy compared to all other SVM kernel methods. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 196–208, 2016     

On the optimal control of steel annealing processes via various versions of genetic and particle swarm optimization algorithms

7 This paper elucidates the computation of optimal controls for steel annealing processes as hybrid systems which comprise of one or more furnaces integrated with plant-wide planning and scheduling operations. A class of hybrid system is considered to capture the trade-off between metallurgical quality requirement and timely product delivery. Various optimization algorithms including particle swarm optimization algorithm (PSO) with time varying inertia weight methods, PSO with globally and locally tuned parameters (GLBest PSO), parameter free PSO (pf-PSO) and PSO like algorithm via extrapolation (ePSO), real coded genetic algorithm (RCGA) and two-phase hybrid real coded genetic algorithm (HRCGA) are considered to solve the optimal control problems for the steel annealing processes (SAP). The optimal solutions including optimal line speed, optimal cost, and job completion time and convergence rate obtained through all these optimization algorithms are compared with each other and also those obtained via the existing method, forward algorithm (FA). Various statistical analyses and analysis of variance (ANOVA) test and hypothesis t-test are carried out in order to compare the performance of each method in solving the optimal control problems of SAP. The comparative study of the performance of the various algorithms indicates that the PSO like algorithms, pf-PSO and ePSO are equally good and are also better than all the other optimization methods considered in this chapter.     

Fuzzy entropy‐based MR brain image segmentation using modified particle swarm optimization

This article presents an image segmentation technique based on fuzzy entropy, which is applied to magnetic resonance (MR) brain images in order to detect brain tumors. The proposed method performs image segmentation based on adaptive thresholding of the input MR images. The image is classified into two membership functions (MFs) of the fuzzy region: Z‐function and S‐function. The optimal parameters of these fuzzy MFs are obtained using modified particle swarm optimization (MPSO) algorithm. The objective function for obtaining the optimal fuzzy MF parameters is considered to be the maximum fuzzy entropy. Through a number of examples, The performance is compared with existing entropy based object segmentation approaches and the superiority of the proposed method is demonstrated. The experimental results are compared with the exhaustive search method and Otsu's segmentation technique. The result shows the proposed fuzzy entropy‐based segmentation method optimized using MPSO achieves maximum entropy with proper segmentation of infected areas and with minimum computational time. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 281–288, 2013     

Fractional gravitational search-radial basis neural network for bone marrow white blood cell classification

Automotive image segmentation systems are becoming an important tool in the medical field for disease diagnosis. The white blood cell (WBC) segmentation is crucial, because it plays an important role in the determination of the diseases and helps experts to diagnose the blood disease disorders. The precise segmentation of the WBCs is quite challenging because of the complex contents in the bone marrow smears. In this paper, a novel neural network (NN) classifier is proposed for the classification of the bone marrow WBCs. The proposed NN classifier integrates the fractional gravitation search (FGS) algorithm for updating the weight in the radial basis function mapping for the classification of the WBC based on the cell nucleus feature. The experimentation of the proposed FGS-RBNN classifier is carried on the images collected from the publically available dataset. The performance of the proposed methodology is evaluated over the existing classifier approaches using the measures accuracy, sensitivity, and specificity. The results show that the classification using the nucleus features alone can be utilized to achieve the classification with the better accuracy. Moreover, the classification performance of the proposed FGS-RBNN is better than the existing classifiers, and it is proved to be the efficacious classifier with a classification accuracy of 95%.     

Deeply supervised U‐Net for mass segmentation in digital mammograms

Mass detection is a critical process in the examination of mammograms. The shape and texture of the mass are key parameters used in the diagnosis of breast cancer. To recover the shape of the mass, semantic segmentation is found to be more useful rather than mere object detection (or) localization. The main challenges involved in the mass segmentation include: (a) low signal to noise ratio (b) indiscernible mass boundaries, and (c) more false positives. These problems arise due to the significant overlap in the intensities of both the normal parenchymal region and the mass region. To address these challenges, deeply supervised U‐Net model (DS U‐Net) coupled with dense conditional random fields (CRFs) is proposed. Here, the input images are preprocessed using CLAHE and a modified encoder‐decoder‐based deep learning model is used for segmentation. In general, the encoder captures the textual information of various regions in an input image, whereas the decoder recovers the spatial location of the desired region of interest. The encoder‐decoder‐based models lack the ability to recover the non‐conspicuous and spiculated mass boundaries. In the proposed work, deep supervision is integrated with a popular encoder‐decoder model (U‐Net) to improve the attention of the network toward the boundary of the suspicious regions. The final segmentation map is also created as a linear combination of the intermediate feature maps and the output feature map. The dense CRF is then used to fine‐tune the segmentation map for the recovery of definite edges. The DS U‐Net with dense CRF is evaluated on two publicly available benchmark datasets CBIS‐DDSM and INBREAST. It provides a dice score of 82.9% for CBIS‐DDSM and 79% for INBREAST.