Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising

Magnetic resonance imaging (MRI) is extensively exploited for more accuratepathological changes as well as diagnosis. Conversely, MRI suffers from variousshortcomings such as ambient noise from the environment, acquisition noise from theequipment, the presence of background tissue, breathing motion, body fat, etc.Consequently, noise reduction is critical as diverse types of the generated noise limit the efficiency of the medical image diagnosis. Local polynomial approximation basedintersection confidence interval (LPA-ICI) filter is one of the effective de-noising filters.This filter requires an adjustment of the ICI parameters for efficient window size selection.From the wide range of ICI parametric values, finding out the best set of tunes values is itselfan optimization problem. The present study proposed a novel technique for parameteroptimization of LPA-ICI filter using genetic algorithm (GA) for brain MR imagesde-noising. The experimental results proved that the proposed method outperforms theLPA-ICI method for de-noising in terms of various performance metrics for different noisevariance levels. Obtained results reports that the ICI parameter values depend on the noisevariance and the concerned under test image

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images. (C) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Enhanced non-parametric sequence learning scheme for internet of things sensory data in cloud infrastructure

The Internet of Things (IoT) Cloud is an emerging technology that enables machine-to-machine, human-to-machine and human-to-human interaction through the Internet. IoT sensor devices tend to generate sensory data known for their dynamic and heterogeneous nature. Hence, it makes it elusive to be managed by the sensor devices due to their limited computation power and storage space. However, the Cloud Infrastructure as a Service (IaaS) leverages the limitations of the IoT devices by making its computation power and storage resources available to execute IoT sensory data. In IoT-Cloud IaaS, resource allocation is the process of distributing optimal resources to execute data request tasks that comprise data filtering operations. Recently, machine learning, non-heuristics, multi-objective and hybrid algorithms have been applied for efficient resource allocation to execute IoT sensory data filtering request tasks in IoT-enabled Cloud IaaS. However, the filtering task is still prone to some challenges. These challenges include global search entrapment of event and error outlier detection as the dimension of the dataset increases in size, the inability of missing data recovery for effective redundant data elimination and local search entrapment that leads to unbalanced workloads on available resources required for task execution. In this thesis, the enhancement of Non-Parametric Sequence Learning (NPSL), Perceptually Important Point (PIP) and Efficient Energy Resource Ranking- Virtual Machine Selection (ERVS) algorithms were proposed. The Non-Parametric Sequence-based Agglomerative Gaussian Mixture Model (NPSAGMM) technique was initially utilized to improve the detection of event and error outliers in the global space as the dimension of the dataset increases in size. Then, Perceptually Important Points K-means-enabled Cosine and Manhattan (PIP-KCM) technique was employed to recover missing data to improve the elimination of duplicate sensed data records. Finally, an Efficient Resource Balance Ranking- based Glow-warm Swarm Optimization (ERBV-GSO) technique was used to resolve the local search entrapment for near-optimal solutions and to reduce workload imbalance on available resources for task execution in the IoT-Cloud IaaS platform. Experiments were carried out using the NetworkX simulator and the results of N-PSAGMM, PIP-KCM and ERBV-GSO techniques with N-PSL, PIP, ERVS and Resource Fragmentation Aware (RF-Aware) algorithms were compared. The experimental results showed that the proposed NPSAGMM, PIP-KCM, and ERBV-GSO techniques produced a tremendous performance improvement rate based on 3.602%/6.74% Precision, 9.724%/8.77% Recall, 5.350%/4.42% Area under Curve for the detection of event and error outliers. Furthermore, the results indicated an improvement rate of 94.273% F1-score, 0.143 Reduction Ratio, and with minimum 0.149% Root Mean Squared Error for redundant data elimination as well as the minimum number of 608 Virtual Machine migrations, 47.62% Resource Utilization and 41.13% load balancing degree for the allocation of desired resources deployed to execute sensory data filtering tasks respectively. Therefore, the proposed techniques have proven to be effective for improving the load balancing of allocating the desired resources to execute efficient outlier (Event and Error) detection and eliminate redundant data records in the IoT-based Cloud IaaS Infrastructure

U-Net based deep convolutional neural network models for liver segmentation from CT scan images

Liver segmentation is a critical task for diagnosis, treatment and follow-up processes of liver cancer. Computed Tomography (CT) scans are the common medical image modality for the segmentation task. Liver segmentation is considered a very hard task for many reasons. Medical images are limited for researchers. Liver shape is changing based on the patient position during the CT scan process, and varies from patient to another based on the health conditions. Liver and other organs, for example heart, stomach, and pancreas, share similar gray scale range in CT images. Liver treatment using surgery operations is very critical because liver contains significant amount of blood and the position of liver is very close to critical organs like heart, lungs, stomach, and crucial blood veins. Therefore the accuracy of segmentation is critical to define liver and tumors shape and position especially when the treatment surgery conducted using radio frequency heating or cryoablation needles. In the literature, convolutional neural networks (CNN) have achieved very high accuracy on liver segmentation and the U-Net model is considered the state-of-the-art for the medical image segmentation task. Many researchers have developed CNN models based on U-Net and stacked U-Nets with/without bridged connections. However, CNN models need significant number of labeled samples for training and validation which is not commonly available in the case of liver CT images. The process of generating manual annotated masks for the training samples are time consuming and need involvement of expert clinical doctors. Data augmentation has thus been widely used in boosting the sample size for model training. Using rotation with steps of 15o and horizontal and vertical flipping as augmentation techniques, the lack of dataset and training samples issue is solved. The choice of rotation and flipping because in the real life situations, most of the CT scans recorded while the while patient lies on face down or with 45o, 60o,90o on right side according to the location of the tumor. Nonetheless, such process has brought up a new issue for liver segmentation. For example, due to the augmentation operations of rotation and flipping, the trained model detected part of the heart as a liver when it is on the wrong side of the body. The first part of this research conducted an extensive experimental study of U-Net based model in terms of deeper and wider, and variant bridging and skip-connections in order to give recommendation for using U-Net based models. Top-down and bottom-up approaches were used to construct variations of deeper models, whilst two, three, and four stacked U-Nets were applied to construct the wider U-Net models. The variation of the skip connections between two and three U-Nets are the key factors in the study. The proposed model used 2 bridged U-Nets with three extra skip connections between the U-Nets to overcome the flipping issue. A new loss function based on minimizing the distance between the center of mass between the predicted blobs has also enhanced the liver segmentation accuracy. Finally, the deep-supervision concept was integrated with the new loss functions where the total loss was calculated as the sum of weighted loss functions over each weighted deeply supervision. It has achieved a segmentation accuracy of up to 90%. The proposed model of 2 bridged U-Nets with compound skip-connections and specific number of levels, layers, filters, and image size has increased the accuracy of liver segmentation to ~90% whereas the original U-Net and bridged nets have recorded a segmentation accuracy of ~85%. Although applying extra deeply supervised layers and weighted compound of dice coefficient and centroid loss functions solved the flipping issue with ~93%, there is still a room for improving the accuracy by applying some image enhancement as pre-processing stage

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

U-Net based deep convolutional neural network models for liver segmentation from CT scan images

Liver segmentation is a critical task for diagnosis, treatment and follow-up processes of liver cancer. Computed Tomography (CT) scans are the common medical image modality for the segmentation task. Liver segmentation is considered a very hard task for many reasons. Medical images are limited for researchers. Liver shape is changing based on the patient position during the CT scan process, and varies from patient to another based on the health conditions. Liver and other organs, for example heart, stomach, and pancreas, share similar gray scale range in CT images. Liver treatment using surgery operations is very critical because liver contains significant amount of blood and the position of liver is very close to critical organs like heart, lungs, stomach, and crucial blood veins. Therefore the accuracy of segmentation is critical to define liver and tumors shape and position especially when the treatment surgery conducted using radio frequency heating or cryoablation needles. In the literature, convolutional neural networks (CNN) have achieved very high accuracy on liver segmentation and the U-Net model is considered the state-of-the-art for the medical image segmentation task. Many researchers have developed CNN models based on U-Net and stacked U-Nets with/without bridged connections. However, CNN models need significant number of labeled samples for training and validation which is not commonly available in the case of liver CT images. The process of generating manual annotated masks for the training samples are time consuming and need involvement of expert clinical doctors. Data augmentation has thus been widely used in boosting the sample size for model training. Using rotation with steps of 15o and horizontal and vertical flipping as augmentation techniques, the lack of dataset and training samples issue is solved. The choice of rotation and flipping because in the real life situations, most of the CT scans recorded while the while patient lies on face down or with 45o, 60o,90o on right side according to the location of the tumor. Nonetheless, such process has brought up a new issue for liver segmentation. For example, due to the augmentation operations of rotation and flipping, the trained model detected part of the heart as a liver when it is on the wrong side of the body. The first part of this research conducted an extensive experimental study of U-Net based model in terms of deeper and wider, and variant bridging and skip-connections in order to give recommendation for using U-Net based models. Top-down and bottom-up approaches were used to construct variations of deeper models, whilst two, three, and four stacked U-Nets were applied to construct the wider U-Net models. The variation of the skip connections between two and three U-Nets are the key factors in the study. The proposed model used 2 bridged U-Nets with three extra skip connections between the U-Nets to overcome the flipping issue. A new loss function based on minimizing the distance between the center of mass between the predicted blobs has also enhanced the liver segmentation accuracy. Finally, the deep-supervision concept was integrated with the new loss functions where the total loss was calculated as the sum of weighted loss functions over each weighted deeply supervision. It has achieved a segmentation accuracy of up to 90%. The proposed model of 2 bridged U-Nets with compound skip-connections and specific number of levels, layers, filters, and image size has increased the accuracy of liver segmentation to ~90% whereas the original U-Net and bridged nets have recorded a segmentation accuracy of ~85%. Although applying extra deeply supervised layers and weighted compound of dice coefficient and centroid loss functions solved the flipping issue with ~93%, there is still a room for improving the accuracy by applying some image enhancement as pre-processing stage

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)