A full migration BBO algorithm with enhanced population quality bounds for multimodal biomedical image registration

Deep Learning of radiology-genomics integration for computational oncology: A mini review

In the field of computational oncology, patient status is often assessed using radiology-genomics, which includes two key technologies and data, such as radiology and genomics. Recent advances in deep learning have facilitated the integration of radiology-genomics data, and even new omics data, significantly improving the robustness and accuracy of clinical predictions. These factors are driving artificial intelligence (AI) closer to practical clinical applications. In particular, deep learning models are crucial in identifying new radiology-genomics biomarkers and therapeutic targets, supported by explainable AI (xAI) methods. This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research directions for multimodal integration and biomarker discovery of radiology-genomics or radiology-omics that are urgently needed in computational oncology.

Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration

In this paper, a weighted multivariate generalized Gaussian mixture model combined with stochastic optimization is proposed for point cloud registration. The mixture model parameters of the target scene and the scene to be registered are updated iteratively by the fixed point method under the framework of the EM algorithm, and the number of components is determined based on the minimum message length criterion (MML). The KL divergence between these two mixture models is utilized as the loss function for stochastic optimization to find the optimal parameters of the transformation model. The self-built point clouds are used to evaluate the performance of the proposed algorithm on rigid registration. Experiments demonstrate that the algorithm dramatically reduces the impact of noise and outliers and effectively extracts the key features of the data-intensive regions.

Normal vibration distribution search-based differential evolution algorithm for multimodal biomedical image registration

In linear registration, a floating image is spatially aligned with a reference image after performing a series of linear metric transformations. Additionally, linear registration is mainly considered a preprocessing version of nonrigid registration. To better accomplish the task of finding the optimal transformation in pairwise intensity-based medical image registration, in this work, we present an optimization algorithm called the normal vibration distribution search-based differential evolution algorithm (NVSA), which is modified from the Bernstein search-based differential evolution (BSD) algorithm. We redesign the search pattern of the BSD algorithm and import several control parameters as part of the fine-tuning process to reduce the difficulty of the algorithm. In this study, 23 classic optimization functions and 16 real-world patients (resulting in 41 multimodal registration scenarios) are used in experiments performed to statistically investigate the problem solving ability of the NVSA. Nine metaheuristic algorithms are used in the conducted experiments. When compared to the commonly utilized registration methods, such as ANTS, Elastix, and FSL, our method achieves better registration performance on the RIRE dataset. Moreover, we prove that our method can perform well with or without its initial spatial transformation in terms of different evaluation indicators, demonstrating its versatility and robustness for various clinical needs and applications. This study establishes the idea that metaheuristic-based methods can better accomplish linear registration tasks than the frequently used approaches; the proposed method demonstrates promise that it can solve real-world clinical and service problems encountered during nonrigid registration as a preprocessing approach.The source code of the NVSA is publicly available at https://github.com/PengGui-N/NVSA.

Evolutionary Image Registration: A Review

Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of state-of-the-art works reporting evolutionary-based registration methods. The papers were selected using the PRISMA 2020 method. The reported algorithms are reviewed and compared in terms of evolutionary components, fitness function, image similarity measures and algorithm accuracy indexes used in the alignment process.

A Technology Watch/Competitive Intelligence–based Decision-Support System optimised with Genetic Algorithms

To survive and prosper in a highly competitive environment where uncertainty and ambiguity are the norm, today’s firms are faced with the need for new information management methods and tools. Two of the most prominent strategies that take information and its treatment as a value-generating element in firms’ decision-making are Technology Watch and Competitive Intelligence. In addition, one of the fundamental components that a system based on these strategies must have is an efficient method of Information Retrieval. The present study describes a Competitive Intelligence–based decision-support system that uses a Genetic Algorithm. The system contributes to improving information retrieval through search optimisation, thus enhancing the performance of this knowledge-generating tool for organisations.

Harris hawks optimization based on global cross-variation and tent mapping

Harris hawks optimization (HHO) is a new meta-heuristic algorithm that builds a model by imitating the predation process of Harris hawks. In order to solve the problems of poor convergence speed caused by uniform choice position update formula in the exploration stage of basic HHO and falling into local optimization caused by insufficient population richness in the later stage of the algorithm, a Harris hawks optimization based on global cross-variation and tent mapping (CRTHHO) is proposed in this paper. Firstly, the tent mapping is introduced in the exploration stage to optimize random parameter q to speed up the convergence in the early stage. Secondly, the crossover mutation operator is introduced to cross and mutate the global optimal position in each iteration process. The greedy strategy is used to select, which prevents the algorithm from falling into local optimal because of skipping the optimal solution and improves the convergence accuracy of the algorithm. In order to investigate the performance of CRTHHO, experiments are carried out on ten benchmark functions and the CEC2017 test set. Experimental results show that the CRTHHO algorithm performs better than the HHO algorithm and is competitive with five advanced meta-heuristic algorithms.

Sequential safe feature elimination rule for L1-regularized regression with Kullback-Leibler divergence

The L₁-regularized regression with Kullback-Leibler divergence (KL-L₁R) is a popular regression technique. Although many efforts have been devoted to its efficient implementation, it remains challenging when the number of features is extremely large. In this paper, to accelerate KL-L₁R, we introduce a novel and fast sequential safe feature elimination rule (FER) based on its sparsity, local regularity properties, and duality theory. It takes negligible time to select and delete most redundant features before and during the training process. Only one reduced model needs to be solved, which makes the computational time shortened. To further speed up the reduced model, the Newton coordinate descent method (Newton-CDM) is chosen as a solver. The superiority of FER is safety, i.e., its solution is exactly the same as the original KL-L₁R. Numerical experiments on three artificial datasets, five real-world datasets, and one handwritten digit dataset demonstrate the feasibility and validity of our FER.

Mining sequential patterns with flexible constraints from MOOC data

Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising approach is to apply sequential pattern mining (SPM) to discover useful knowledge in these data. In this paper, mining sequential patterns (SPs) with flexible constraints in MOOC enrollment data is proposed, which follows that research approach. Three constraints are proposed: the length constraint, discreteness constraint, and validity constraint. They are used to describe the effect of the length of enrollment sequences, variance of enrollment dates, and enrollment moments, respectively. To improve the mining efficiency, the three constraints are pushed into the support, which is the most typical parameter in SPM, to form a new parameter called support with flexible constraints (SFC). SFC is proved to satisfy the downward closure property, and two algorithms are proposed to discover SPs with flexible constraints. They traverse the search space in a breadth-first and depth-first manner. The experimental results demonstrate that the proposed algorithms effectively reduce the number of patterns, with comparable performance to classical SPM algorithms.

Symmetry-based representation for registration of multimodal images

We propose a new two-dimensional structural representation method for registration of multimodal images by using the local structural symmetry of images, which is similar at different modalities. The symmetry is measured in various orientations and the best is mapped and used for the representation image. The optimum performance is obtained when using only two different orientations, which is called binary dominant symmetry representation (BDSR). This representation is highly robust to noise and intensity non-uniformity. We also propose a new objective function based on L2 distance with low sensitivity to the overlapping region. Then, five different meta-heuristic algorithms are comparatively applied. Two of them have been used for the first time on image registration. BDSR remarkably outperforms the previous successful representations, such as entropy images, self-similarity context, and modality-independent local binary pattern, as well as mutual information-based registration, in terms of success rate, runtime, convergence error, and representation construction.

A holistic overview of deep learning approach in medical imaging

Medical images are a rich source of invaluable necessary information used by clinicians. Recent technologies have introduced many advancements for exploiting the most of this information and use it to generate better analysis. Deep learning (DL) techniques have been empowered in medical images analysis using computer-assisted imaging contexts and presenting a lot of solutions and improvements while analyzing these images by radiologists and other specialists. In this paper, we present a survey of DL techniques used for variety of tasks along with the different medical image's modalities to provide critical review of the recent developments in this direction. We have organized our paper to provide significant contribution of deep leaning traits and learn its concepts, which is in turn helpful for non-expert in medical society. Then, we present several applications of deep learning (e.g., segmentation, classification, detection, etc.) which are commonly used for clinical purposes for different anatomical site, and we also present the main key terms for DL attributes like basic architecture, data augmentation, transfer learning, and feature selection methods. Medical images as inputs to deep learning architectures will be the mainstream in the coming years, and novel DL techniques are predicted to be the core of medical images analysis. We conclude our paper by addressing some research challenges and the suggested solutions for them found in literature, and also future promises and directions for further developments.

SSK-DDoS: distributed stream processing framework based classification system for DDoS attacks

Distributed denial of service (DDoS) is an immense threat for Internet based-applications and their resources. It immediately floods the victim system by transmitting a large number of network packets, and due to this, the victim system resources become unavailable for legitimate users. Therefore, this attack is claimed to be a dangerous attack for Internet-based applications and their resources. Several security approaches have been proposed in the literature to protect Internet-based applications from this type of threat. However, the frequency and strength of DDoS attacks are increasing day-by-day. Further, most of the traditional and distributed processing frameworks-based DDoS attack detection systems analyzed network flows in offline batch processing. Hence, they failed to classify network flows in real-time. This paper proposes a novel Spark Streaming and Kafka-based distributed classification system, named by SSK-DDoS, for classifying different types of DDoS attacks and legitimate network flows. This classification approach is implemented using a distributed Spark MLlib machine learning algorithms on a Hadoop cluster and deployed on the Spark streaming platform to classify streams in real-time. The incoming streams consume by Kafka's topic to perform preprocessing tasks such as extracting and formulating features for classifying them into seven groups: Benign, DDoS-DNS, DDoS-LDAP, DDoS-MSSQL, DDoS-NetBIOS, DDoS-UDP, and DDoS-SYN. Further, the SSK-DDoS classification system stores formulated features with their predicted class into the HDFS that will help to retrain the distributed classification approach using a new set of samples. The proposed SSK-DDoS classification system has been validated using the recent CICDDoS2019 dataset. The results show that the proposed SSK-DDoS efficiently classified network flows into seven classes and stored formulated features with the predicted value of each incoming network flow into HDFS.

Registration of computed tomography images of a lung infected with COVID-19 based in the new meta-heuristic algorithm HPSGWO

Computed tomography (CT) helps the radiologist in the rapid and correct detection of a person infected with the coronavirus disease 2019 (COVID-19), and this by showing the presence of the ground-glass opacity in the lung of with the virus. Tracking the evolution of the spread of the ground-glass opacity (GGO) in the lung of the person infected with the virus needs to study more than one image in different times. The various CT images must be registration to identify the evolution of the ground glass in the lung and to facilitate the study and identification of the virus. Due to the process of registration images is essentially an improvement problem, we present in this paper a new HPSGWO algorithm for registration CT images of a lung infected with the COVID-19. This algorithm is a hybridization of the two algorithms Particle swarm optimization (PSO) and Grey wolf optimizer (GWO). The simulation results obtained after applying the algorithm to the test images show that the proposed approach achieved high-precision and robust registration compared to other methods such as GWO, PSO, Firefly Algorithm (FA), and Crow Searcha Algorithms (CSA).

Enhanced Tunicate Swarm Algorithm for Solving Large-Scale Nonlinear Optimization Problems

Nowadays optimization problems become difficult and complex, traditional methods become inefficient to reach global optimal solutions. Meanwhile, a huge number of meta-heuristic algorithms have been suggested to overcome the shortcomings of traditional methods. Tunicate Swarm Algorithm (TSA) is a new biologically inspired meta-heuristic optimization algorithm which mimics jet propulsion and swarm intelligence during the searching for a food source. In this paper, we suggested an enhancement to TSA, named Enhanced Tunicate Swarm Algorithm (ETSA), based on a novel searching strategy to improve the exploration and exploitation abilities. The proposed ETSA is applied to 20 unimodal, multimodal and fixed dimensional benchmark test functions and compared with other algorithms. The statistical measures, error analysis and the Wilcoxon test have affirmed the robustness and effectiveness of the ETSA. Furthermore, the scalability of the ETSA is confirmed using high dimensions and results exhibited that the ETSA is least affected by increasing the dimensions. Additionally, the CPU time of the proposed algorithms are obtained, the ETSA provides less CPU time than the others for most functions. Finally, the proposed algorithm is applied at one of the important electrical applications, Economic Dispatch Problem, and the results affirmed its applicability to deal with practical optimization tasks.

A complementary learning approach for expertise transference of human-optimized controllers

In this paper, a complementary learning scheme for experience transference of unknown continuous-time linear systems is proposed. The algorithm is inspired in the complementary learning properties that exhibit the hippocampus and neocortex learning systems via the striatum. The hippocampus is modelled as pattern-separated data of a human optimized controller. The neocortex is modelled as a Q-reinforcement learning algorithm which improves the hippocampus control policy. The complementary learning (striatum) is designed as an inverse reinforcement learning algorithm which relates the hippocampus and neocortex learning models to seek and transfer the weights of the hidden expert's utility function. Convergence of the proposed approach is analysed using Lyapunov recursions. Simulations are given to verify the proposed approach.

Robust multimodal registration of fluorescein angiography and optical coherence tomography angiography images using evolutionary algorithms

Optical coherence tomography angiography (OCTA) and fluorescein angiography (FA) are two different vascular imaging modalities widely used in clinical practice to diagnose and grade different relevant retinal pathologies. Although each of them has its advantages and disadvantages, the joint analysis of the images produced by both techniques to analyze a specific area of the retina is of increasing interest, given that they provide common and complementary visual information. However, in order to facilitate this analysis task, a previous registration of the pair of FA and OCTA images is desirable in order to superimpose their common areas and focus the gaze on the regions of interest. Normally, this task is manually carried out by the expert clinician, but it turns out to be tedious and time-consuming. Here, we present a three-stage methodology for robust multimodal registration of FA and superficial plexus OCTA images. The first one is a preprocessing stage devoted to reducing the noise and segmenting the main vessels in both types of images. The second stage uses the vessel information to do an approximate registration based on template matching. Lastly, the third stage uses an evolutionary algorithm based on differential evolution to refine the previous registration and obtain the optimal registration. The method was evaluated in a dataset with 172 pairs of FA and OCTA images, obtaining a success rate of 98.8%. The best mean execution time of the method was less than 5 s per image.

The Frequency of Random Findings on Abdominal/Pelvis Computed Tomography in Pediatric Trauma Patients

AIM

In this study, we aimed to find the percentage of random pathologies and abdominopelvic region anomalies that are not related to trauma in pediatric patients.

BACKGROUND

An abdominal assessment of an injured child usually involves computed tomography imaging of the abdomen and pelvis (CTAP) to determine the presence and size of injuries. Imaging may accidentally reveal irrelevant findings.

OBJECTIVE

Although the literature in adults has reviewed the frequency of discovering these random findings, few studies have been identified in the pediatric population.

METHODS

Data on 142 (38 female, 104 male) patients who underwent CTAP during their trauma evaluation between January 2019 and January 2020 were obtained from our level 3 pediatric trauma center records. The records and CTAP images were examined retrospectively for extra traumatic pathologies and anomalies.

RESULTS

67 patients (47%) had 81 incidental findings. There were 17 clinically significant random findings. No potential tumors were found in this population.

CONCLUSION

Pediatric trauma CTAP reveals random findings. For further evaluation, incidental findings should be indicated in the discharge summaries.