PCA based clustering for brain tumor segmentation of T1w MRI images

Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images

Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate brain MRI scans and categorize them into four types-pituitary, meningioma, glioma, and normal-this study investigates a potent artificial intelligence (AI) technique. Even though AI has been utilized in the past to detect brain tumors, current techniques still have issues with accuracy and dependability. Our study presents a novel AI technique that combines two distinct deep learning models to enhance this. When combined, these models improve accuracy and yield more trustworthy outcomes than when used separately. Key performance metrics including accuracy, precision, and dependability are used to assess the system once it has been trained using MRI scan pictures. Our results show that this combined AI approach works better than individual models, particularly in identifying different types of brain tumors. Specifically, the InceptionV3 + Xception combination hit an accuracy level of 98.50% in training and 98.30% in validation. Such results further argue the potential application for advanced AI techniques in medical imaging while speaking even more strongly to the fact that multiple AI models used concurrently are able to enhance brain tumor detection.

Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing

Purpose

Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health.

Methods

This system employs a methodology that calculates distances within multidimensional spaces and integrates cost functions to obtain personalized optimisation of recommendations. It also incorporates a classification system for determining the intensity levels of the clinical interventions.

Results

The recommendation system showed high efficiency in identifying and visualizing individuals at high risk of early vascular ageing among healthy patients. Additionally, the system corroborated its consistency and reliability in generating personalized recommendations among different levels of granularity, emphasizing its focus on moderate or low-intensity recommendations, which could improve patient adherence to the intervention.

Conclusion

This tool might significantly aid healthcare professionals in their daily analysis, improving the prevention and management of cardiovascular diseases.

Machine learning and deep learning for brain tumor MRI image segmentation

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.

An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering

Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.

WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection

This study presents wrapper-based metaheuristic deep learning networks (WBM-DLNets) feature optimization algorithms for brain tumor diagnosis using magnetic resonance imaging. Herein, 16 pretrained deep learning networks are used to compute the features. Eight metaheuristic optimization algorithms, namely, the marine predator algorithm, atom search optimization algorithm (ASOA), Harris hawks optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, grey wolf optimization algorithm (GWOA), bat algorithm, and firefly algorithm, are used to evaluate the classification performance using a support vector machine (SVM)-based cost function. A deep-learning network selection approach is applied to determine the best deep-learning network. Finally, all deep features of the best deep learning networks are concatenated to train the SVM model. The proposed WBM-DLNets approach is validated based on an available online dataset. The results reveal that the classification accuracy is significantly improved by utilizing the features selected using WBM-DLNets relative to those obtained using the full set of deep features. DenseNet-201-GWOA and EfficientNet-b0-ASOA yield the best results, with a classification accuracy of 95.7%. Additionally, the results of the WBM-DLNets approach are compared with those reported in the literature.

High-resolution knee plain radiography image synthesis using style generative adversarial network adaptive discriminator augmentation

In this retrospective study, 10,000 anteroposterior (AP) radiography of the knee from a single institution was used to create medical data set that are more balanced and cheaper to create. Two types of convolutional networks were used, deep convolutional GAN (DCGAN) and Style GAN Adaptive Discriminator Augmentation (StyleGAN2-ADA). To verify the quality of generated images from StyleGAN2-ADA compared to real ones, the Visual Turing test was conducted by two computer vision experts, two orthopedic surgeons, and a musculoskeletal radiologist. For quantitative analysis, the Fréchet inception distance (FID), and principal component analysis (PCA) were used. Generated images reproduced the features of osteophytes, joint space narrowing, and sclerosis. Classification accuracy of the experts was 34%, 43%, 44%, 57%, and 50%. FID between the generated images and real ones was 2.96, which is significantly smaller than another medical data set (BreCaHAD = 15.1). PCA showed that no significant difference existed between the PCs of the real and generated images (p > 0.05). At least 2000 images were required to make reliable images optimally. By performing PCA in latent space, we were able to control the desired PC that show a progression of arthritis. Using a GAN, we were able to generate knee X-ray images that accurately reflected the characteristics of the arthritis progression stage, which neither human experts nor artificial intelligence could discern apart from the real images. In summary, our research opens up the potential to adopt a generative model to synthesize realistic anonymous images that can also solve data scarcity and class inequalities.

Seg Net and Salp Water Optimization-driven Deep Belief network for segmentation and classification of brain tumor

Classification of brain tumor in Magnetic Resonance Imaging (MRI) images is highly popular in treatment planning, early diagnosis, and outcome evaluation. It is very difficult for classifying and diagnosing tumors from several images. Thus, an automatic prediction strategy is essential in classifying brain tumors as malignant, core, edema, or benign. In this research, a novel approach using Salp Water Optimization-based Deep Belief network (SWO-based DBN) is introduced to classify brain tumor. At the initial stage, the input image is pre-processed to eradicate the artifacts present in input image. Following pre-processing, the segmentation is executed by SegNet, where the SegNet is trained using the proposed SWO. Moreover, the Convolutional Neural Network (CNN) features are employed to mine the features for future processing. At last, the introduced SWO-based DBN technique efficiently categorizes the brain tumor with respect to the extracted features. Thereafter, the produced output of the introduced SegNet + SWO-based DBN is made use of in brain tumor segmentation and classification. The developed technique produced better results with highest values of accuracy at 0.933, specificity at 0.880, and sensitivity at 0.938 using BRATS, 2018 datasets and accuracy at 0.921, specificity at 0.853, and sensitivity at 0.928 for BRATS, 2020 dataset.

Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C-Means Algorithm

This study was aimed to explore the diagnostic value of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in brain tumors under the fuzzy C-means (FCM) algorithm. The two-dimensional FCM hybrid algorithm was improved to be three-dimensional. The MRI images and MRS spectra of 127 patients with brain tumors (low-grade glioma group) and 54 healthy people (healthy group) were analyzed. The results suggested that the membership matrix of the improved algorithm had lower ambiguity, higher segmentation accuracy, closer relationship of intrapixels, and stronger irrelevance of interclass pixels. Through the analysis of gray matter volume, it was found that, compared with the healthy group, the gray matter and white matter volumes in the brain of high-grade glioma were higher, and those of low-grade glioma group were lower. The improved FCM algorithm could obtain a higher accuracy of 88.64% in segmenting images. It had a higher sensitivity to gray matter changes in brain tumors, reaching 92.72%; its specificity was not much different from that of traditional FCM, which were 83.61% and 88.06%, respectively. In the diagnostic value, the area under the curve of mean kurtosis was the largest, which was 0.962 (P < 0.001). The best critical value was 0.4096, which had a greater reference significance for clinical treatment and prognosis. The ratio of choline/N-acetyl-aspartate and the ratio of choline/creatine also showed significant differences in high- and low-grade gliomas (P < 0.05), but the specificity and sensitivity were slightly lower. It also had guiding significance for the grading of gliomas. Overall, the improved FCM algorithm had obvious advantages in the segmentation process of MRI images, which provided help for the clinical diagnosis of brain tumors.

Combining Radiology and Pathology for Automatic Glioma Classification

Subtype classification is critical in the treatment of gliomas because different subtypes lead to different treatment options and postoperative care. Although many radiological- or histological-based glioma classification algorithms have been developed, most of them focus on single-modality data. In this paper, we propose an innovative two-stage model to classify gliomas into three subtypes (i.e., glioblastoma, oligodendroglioma, and astrocytoma) based on radiology and histology data. In the first stage, our model classifies each image as having glioblastoma or not. Based on the obtained non-glioblastoma images, the second stage aims to accurately distinguish astrocytoma and oligodendroglioma. The radiological images and histological images pass through the two-stage design with 3D and 2D models, respectively. Then, an ensemble classification network is designed to automatically integrate the features of the two modalities. We have verified our method by participating in the MICCAI 2020 CPM-RadPath Challenge and won 1st place. Our proposed model achieves high performance on the validation set with a balanced accuracy of 0.889, Cohen’s Kappa of 0.903, and an F1-score of 0.943. Our model could advance multimodal-based glioma research and provide assistance to pathologists and neurologists in diagnosing glioma subtypes. The code has been publicly available online at https://github.com/Xiyue-Wang/1st-in-MICCAI2020-CPM.

A Hybrid Deep Learning-Based Approach for Brain Tumor Classification

Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images.

An Operative Acute Brain Tumor Recognition by Jointure Inward Unswerving Probabilistic Neural Network Classifier

Brain tumors have to be predicted earlier to avoid the risk of being mortal. For an effective detection an adaptive segmentation with two-tier tumors region extraction is needed. This framework offers preprocessing to avoid noise occurrence by fusing median and wiener filter also utilizes adaptive pillar C-means algorithm for obtaining the essential feature set thus the processing time is reduced. Thus the attained essential feature sets are then classified by means of unswerving PNN (Probabilistic Neural network) classifier where classification is done twice initially to classify whether benign or malignant, Sub sequently to classify different sorts of brain tumor such as Astrocytoma, Meningioma, Glioblastoma and Medulloblastoma. Since the non-linearity of PNN due to distance factor consumes more computation time which is tackled by intruding the radial basis function resulted in LS-SVM (Least Square-Support Vector Machine) as a distance factor which is linear one. Thus computation time is further reduced.

Analysis of Brain MRI Images Using Improved CornerNet Approach

The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.

Altered transcriptome and disease-related phenotype emerge only after fibroblasts harvested from patients with age-related macular degeneration are differentiated into retinal pigment epithelium

We have reported previously that retinal pigment epithelium (RPE) differentiated from induced pluripotent stem cells (iPSC) generated from fibroblasts of patients with age-related macular degeneration (AMD) exhibit a retinal degenerative disease phenotype and a distinct transcriptome compared to age-matched controls. Since the genetic composition of the iPSC and RPE are inherited from fibroblasts, we investigated whether differential behavior was present in the parental fibroblasts and iPSC prior to differentiation of the cell lines into RPE. Principal component analyses revealed significant overlap (essentially no differences) in the transcriptome of fibroblasts between AMD and controls. After reprogramming, there was no significant difference in the transcriptome of iPSC generated from AMD versus normal donors. In contrast, the transcriptome of RPE derived from iPSC segregated into two distinct clusters of AMD-derived cells versus controls. Interestingly, mitochondrial dysfunction in AMD-derived RPE was evident after approximately two months in culture. Moreover, these differences in mitochondrial dysfunction were not evident in the parental fibroblasts and iPSC. This study demonstrates an altered transcriptome and impaired mitochondrial function in RPE derived from AMD patients versus controls, and demonstrates these differences are not present in the original fibroblasts or iPSC. These results suggest that pathology in AMD is triggered upon differentiation of parent cells into RPE. More study of this phenomenon could advance the current understandings of the etiology of AMD and the development of novel therapeutic targets.

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.

Brain Tumor Classification & Segmentation by Using Advanced DNN, CNN & ResNet-50 Neural Networks

In the medical domain, brain image classification is an extremely challenging field. Medical images play a vital role in making the doctor's precise diagnosis and in the surgery process. Adopting intelligent algorithms makes it feasible to detect the lesions of medical images quickly, and it is especially necessary to extract features from medical images. Several studies have integrated multiple algorithms toward medical images domain. Concerning feature extraction from the medical image, a vast amount of data is analyzed to achieve processing results, helping physicians deliver more precise case diagnoses. Image processing mechanism becomes extensive usage in medical science to advance the early detection and treatment aspects. In this aspect, this paper takes tumor, and healthy images as the research object and primarily performs image processing and data augmentation process to feed the dataset to the neural networks. Deep neural networks (DNN), to date, have shown outstanding achievement in classification and segmentation tasks. Carrying this concept into consideration, in this study, we adopted a pre-trained model Resnet_50 for image analysis. The paper proposed three diverse neural networks, particularly DNN, CNN, and ResNet-50. Finally, the splitting dataset is individually assigned to each simplified neural network. Once the image is classified as a tumor accurately, the OTSU segmentation is employed to extract the tumor alone. It can be examined from the experimental outcomes that the ResNet-50 algorithm shows high accuracy 0.996, precision 1.00 with best F1 score 1.0, and minimum test losses of 0.0269 in terms of Brain tumor classification. Extensive experiments prove our offered tumor detection segmentation efficiency and accuracy. To this end, our approach is comprehensive sufficient and only requires minimum pre-and post-processing, which allows its adoption in various medical image classification & segmentation tasks.

Probabilistic Unsupervised Machine Learning Approach for a Similar Image Recommender System for E-Commerce

The recommender system is the most profound research area for e-commerce product recommendations. Currently, many e-commerce platforms use a text-based product search, which has limitations to fetch the most similar products. An image-based similarity search for recommendations had considerable gains in popularity for many areas, especially for the e-commerce platforms giving a better visual search experience by the users. In our research work, we proposed a machine-learning-based approach for a similar image-based recommender system. We applied a dimensionality reduction technique using Principal Component Analysis (PCA) through Singular Value Decomposition (SVD) for transforming the extracted features into lower-dimensional space. Further, we applied the K-Means++ clustering approach for the possible cluster identification for a similar group of products. Later, we computed the Manhattan distance measure for the input image to the target clusters set for fetching the top-N similar products with low distance measure. We compared our approach with five different unsupervised clustering algorithms, namely Minibatch, K-Mediod, Agglomerative, Brich, and the Gaussian Mixture Model (GMM), and used the 40,000 fashion product image dataset from the Kaggle web platform for the product recommendation process. We computed various cluster performance metrics on K-means++ and achieved a Silhouette Coefficient (SC) of 0.1414, a Calinski-Harabasz (CH) index score of 669.4, and a Davies–Bouldin (DB) index score of 1.8538. Finally, our proposed PCA-SVD transformed K-mean++ approach showed superior performance compared to the other five clustering approaches for similar image product recommendations.

A health informatics transformation model based on intelligent cloud computing - exemplified by type 2 diabetes mellitus with related cardiovascular diseases

BACKGROUND AND OBJECTIVE

Many studies regarding health analysis request structured datasets but the legacy resources provide scattered data. This study aims to establish a health informatics transformation model (HITM) based upon intelligent cloud computing with the self-developed analytics modules by open source technique. The model was exemplified by the open data of type 2 diabetes mellitus (DM2) with related cardiovascular diseases.

METHODS

The Apache-SPARK framework was employed to generate the infrastructure of the HITM, which enables the machine learning (ML) algorithms including random forest, multi-layer perceptron classifier, support vector machine, and naïve Bayes classifier as well as the regression analysis for intelligent cloud computing. The modeling applied the MIMIC-III open database as an example to design the health informatics data warehouse, which embeds the PL/SQL-based modules to extract the analytical data for the training processes. A coupling analysis flow can drive the ML modules to train the sample data and validate the results.

RESULTS

The four modes of cloud computation were compared to evaluate the feasibility of the cloud platform in accordance with its system performance for more than 11,500 datasets. Then, the modeling adaptability was validated by simulating the featured datasets of obesity and cardiovascular-related diseases for patients with DM2 and its complications. The results showed that the run-time efficiency of the platform performed in around one minute and the prediction accuracy of the featured datasets reached 90%.

CONCLUSIONS

This study helped contribute the modeling for efficient transformation of health informatics. The HITM can be customized for the actual clinical database, which provides big data for training, with the proper ML modules for a predictable process in the cloud platform. The feedback of intelligent computing can be referred to risk assessment in health promotion.

Patient clustering using dynamic partitioning on correlated and uncertain biomedical data

Background and objectivesHealth professionals look for specific patterns by correlating multiple physiological data in the process of deciding treatments to remedy clinical abnormalities. Biomedical data exhibit some common patterns in the event of identical clinical illnesses. The primary interest of this work is automatic discovery of such patterns in vital sign data (e.g. heart rate, blood pressure) using unsupervised learning and utilising them to identify patients with similar clinical conditions. MethodsA patient clustering method is developed that efficiently isolates patients into multiple groups by discovering dynamic patterns in multi-dimensional vital sign data. A dynamic partitioning algorithm and a patient clustering approach is proposed by introducing a measure namely aggregated instance-wise uncertainty (AIU) computed from multi-dimensional physiological time-series data. ResultsThe developed model is evaluated qualitatively using principal component analysis and silhouette value; and quantitatively in terms of its ability of clustering patients associated with different clinical situations. Experiments are conducted using real-world biomedical data of patients having various clinical conditions. Thee observed accuracy was 82.85% and 91.17% on two experimental datasets comprised of 35 and 34 patients data respectively.The comparisons show that the proposed approached outperformed than other methods in state-of-the-art approach. ConclusionsThe experimental outcomes demonstrate the effectiveness of the proposed approach in discovering distinct patterns with predictive significance.

Study of Tissue Variation and Analysis of MR Brain Images using Optimized Multilevel Threshold and Deep CNN Features in Neurodegenerative Disorders

Dementia is a degenerative irreversible disorder that globally causes a high socio-economic burden. The pathology progression of mild cognitive impairment (MCI) and Alzheimer diseases (AD) are correlated with each other. There is a need to examine the pathology variation to discriminate the disorder to provide appropriate treatment strategies. This study investigates about the brain tissue variations to identify the subtle change in progression. The considered normal, MCI and AD magnetic resonance (MR) images are obtained from Alzheimer's disease Neuroimaging Initiative (ADNI). In this work, multilevel Tsallis based grey wolf optimization (GWO) is used to segment the brain tissues. Then the feature is extracted from segmented white matter (WM), grey matter (GM) and cerebro spinal fluid (CSF) using convolution neural network (CNN). The obtained deep features are given to principal component analysis (PCA) to obtain a prominent feature set for normal, MCI and AD. Further the tissue variation of optimized deep features is analyzed using support vector machine (SVM). The results shows that Tsallis based GWO perform reliable tissue segmentation for normal, MCI and AD. The deep features are able to observe discrimination than the fully considered feature set. Finally, the classifier result shows distinct tissue variation among normal, MCI and AD subjects. Further the prominent features give a classification accuracy of 77%, 80.22% and 78.7% for WM, GM and CSF respectively. This concludes that GM variation is a close biological substrate of dementia progressive condition than the effects of time or aging. Thus, the proposed framework can be used as an effective system for diagnosis of progression in neurodegenerative disorders.

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-means clustering. The histogram method was utilized to identify the number of clusters to avoid random initialization. Different types of features such as gray level co-occurrence matrix (GLCM), color, and shape-based features were extracted from the segmented image of the nucleus. Machine learning prediction algorithms were applied to classify the cancerous and non-cancerous cells. The proposed strategy is compared with an existing clustering algorithm, and the efficiency is evaluated based on the prediction metrics. The experimental results show that the HSCRKM method efficiently segments the nucleus, and it is also inferred that logistic regression and neural network perform better than other prediction algorithms.

AUTOMATION OF MR BRAIN IMAGE CLASSIFICATION FOR MALIGNANCY DETECTION

Magnetic resonance imaging (MRI) plays an integral role among the advanced techniques for detecting a brain tumor. The early detection of brain tumor with proper automation algorithm results in assisting oncologists to make easy decisions for diagnostic purposes. This paper presents an automatic classification of MR brain images in normal and malignant conditions. The feature extraction is done with gray-level co-occurrence matrix, and we proposed a feature reduction technique based on statistical test which is preceded by principal component analysis (PCA). The main focus of the work is to establish the statistical significance of the features obtained after PCA, thereby selecting significant feature values for subsequent classification. For that, a [Formula: see text]-test is performed which yielded a [Formula: see text]-value of 0.05. Finally, a comparative study using [Formula: see text]-nearest neighbor (kNN), support vector machine and artificial neural network (ANN)-based supervised classifiers is performed. In this work, we could achieve reasonably good sensitivity, specificity and accuracy for all the classifiers. The ANN classifier gives better performance with sensitivity of 97.33%, specificity of 97.42% and accuracy of 98.66% on the whole brain atlas database. The experimental results obtained are comparable to the other recent state-of-the-art.

In-depth comparative analysis of Illumina® MiSeq run metrics: Development of a wet-lab quality assessment tool

Whole genome sequencing of bacterial isolates has become a daily task in many laboratories, generating incredible amounts of data. However, data acquisition is not an end in itself; the goal is to acquire high-quality data useful for understanding genetic relationships. Having a method that could rapidly determine which of the many available run metrics are the most important indicators of overall run quality and having a way to monitor these during a given sequencing run would be extremely helpful to this effect. Therefore, we compared various run metrics across 486 MiSeq runs, from five different machines. By performing a statistical analysis using principal components analysis and a K-means clustering algorithm of the metrics, we were able to validate metric comparisons among instruments, allowing for the development of a predictive algorithm, which permits one to observe whether a given MiSeq run has performed adequately. This algorithm is available in an Excel spreadsheet: that is, MiSeq Instrument & Run (In-Run) Forecast. Our tool can help verify that the quantity/quality of the generated sequencing data consistently meets or exceeds recommended manufacturer expectations. Patterns of deviation from those expectations can be used to assess potential run problems and plan preventative maintenance, which can save valuable time and funding resources.

A Hierarchical Multivariate Curve Resolution Methodology To Identify and Map Compounds in Spectral Images

The use of spectroscopic methods, such as near-infrared or Raman, for quality control applications combined with the constant search for finer details leads to the acquisition of increasingly complex data sets. This should not prevent the user from characterizing a sample by identifying and mapping its chemical compounds. Multivariate data analysis methods make it possible to obtain qualitative and quantitative information from such data sets. However, samples containing a large (and/or unknown) number of species, segregated trace compounds (present in few pixels), low signal-to-noise ratios (SNR), and often insufficient spatial resolutions still represent significant hurdles for the analyst.

A fast segmentation-free fully automated approach to white matter injury detection in preterm infants

White matter injury (WMI) is the most prevalent brain injury in the preterm neonate leading to developmental deficits. However, detecting WMI in magnetic resonance (MR) images of preterm neonate brains using traditional WM segmentation-based methods is difficult mainly due to lack of reliable preterm neonate brain atlases to guide segmentation. Hence, we propose a segmentation-free, fast, unsupervised, atlas-free WMI detection method. We detect the ventricles as blobs using a fast linear maximally stable extremal regions algorithm. A reference contour equidistant from the blobs and the brain-background boundary is used to identify tissue adjacent to the blobs. Assuming normal distribution of the gray-value intensity of this tissue, the outlier intensities in the entire brain region are identified as potential WMI candidates. Thereafter, false positives are discriminated using appropriate heuristics. Experiments using an expert-annotated dataset show that the proposed method runs 20 times faster than our earlier work which relied on time-consuming segmentation of the WM region, without compromising WMI detection accuracy. Graphical Abstract Key Steps of Segmentation-free WMI Detection.

Brain tumor segmentation with Vander Lugt correlator based active contour

BACKGROUND AND OBJECTIVE

The manual segmentation of brain tumors from medical images is an error-prone, sensitive, and time-absorbing process. This paper presents an automatic and fast method of brain tumor segmentation.

METHODS

In the proposed method, a numerical simulation of the optical Vander Lugt correlator is used for automatically detecting the abnormal tissue region. The tumor filter, used in the simulated optical correlation, is tailored to all the brain tumor types and especially to the Glioblastoma, which considered to be the most aggressive cancer. The simulated optical correlation, computed between Magnetic Resonance Images (MRI) and this filter, estimates precisely and automatically the initial contour inside the tumorous tissue. Further, in the segmentation part, the detected initial contour is used to define an active contour model and presenting the problematic as an energy minimization problem. As a result, this initial contour assists the algorithm to evolve an active contour model towards the exact tumor boundaries. Equally important, for a comparison purposes, we considered different active contour models and investigated their impact on the performance of the segmentation task. Several images from BRATS database with tumors anywhere in images and having different sizes, contrast, and shape, are used to test the proposed system. Furthermore, several performance metrics are computed to present an aggregate overview of the proposed method advantages.

RESULTS

The proposed method achieves a high accuracy in detecting the tumorous tissue by a parameter returned by the simulated optical correlation. In addition, the proposed method yields better performance compared to the active contour based methods with the averages of Sensitivity=0.9733, Dice coefficient = 0.9663, Hausdroff distance = 2.6540, Specificity = 0.9994, and faster with a computational time average of 0.4119 s per image.

CONCLUSIONS

Results reported on BRATS database reveal that our proposed system improves over the recently published state-of-the-art methods in brain tumor detection and segmentation.

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil

Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method.