Constrained Gaussian mixture model framework for automatic segmentation of MR brain images

Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images

Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise segmentation of brain tissues. Accurate segmentation is crucial for diagnosing brain injuries and neurodegenerative conditions. We introduce an Enhanced Spatial Fuzzy C-means (esFCM) algorithm for 3D T1 MRI segmentation to three tissues, i.e. White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The esFCM employs a weighted least square algorithm utilizing the Structural Similarity Index (SSIM) for polynomial bias field correction. It also takes advantage of the information from the membership function of the last iteration to compute neighborhood impact. This strategic refinement enhances the algorithm's adaptability to complex image structures, effectively addressing challenges such as intensity irregularities and contributing to heightened segmentation accuracy. We compare the segmentation accuracy of esFCM against four variants of FCM, Gaussian Mixture Model (GMM) and FSL and ANTs algorithms using four various dataset, employing three measurement criteria. Comparative assessments underscore esFCM's superior performance, particularly in scenarios involving added noise and bias fields.The obtained results emphasize the significant potential of the proposed method in the segmentation of MRI images.

Deep Generative Adversarial Reinforcement Learning for Semi-Supervised Segmentation of Low-Contrast and Small Objects in Medical Images

Deep reinforcement learning (DRL) has demonstrated impressive performance in medical image segmentation, particularly for low-contrast and small medical objects. However, current DRL-based segmentation methods face limitations due to the optimization of error propagation in two separate stages and the need for a significant amount of labeled data. In this paper, we propose a novel deep generative adversarial reinforcement learning (DGARL) approach that, for the first time, enables end-to-end semi-supervised medical image segmentation in the DRL domain. DGARL ingeniously establishes a pipeline that integrates DRL and generative adversarial networks (GANs) to optimize both detection and segmentation tasks holistically while mutually enhancing each other. Specifically, DGARL introduces two innovative components to facilitate this integration in semi-supervised settings. First, a task-joint GAN with two discriminators links the detection results to the GAN's segmentation performance evaluation, allowing simultaneous joint evaluation and feedback. This ensures that DRL and GAN can be directly optimized based on each other's results. Second, a bidirectional exploration DRL integrates backward exploration and forward exploration to ensure the DRL agent explores the correct direction when forward exploration is disabled due to lack of explicit rewards. This mitigates the issue of unlabeled data being unable to provide rewards and rendering DRL unexplorable. Comprehensive experiments on three generalization datasets, comprising a total of 640 patients, demonstrate that our novel DGARL achieves 85.02% Dice and improves at least 1.91% for brain tumors, achieves 73.18% Dice and improves at least 4.28% for liver tumors, and achieves 70.85% Dice and improves at least 2.73% for pancreas compared to the ten most recent advanced methods, our results attest to the superiority of DGARL. Code is available at GitHub.

Combiner and HyperCombiner networks: Rules to combine multimodality MR images for prostate cancer localisation

One of the distinct characteristics of radiologists reading multiparametric prostate MR scans, using reporting systems like PI-RADS v2.1, is to score individual types of MR modalities, including T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer. This work aims to demonstrate that it is feasible for low-dimensional parametric models to model such decision rules in the proposed Combiner networks, without compromising the accuracy of predicting radiologic labels. First, we demonstrate that either a linear mixture model or a nonlinear stacking model is sufficient to model PI-RADS decision rules for localising prostate cancer. Second, parameters of these combining models are proposed as hyperparameters, weighing independent representations of individual image modalities in the Combiner network training, as opposed to end-to-end modality ensemble. A HyperCombiner network is developed to train a single image segmentation network that can be conditioned on these hyperparameters during inference for much-improved efficiency. Experimental results based on 751 cases from 651 patients compare the proposed rule-modelling approaches with other commonly-adopted end-to-end networks, in this downstream application of automating radiologist labelling on multiparametric MR. By acquiring and interpreting the modality combining rules, specifically the linear-weights or odds ratios associated with individual image modalities, three clinical applications are quantitatively presented and contextualised in the prostate cancer segmentation application, including modality availability assessment, importance quantification and rule discovery.

The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics

Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.

Improving across-dataset brain tissue segmentation for MRI imaging using transformer

Brain tissue segmentation has demonstrated great utility in quantifying MRI data by serving as a precursor to further post-processing analysis. However, manual segmentation is highly labor-intensive, and automated approaches, including convolutional neural networks (CNNs), have struggled to generalize well due to properties inherent to MRI acquisition, leaving a great need for an effective segmentation tool. This study introduces a novel CNN-Transformer hybrid architecture designed to improve brain tissue segmentation by taking advantage of the increased performance and generality conferred by Transformers for 3D medical image segmentation tasks. We first demonstrate the superior performance of our model on various T1w MRI datasets. Then, we rigorously validate our model's generality applied across four multi-site T1w MRI datasets, covering different vendors, field strengths, scan parameters, and neuropsychiatric conditions. Finally, we highlight the reliability of our model on test-retest scans taken in different time points. In all situations, our model achieved the greatest generality and reliability compared to the benchmarks. As such, our method is inherently robust and can serve as a valuable tool for brain related T1w MRI studies. The code for the TABS network is available at: https://github.com/raovish6/TABS.

Distributional independent component analysis for diverse neuroimaging modalities

Recent advances in neuroimaging technologies have provided opportunities to acquire brain images of different modalities for studying human brain organization from both functional and structural perspectives. Analysis of images derived from various modalities involves some common goals such as dimension reduction, denoising, and feature extraction. However, since these modalities have vastly different data characteristics, the current analysis is usually performed using distinct analytical tools that are only suitable for a specific imaging modality. In this paper, we present a Distributional Independent Component Analysis (DICA) that represents a new approach that performs decomposition on the distribution level, providing a unified framework for extracting features across imaging modalities with different scales and representations. When applying DICA to fMRI images, we successfully recover well-established brain functional networks in neuroscience literature, providing empirical validation that DICA delivers neurologically relevant findings. More importantly, we discover several structural network components when applying DICA to DTI images. Through fiber tracking, we find these DICA-derived structural components correspond to several major white fiber bundles. To the best of our knowledge, this is the first time these fiber bundles are successfully identified via blind source separation on single subject DTI images. We also evaluate the performance of DICA as compared with existing ICA methods through extensive simulation studies.

Segmentation of Brain MR Image Using Modified Student’s t-Mixture Model

In conventional brain image analysis, it is a critical step to segment brain magnetic resonance (MR) image into three major tissues: Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The main difficulties for segmenting brain MR image are partial volume effect, intensity inhomogeneity and noise, which result in challenging segmentation task. In this paper, we propose a novel modified method based on the basis of the conventional Student’s t-Mixture Model (SMM), for segmentation of brain MR image and correction of bias field. The advantages of our model are introduced as follows. First, we take account of the influence on the probabilities of the pixels in the adjacent region and take full advantage of the local spatial information and class information. Second, our modified SMM is derived from the traditional finite mixture model (FMM) by adding the bias field correction model; the logarithmic likelihood function of traditional FMM is revised. Third, the noise and bias field can be easily extended to combine with the SMM model and EM algorithm. Last but not least, the exponential coefficients are employed to control the results of segmentation details. As a result, our effective and highly accurate method exhibits high robustness on both simulated and real MR image segmentation, compared to the state-of-the-art algorithms.

An atlas of classifiers-a machine learning paradigm for brain MRI segmentation

We present the Atlas of Classifiers (AoC)-a conceptually novel framework for brain MRI segmentation. The AoC is a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights, a few for each voxel, represent the training dataset. It can, therefore, be considered as a light-weight learning machine, which despite its low capacity does not underfit the problem. The AoC construction is independent of the actual intensities of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities. In this sense, it does not overfit the training data, as well. The proposed method has been applied to numerous publicly available datasets for the segmentation of brain MRI tissues and is shown to be robust to noise and outreach commonly used methods. Promising results were also obtained for multi-modal, cross-modality MRI segmentation. Finally, we show how AoC trained on brain MRIs of healthy subjects can be exploited for lesion segmentation of multiple sclerosis patients.

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic diversity, population dynamics, mechanisms of divergence and adaptation, and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics – the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, provides the opportunity to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV as an efficient and comprehensive method to collect phenomic data in ecological and evolutionary research. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can effectively capture phenomic-level data by taking pictures and analyzing them using CV. Next we describe the primary types of image-based data, review CV approaches for extracting them (including techniques that entail machine learning and others that do not), and identify the most common hurdles and pitfalls. Finally, we highlight recent successful implementations and promising future applications of CV in the study of phenotypes. In anticipation that CV will become a basic component of the biologist’s toolkit, our review is intended as an entry point for ecologists and evolutionary biologists that are interested in extracting phenotypic information from digital images.

Gray Matter Segmentation of Brain MRI Using Hybrid Enhanced Independent Component Analysis in Noisy and Noise Free Environment

Medical segmentation is the primary task performed to diagnosis the abnormalities in the human body. The brain is the complex organ and anatomical segmentation of brain tissues is a challenging task. In this paper, we used Enhanced Independent component analysis to perform the segmentation of gray matter. We used modified K means, Expected Maximization and Hidden Markov random field to provide better spatial correlation that overcomes in-homogeneity, noise and low contrast. Our objective is achieved in two steps (i) initially unwanted tissues are clipped from the MRI image using skull stripped Algorithm (ii) Enhanced Independent Component analysis is used to perform the segmentation of gray matter. We apply the proposed method on both T1w and T2w MRI to perform segmentation of gray matter at different noisy environments. We evaluate the the performance of our proposed system with Jaccard Index, Dice Coefficient and Accuracy. We further compared the proposed system performance with the existing frameworks. Our proposed method gives better segmentation of gray matter useful for diagnosis neurodegenerative disorders.

BISON: Brain tissue segmentation pipeline using T1 -weighted magnetic resonance images and a random forest classifier

PURPOSE

Tissue segmentation from T₁ -weighted (T1W) MRI is a critical requirement in many neuroscience and clinical applications. However, accurate tissue segmentation is challenging because of the variabilities in tissue intensity profiles caused by differences in scanner models, acquisition protocols, and age. In addition, many methods assume healthy anatomy and fail in the presence of pathology such as white matter hyperintensities (WMHs). We present BISON (Brain tISsue segmentatiON), a new pipeline for tissue segmentation using a random forest classifier and a set of intensity and location priors based on T1W MRI.

METHODS

BISON was developed and cross-validated using multiscanner manual labels of 72 subjects aged 5 to 96 years. We also assessed the test-retest reliability of BISON on two data sets: 20 subjects with scan/rescan MR images and manual segmentations and 90 scans from a single individual. The results were compared against Atropos, a state-of-the-art commonly used tissue classification method from advanced normalization tools (ANTs).

RESULTS

BISON cross-validation dice kappa values against manual segmentations of 72 MRI volumes yielded κ_GM = 0.88, κ_WM = 0.85, κ_CSF = 0.77, outperforming Atropos (κ_GM = 0.79, κ_WM = 0.84, κ_CSF = 0.64), test-retest values on 20 subjects of κ_GM = 0.94, κ_WM = 0.92, κ_CSF = 0.77 outperforming both manual (κ_GM = 0.92, κ_WM = 0.91, κ_CSF =0.74) and Atropos (κ_GM = 0.87, κ_WM = 0.92, κ_CSF = 0.79). Finally, BISON outperformed Atropos, FAST (fast automated segmentation tool) from the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library, and SPM12 (statistical parametric mapping 12) in the presence of WMHs.

CONCLUSION

BISON can provide accurate and robust segmentations in data from various age ranges and scanner models, making it ideal for performing tissue classification in large multicenter and multiscanner databases.

Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation

Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.

Review on 2D and 3D MRI Image Segmentation Techniques

BACKGROUND

Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics.

DISCUSSION

Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation.

CONCLUSION

This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

A 3D Spatially Weighted Network for Segmentation of Brain Tissue From MRI

The segmentation of brain tissue in MRI is valuable for extracting brain structure to aid diagnosis, treatment and tracking the progression of different neurologic diseases. Medical image data are volumetric and some neural network models for medical image segmentation have addressed this using a 3D convolutional architecture. However, this volumetric spatial information has not been fully exploited to enhance the representative ability of deep networks, and these networks have not fully addressed the practical issues facing the analysis of multimodal MRI data. In this paper, we propose a spatially-weighted 3D network (SW-3D-UNet) for brain tissue segmentation of single-modality MRI, and extend it using multimodality MRI data. We validate our model on the MRBrainS13 and MALC12 datasets. This unpublished model ranked first on the leaderboard of the MRBrainS13 Challenge.

A Spatially Constrained Probabilistic Model for Robust Image Segmentation

In general, the hidden Markov random field (HMRF) represents the class label distribution of an image in probabilistic model based segmentation. The class label distributions provided by existing HMRF models consider either the number of neighboring pixels with similar class labels or the spatial distance of neighboring pixels with dissimilar class labels. Also, this spatial information is only considered for estimation of class labels of the image pixels, while its contribution in parameter estimation is completely ignored. This, in turn, deteriorates the parameter estimation, resulting in sub-optimal segmentation performance. Moreover, the existing models assign equal weightage to the spatial information for class label estimation of all pixels throughout the image, which, create significant misclassification for the pixels in boundary region of image classes. In this regard, the paper develops a new clique potential function and a new class label distribution, incorporating the information of image class parameters. Unlike existing HMRF model based segmentation techniques, the proposed framework introduces a new scaling parameter that adaptively measures the contribution of spatial information for class label estimation of image pixels. The importance of the proposed framework is depicted by modifying the HMRF based segmentation methods. The advantage of proposed class label distribution is also demonstrated irrespective of the underlying intensity distributions. The comparative performance of the proposed and existing class label distributions in HMRF model is demonstrated both qualitatively and quantitatively for brain MR image segmentation, HEp-2 cell delineation, natural image and object segmentation.

Robust spatial fuzzy GMM based MRI segmentation and carotid artery plaque detection in ultrasound images

BACKGROUND AND OBJECTIVE

In medical image analysis for disease diagnosis, segmentation is one of the challenging tasks. Owing to the inherited degradations in MRI improper segments are produced. Segmentation process is an important step in brain tissue analysis. Moreover, an early detection of plaque in carotid artery using ultrasound images may prevent serious brain strokes. Unfortunately, low quality and noisy ultrasound images are still challenges for accurate segmentation. The objective of this research is to develop a robust segmentation approach for medical images such as brain MRI and carotid artery ultrasound images.

METHODS

In this paper, a novel approach is proposed to address the segmentation challenges of medical images. The proposed approach employed fuzzy intelligence and Gaussian mixture model (GMM). It comprises two phases; firstly, incorporating spatial fuzzy c-means in GMM by exploiting statistical, texture, and wavelet image features. During model development, GMM parameters are estimated in presence of noise by EM algorithm iteratively. Utilizing these parameters, brain MRI images are segmented. In next phase, developed approach is applied to solve a real problem of carotid artery plaque detection using ultrasound images. The dataset of real patients annotated by radiologists has been obtained from Radiology Department, Shifa International Hospital Islamabad, Pakistan. For this, intima-media-thickness values are computed from the proposed segmentation followed by support vector machines for plaque classification (normal/abnormal).

RESULTS

The obtained segmentation has been evaluated on standard brain MRI dataset and offers high segmentation accuracy of 99.2%. The proposed approach outperforms in term of segmentation performance range of 3-9% as compared to the state of the art approaches on brain MRI. Furthermore, the proposed approach shows robustness to various levels of Gaussian and Rician image noises. On carotid artery dataset, we have obtained high plaque detection rate in terms of accuracy, sensitivity, specificity, and F-score values of 98.8%, 99.3%, 98.0%, and 97.5% respectively.

CONCLUSIONS

The proposed approach segments both modalities with high precision and shows robustness at Gaussian and Rician noise levels. Results for brain MRI and ultrasound images indicate its effectiveness and can be used as second opinion in addition to the radiologists. The developed approach is straightforward, efficient, and reproducible. It may benefit to improve the clinical evaluation of the disease in both asymptomatic and symptomatic individuals.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

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.

Automatic brain tissue segmentation based on graph filter

Background

Accurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects.

Methods

To overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals.

Results

The performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset.

Conclusions

The proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.

Histogram-Based Features Selection and Volume of Interest Ranking for Brain PET Image Classification

Positron emission tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative diseases diagnosis. Computer-aided diagnosis, based on medical image analysis, could help quantitative evaluation of brain diseases such as Alzheimer's disease (AD). A novel method of ranking the effectiveness of brain volume of interest (VOI) to separate healthy control from AD brains PET images is presented in this paper. Brain images are first mapped into anatomical VOIs using an atlas. Histogram-based features are then extracted and used to select and rank VOIs according to the area under curve (AUC) parameter, which produces a hierarchy of the ability of VOIs to separate between groups of subjects. The top-ranked VOIs are then input into a support vector machine classifier. The developed method is evaluated on a local database image and compared to the known selection feature methods. Results show that using AUC outperforms classification results in the case of a two group separation.

Heterogeneity in pulmonary emphysema: Analysis of CT attenuation using Gaussian mixture model

PURPOSE

To utilize Gaussian mixture model (GMM) for the quantification of chronic obstructive pulmonary disease (COPD) and to evaluate the combined use of multiple types of quantification.

MATERIALS AND METHODS

Eighty-seven patients (67 men, 20 women; age, 67.4 ± 11.0 years) who had undergone computed tomography (CT) and pulmonary function test (PFT) were included. The heterogeneity of CT attenuation in emphysema (HC) was obtained by analyzing a distribution of CT attenuation with GMM. The percentages of low-attenuation volume in the lungs (LAV), wall area of bronchi (WA), and the cross-sectional area of small pulmonary vessels (CSA) were also calculated. The relationships between COPD quantifications and the PFT results were evaluated by Pearson's correlation coefficients and through linear models, with the best models selected using Akaike information criterion (AIC).

RESULTS

The correlation coefficients with FEV1 were as follows: LAV, -0.505; HC, -0.277; CSA, 0.384; WA, -0.196. The correlation coefficients with FEV1/FVC were: LAV, -0.640; HC, -0.136; CSA, 0.288; WA, -0.131. For predicting FEV1, the smallest AIC values were obtained in the model with LAV, HC, CSA, and WA. For predicting FEV1/FVC, the smallest AIC values were obtained in the model with LAV and HC. In both models, the coefficient of HC was statistically significant (P-values = 0.000880 and 0.0441 for FEV1 and FEV1/FVC, respectively).

CONCLUSION

GMM was applied to COPD quantification. The results of this study show that COPD severity was associated with HC. In addition, it is shown that the combined use of multiple types of quantification made the evaluation of COPD severity more reliable.

Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans

OBJECTIVE

Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF [before and after surgery, i.e., preoperative (pre-op) versus postoperative (post-op)] plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-op computational tomographic (CT) scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity- and feature-based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e., a training set of CT scans with labeled pixel identities is employed.

METHODS

Our contributions include: 1) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes; 2) quantification of associated computation and memory footprint; and 3) a customized training and test procedure for segmenting post-op hydrocephalic CT images.

RESULTS

Experiments performed on infant CT brain images acquired from the CURE Children's Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against a number of training samples, enhancing its deployment potential.

A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In this work, we present a hidden Markov random field model with expectation maximization (EM-HMRF) modeling the components using the α-stable distribution. The proposed model is a generalization of the widely used EM-HMRF algorithm with Gaussian distributions. We test the α-stable EM-HMRF model in synthetic data and brain MRI data. The proposed methodology presents two main advantages: Firstly, it is more robust to outliers. Secondly, we obtain similar results than using Gaussian when the Gaussian assumption holds. This approach is able to model the spatial dependence between neighboring voxels in tomographic brain MRI.

Robust generative asymmetric GMM for brain MR image segmentation

BACKGROUND AND OBJECTIVES

Accurate segmentation of brain tissues from magnetic resonance (MR) images based on the unsupervised statistical models such as Gaussian mixture model (GMM) has been widely studied during last decades. However, most GMM based segmentation methods suffer from limited accuracy due to the influences of noise and intensity inhomogeneity in brain MR images. To further improve the accuracy for brain MR image segmentation, this paper presents a Robust Generative Asymmetric GMM (RGAGMM) for simultaneous brain MR image segmentation and intensity inhomogeneity correction.

METHOD

First, we develop an asymmetric distribution to fit the data shapes, and thus construct a spatial constrained asymmetric model. Then, we incorporate two pseudo-likelihood quantities and bias field estimation into the model's log-likelihood, aiming to exploit the neighboring priors of within-cluster and between-cluster and to alleviate the impact of intensity inhomogeneity, respectively. Finally, an expectation maximization algorithm is derived to iteratively maximize the approximation of the data log-likelihood function to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously.

RESULTS

To demonstrate the performances of the proposed algorithm, we first applied the proposed algorithm to a synthetic brain MR image to show the intermediate illustrations and the estimated distribution of the proposed algorithm. The next group of experiments is carried out in clinical 3T-weighted brain MR images which contain quite serious intensity inhomogeneity and noise. Then we quantitatively compare our algorithm to state-of-the-art segmentation approaches by using Dice coefficient (DC) on benchmark images obtained from IBSR and BrainWeb with different level of noise and intensity inhomogeneity. The comparison results on various brain MR images demonstrate the superior performances of the proposed algorithm in dealing with the noise and intensity inhomogeneity.

CONCLUSION

In this paper, the RGAGMM algorithm is proposed which can simply and efficiently incorporate spatial constraints into an EM framework to simultaneously segment brain MR images and estimate the intensity inhomogeneity. The proposed algorithm is flexible to fit the data shapes, and can simultaneously overcome the influence of noise and intensity inhomogeneity, and hence is capable of improving over 5% segmentation accuracy comparing with several state-of-the-art algorithms.

Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction

In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach makes it suitable to address a wide range of medical image computing problems, our work focuses primarily on neuroimaging applications. In particular we validate the proposed method on both real and synthetic brain MR scans including the cervical cord and demonstrate that it yields accurate alignment of brain and spinal cord structures, as compared to state-of-the-art tools for medical image registration. At the same time we illustrate how the resulting tissue probability maps can readily be used to segment, bias correct and spatially normalise unseen data, which are all crucial pre-processing steps for MR imaging studies.

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We present a generative modelling framework to process large MRI data sets.

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The proposed framework can serve to learn average-shaped tissue probability maps and empirical intensity priors.

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We explore semi-supervised learning and variational inference schemes.

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The method is validated against state-of-the-art tools using publicly available data.

An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images

OBJECTIVE

This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images.

METHODS

For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line.

RESULTS

Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%).

State-of-the-Art Methods for Brain Tissue Segmentation: A Review

Brain tissue segmentation is one of the most sought after research areas in medical image processing. It provides detailed quantitative brain analysis for accurate disease diagnosis, detection, and classification of abnormalities. It plays an essential role in discriminating healthy tissues from lesion tissues. Therefore, accurate disease diagnosis and treatment planning depend merely on the performance of the segmentation method used. In this review, we have studied the recent advances in brain tissue segmentation methods and their state-of-the-art in neuroscience research. The review also highlights the major challenges faced during tissue segmentation of the brain. An effective comparison is made among state-of-the-art brain tissue segmentation methods. Moreover, a study of some of the validation measures to evaluate different segmentation methods is also discussed. The brain tissue segmentation, content in terms of methodologies, and experiments presented in this review are encouraging enough to attract researchers working in this field.

Toward the automatic quantification of in utero brain development in 3D structural MRI: A review

Investigating the human brain in utero is important for researchers and clinicians seeking to understand early neurodevelopmental processes. With the advent of fast magnetic resonance imaging (MRI) techniques and the development of motion correction algorithms to obtain high-quality 3D images of the fetal brain, it is now possible to gain more insight into the ongoing maturational processes in the brain. In this article, we present a review of the major building blocks of the pipeline toward performing quantitative analysis of in vivo MRI of the developing brain and its potential applications in clinical settings. The review focuses on T1- and T2-weighted modalities, and covers state of the art methodologies involved in each step of the pipeline, in particular, 3D volume reconstruction, spatio-temporal modeling of the developing brain, segmentation, quantification techniques, and clinical applications. Hum Brain Mapp 38:2772-2787, 2017. © 2017 Wiley Periodicals, Inc.

Deep and Structured Robust Information Theoretic Learning for Image Analysis

This paper presents a robust information theoretic (RIT) model to reduce the uncertainties, i.e., missing and noisy labels, in general discriminative data representation tasks. The fundamental pursuit of our model is to simultaneously learn a transformation function and a discriminative classifier that maximize the mutual information of data and their labels in the latent space. In this general paradigm, we, respectively, discuss three types of the RIT implementations with linear subspace embedding, deep transformation, and structured sparse learning. In practice, the RIT and deep RIT are exploited to solve the image categorization task whose performances will be verified on various benchmark data sets. The structured sparse RIT is further applied to a medical image analysis task for brain magnetic resonance image segmentation that allows group-level feature selections on the brain tissues.

Patch-based augmentation of Expectation-Maximization for brain MRI tissue segmentation at arbitrary age after premature birth

Accurate automated tissue segmentation of premature neonatal magnetic resonance images is a crucial task for quantification of brain injury and its impact on early postnatal growth and later cognitive development. In such studies it is common for scans to be acquired shortly after birth or later during the hospital stay and therefore occur at arbitrary gestational ages during a period of rapid developmental change. It is important to be able to segment any of these scans with comparable accuracy. Previous work on brain tissue segmentation in premature neonates has focused on segmentation at specific ages. Here we look at solving the more general problem using adaptations of age specific atlas based methods and evaluate this using a unique manually traced database of high resolution images spanning 20 gestational weeks of development. We examine the complimentary strengths of age specific atlas-based Expectation-Maximization approaches and patch-based methods for this problem and explore the development of two new hybrid techniques, patch-based augmentation of Expectation-Maximization with weighted fusion and a spatial variability constrained patch search. The former approach seeks to combine the advantages of both atlas- and patch-based methods by learning from the performance of the two techniques across the brain anatomy at different developmental ages, while the latter technique aims to use anatomical variability maps learnt from atlas training data to locally constrain the patch-based search range. The proposed approaches were evaluated using leave-one-out cross-validation. Compared with the conventional age specific atlas-based segmentation and direct patch based segmentation, both new approaches demonstrate improved accuracy in the automated labeling of cortical gray matter, white matter, ventricles and sulcal cortical-spinal fluid regions, while maintaining comparable results in deep gray matter.