Multispectral analysis of the brain using magnetic resonance imaging

Bayesian Depth-Wise Convolutional Neural Network Design for Brain Tumor MRI Classification

In recent years, deep learning has been applied to many medical imaging fields, including medical image processing, bioinformatics, medical image classification, segmentation, and prediction tasks. Computer-aided detection systems have been widely adopted in brain tumor classification, prediction, detection, diagnosis, and segmentation tasks. This work proposes a novel model that combines the Bayesian algorithm with depth-wise separable convolutions for accurate classification and predictions of brain tumors. We combine Bayesian modeling learning and Convolutional Neural Network learning methods for accurate prediction results to provide the radiologists the means to classify the Magnetic Resonance Imaging (MRI) images rapidly. After thorough experimental analysis, our proposed model outperforms other state-of-the-art models in terms of validation accuracy, training accuracy, F1-score, recall, and precision. Our model obtained high performances of 99.03% training accuracy and 94.32% validation accuracy, F1-score, precision, and recall values of 0.94, 0.95, and 0.94, respectively. To the best of our knowledge, the proposed work is the first neural network model that combines the hybrid effect of depth-wise separable convolutions with the Bayesian algorithm using encoders.

Brain tissues have single-voxel signatures in multi-spectral MRI

Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q-space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.

Deep HT: A deep neural network for diagnose on MR images of tumors of the hand

BACKGROUND

There are many types of hand tumors, and it is often difficult for imaging diagnosticians to make a correct diagnosis, which can easily lead to misdiagnosis and delay in treatment. Thus in this paper, we propose a deep neural network for diagnose on MR Images of tumors of the hand in order to better define preoperative diagnosis and standardize surgical treatment.

METHODS

We collected MRI figures of 221 patients with hand tumors from one medical center from 2016 to 2019, invited medical experts to annotate the images to form the annotation data set. Then the original image is preprocessed to get the image data set. The data set is randomly divided into ten parts, nine for training and one for test. Next, the data set is input into the neural network system for testing. Finally, average the results of ten experiments as an estimate of the accuracy of the algorithm.

RESULTS

This research uses 221 images as dataset and the system shows an average confidence level of 71.6% in segmentation of hand tumors. The segmented tumor regions are validated through ground truth analysis and manual analysis by a radiologist.

CONCLUSIONS

With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder decoder deep architectures. Therefore, in this paper, we propose an automatic segmentation method based on DeepLab v3+ and achieved a good diagnostic accuracy rate.

Emerging Advanced Technologies Developed by IPR for Bio Medical Applications ‑.A Review

Over the last decade, research has intensified worldwide on the use of low-temperature plasmas in medicine and healthcare. Researchers have discovered many methods of applying plasmas to living tissues to deactivate pathogens; to end the flow of blood without damaging healthy tissue; to sanitize wounds and accelerate its healing; and to selectively kill malignant cancer cells. This review paper presents the latest development of advanced and plasma-based technologies used for applications in neurology in particular. Institute for Plasma Research (IPR), an aided institute of the Department of Atomic Energy (DAE), has also developed various technologies in some of these areas. One of these is an Atmospheric Pressure Plasma Jet (APPJ). This device is being studied to treat skin diseases, for coagulation of blood at faster rates and its interaction with oral, lung, and brain cancer cells. In certain cases, in-vitro studies have yielded encouraging results and limited in-vivo studies have been initiated. Plasma activated water has been produced in the laboratory for microbial disinfection, with potential applications in the health sector. Recently, plasmonic nanoparticle arrays which allow detection of very low concentrations of chemicals is studied in detail to allow early-stage detection of diseases. IPR has also been developing AI-based software called DeepCXR and AIBacilli for automated, high-speed screening and detection of footprints of tuberculosis (TB) in Chest X-ray images and for recognizing single/multiple TB bacilli in sputum smear test images, respectively. Deep Learning systems are increasingly being used around the world for analyzing electroencephalogram (EEG) signals for emotion recognition, mental workload, and seizure detection.

Personalized microstructural evaluation using a Mahalanobis-distance based outlier detection strategy on epilepsy patients' DTI data - Theory, simulations and example cases

Quantitative MRI methods have recently gained extensive interest and are seeing substantial developments; however, their application in single patient vs control group comparisons is often limited by inherent statistical difficulties. One such application is detecting malformations of cortical development (MCDs) behind drug resistant epilepsies, a task that, especially when based solely on conventional MR images, may represent a serious challenge. We aimed to develop a novel straightforward voxel-wise evaluation method based on the Mahalanobis-distance, combining quantitative MRI data into a multidimensional parameter space and detecting lesion voxels as outliers. Simulations with standard multivariate Gaussian distribution and resampled DTI-eigenvalue data of 45 healthy control subjects determined the optimal critical value, cluster size threshold, and the expectable lesion detection performance through ROC-analyses. To reduce the effect of false positives emanating from registration artefacts and gyrification differences, an automatic classification method was applied, fine-tuned using a leave-one-out strategy based on diffusion and T1-weighted data of the controls. DWI processing, including thorough corrections and robust tensor fitting was performed with ExploreDTI, spatial coregistration was achieved with the DARTEL tools of SPM12. Additional to simulations, clusters of outlying diffusion profile, concordant with neuroradiological evaluation and independent calculations with the MAP07 toolbox were identified in 12 cases of a 13 patient example population with various types of MCDs. The multidimensional approach proved sufficiently sensitive in pinpointing regions of abnormal tissue microstructure using DTI data both in simulations and in the heterogeneous example population. Inherent limitations posed by registration artefacts, age-related differences, and the different or mixed pathologies limit the generalization of specificity estimation. Nevertheless, the proposed statistical method may aid the everyday examination of individual subjects, ever so more upon extending the framework with quantitative information from other modalities, e.g. susceptibility mapping, relaxometry, or perfusion.

An overview of deep learning in medical imaging focusing on MRI

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Multivariate characterization of white matter heterogeneity in autism spectrum disorder

The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders.

Evaluation of automated brain MR image segmentation and volumetry methods

We compare three widely used brain volumetry methods available in the software packages FSL, SPM5, and FreeSurfer and evaluate their performance using simulated and real MR brain data sets. We analyze the accuracy of gray and white matter volume measurements and their robustness against changes of image quality using the BrainWeb MRI database. These images are based on "gold-standard" reference brain templates. This allows us to assess between- (same data set, different method) and also within-segmenter (same method, variation of image quality) comparability, for both of which we find pronounced variations in segmentation results for gray and white matter volumes. The calculated volumes deviate up to >10% from the reference values for gray and white matter depending on method and image quality. Sensitivity is best for SPM5, volumetric accuracy for gray and white matter was similar in SPM5 and FSL and better than in FreeSurfer. FSL showed the highest stability for white (<5%), FreeSurfer (6.2%) for gray matter for constant image quality BrainWeb data. Between-segmenter comparisons show discrepancies of up to >20% for the simulated data and 24% on average for the real data sets, whereas within-method performance analysis uncovered volume differences of up to >15%. Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies.

Volume and shape in feature space on adaptive FCM in MRI segmentation

Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Automated segmentation and classification of high throughput yeast assay spots

Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here, an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named "X-Gal" and "growth assay" plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally, an artificial neural network is used to score the X-Gal and growth assay spots using the extracted features. The performance of the system is evaluated using spots of 60 images. After training the networks using training and validation sets, the system was assessed on the test set. The overall accuracies of 95.4% and 88.2% are achieved, respectively, for scoring the X-Gal and growth assay spots.

Wavelet denoising of multicomponent images using gaussian scale mixture models and a noise-free image as priors

In this paper, a Bayesian wavelet-based denoising procedure for multicomponent images is proposed. A denoising procedure is constructed that (1) fully accounts for the multicomponent image covariances, (2) makes use of Gaussian scale mixtures as prior models that approximate the marginal distributions of the wavelet coefficients well, and (3) makes use of a noise-free image as extra prior information. It is shown that such prior information is available with specific multicomponent image data of, e.g., remote sensing and biomedical imaging. Experiments are conducted in these two domains, in both simulated and real noisy conditions.

Multispectral analysis of bone lesions in the hands of patients with rheumatoid arthritis

Quantitative measures of rheumatoid arthritis (RA) disease progression can provide valuable tools for evaluation of new treatments during clinical trials. In this study, a novel multispectral (MS) MRI analysis method is presented to quantify changes in bone lesion volume (DeltaBLV) in the hands of RA patients. Image registration and MS analysis were employed to identify MS tissue class transitions between two serial MRI exams. DeltaBLV was determined from MS class transitions between two time points. The following three classifiers were investigated: (a) multivariate Gaussian (MVG), (b) k-nearest neighbor (k-NN), and (c) K-means (KM). Unlike supervised classifiers (MVG, k-NN), KM, an unsupervised classifier, does not require labeled training data, resulting in potentially greater clinical utility. All MS estimates of DeltaBLV were linearly correlated (r(p)) with manual estimates. KM and k-NN estimates also exhibited a significant rank-order correlation (r(s)) with manual estimates. For KM, r(p) = 0.94 p < 0.0001, r(s) = 0.76 p = 0.002; for k-NN, r(p) = 0.86 p = 0.0001, r(s) = 0.69 p = 0.009; and for MVG, r(p) = 0.84 p = 0.0003, r(s) = 0.49 p = 0.09. Temporal classification rates were as follows: for KM, 90.1%; for MVG, 89.5%; and for k-NN, 86.7%. KM matched the performance of k-NN, offering strong potential for use in multicenter clinical trials. This study demonstrates that MS tissue class transitions provide a quantitative measure of DeltaBLV.

Wavelet thresholding of multivalued images

In this paper, a denoising technique for multivalued images exploiting interband correlations is proposed. A redundant wavelet transform is applied and denoising is applied by thresholding wavelet coefficients. Specific functions of the wavelet coefficients are defined that exploit interscale and/or interband correlation of the signal. Three functions are studied: the square of the wavelet coefficients, products of coefficients at adjacent scales, and products of coefficients from different bands. For these functions, the signal and noise probability density functions (pdf) become more separated. The high signal correlation between bands is exploited by summing these products over all bands, in this way separating noise and signal pdfs even more. The noise pdf of the proposed quantities is derived analytically and from this, a wavelet threshold is derived. The technique is demonstrated to outperform single band wavelet thresholding on multispectral remote sensing images and on multimodal MRI images.

An orthogonal wavelet representation of multivalued images

In this paper, a new orthogonal wavelet representation of multivalued images is presented. The idea for this representation is based on the concept of maximal gradient of multivalued images. This concept is generalized from gradients toward linear vector operators in the image plane with equal components along rows and columns. Using this generalization, the pyramidal dyadic wavelet transform algorithm using quadrature mirror filters is modified to be applied to multivalued images. This results in a representation of a single image, containing multiscale detail information from all component images involved. This representation leads to multiple applications ranging from multispectral image fusion to color and multivalued image enhancement, denoising and segmentation. In this paper, the representation is applied for fusion of images. More in particular, we introduce a scheme to merge high spatial resolution greylevel images with low spatial resolution multivalued images to improve spatial resolution of the latter while preserving spectral resolution. Two applications are studied: demosaicing of color images and merging of multispectral remote sensing images.

A multivalued image wavelet representation based on multiscale fundamental forms

In this paper, a new wavelet representation for multivalued images is presented. The idea for this representation is based on the first fundamental form that provides a local measure for the contrast of a multivalued image. In this paper, this concept is extended toward multiscale fundamental forms using the dyadic wavelet transform of Mallat. The multiscale fundamental forms provide a local measure for the contrast of a multivalued image at different scales. The representation allows for a multiscale edge description of multivalued images. A variety of applications is presented, including multispectral image fusion, color image enhancement and multivalued image noise filtering. In an experimental section, the presented techniques are compared to single valued and/or single scale algorithms that were previously described in the literature. The techniques, based on the new representation are demonstrated to outperform the others.

Fusion and merging of multispectral images with use of multiscale fundamental forms

A new multispectral image wavelet representation is introduced, based on multiscale fundamental forms. This representation describes gradient information of multispectral images in a multiresolution framework. The representation is, in particular, extremely suited for fusion and merging of multispectral images. For fusion as well as for merging, a strategy is described. Experiments are performed on multispectral images, where Landsat Thematic Mapper images are fused and merged with SPOT Panchromatic images. The proposed techniques are compared with wavelet-based techniques described in the literature.

Novel approaches to imaging brain tumors

Brain imaging techniques are assuming a greater range of roles in neuro-oncology. New techniques promise earlier recognition of the spread of tumors to the brain, which is useful in staging of disseminated disease, as well as better definition of small lesions associated with presentations of epilepsy. There is the promise that entirely noninvasive, specific diagnosis of brain tumors may become possible. Imaging methods are being used increasingly to direct and monitor therapy. Preoperative and intraoperative imaging are being used for guiding tumor surgery. An exciting potential goal for greater use of imaging is in the individualization of medical therapies either by analysis of in vitro responses or by visualization of drug responses on the tumor in situ. An important focus for technical development is in the robust integration of complementary information to allow optimization of the sensitivity and specificity of multimodal examinations.

Validation of partial tissue segmentation of single-channel magnetic resonance images of the brain

We describe and evaluate a practical, automated algorithm based on local statistical mixture modeling for segmenting single-channel, T1-weighted volumetric magnetic resonance images of the brain into gray matter, white matter, and cerebrospinal fluid. We employed a stereological sampling method to assess, prospectively, the performance of the method with respect to human experts on 10 normal T1-weighted brain scans acquired with a three-dimensional gradient echo pulse sequence. The overall kappa statistic for the concordance of the algorithm with the human experts was 0.806, while that among raters, excluding the algorithm, was 0.802. The algorithm had better agreement with the modal expert decision (kappa = 0.878). The algorithm could not be distinguished from the experts by this measure. We also validated the algorithm on a simulated MR scan of a digital brain phantom with known tissue composition. Global gray matter and white matter errors were 1% and <1%, respectively, and correlation coefficients with the underlying tissue model were 0.95 for gray matter, 0.98 for white matter, and 0.95 for cerebrospinal fluid. In both approaches to validation, we evaluated both local and global performance of the algorithm. Human experts generated slightly higher global gray matter proportion estimates on the test brain scans relative to the algorithm (3.7%) and on the simulated MR scan relative to the true tissue model (4.4%). The algorithm underestimated gray in some subcortical nuclei which contain admixed gray and white matter. We demonstrate the reliability of the method on individual 1 NEX data sets of the test subjects, and its insensitivity to the precise values of initial model parameters. The output of this algorithm is suitable for quantifying cerebral cortical tissue, using a commonly performed commercial pulse sequence.

Brain tissue classification of magnetic resonance images using partial volume modeling

This paper presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, we consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classes, but also mixtures, called mixclasses. A statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to assess the risk alpha of the approximation. In order to classify a brain into three types of brain tissue and deal with the problem of partial volume effects, the proposed algorithm uses two steps: 1) segmentation of the brain into pure and mixclasses using the mixture model; 2) reclassification of the mixclasses into the pure classes using knowledge about the obtained pure classes. Both steps use Markov random field (MRF) models. The multifractal dimension, describing the topology of the brain, is added to the MRFs to improve discrimination of the mixclasses. The algorithm is evaluated using both simulated images and real MR images with different T1-weighted acquisition sequences.

Volume distribution of cerebrospinal fluid using multispectral MR imaging

The goal of this study was to design a reliable method to quantify and visualize the anatomical distribution of cerebrospinal fluid (CSF) intracranially. The method should be clinically applicable and based on multispectral analysis of three-dimensional (3D) magnetic resonance images. T1-weighted, T2-weighted and proton density-weighted fast 3D gradient pulse sequences were used to form high resolution multispectral 3D images of the entire head. Training on single 2D slices, the Mahalanobis distances between the resulting multivariate tissue-specific densities were studied as functions of the feature vector composition and dimension. Multispectral analysis was applied to the images of four human brains. One feature vector with three components gave CSF volumes that were in the normal range and corresponding anatomical distributions that largely agreed with general anatomical knowledge. The exception was CSF missing around the basal parts of the brain due to signal artifacts. These artifacts were almost certainly due to the coil effect and magnetic field inhomogeneities induced by the imaged head. Such misclassifications could probably be reduced by bias field estimation and proper image restoration. Most CSF voxels formed large connected components that were found automatically, so the manual post-processing of the classified 3D image to locate CSF voxels was moderate. It is concluded that some of the fast, high resolution 3D gradient echo pulse sequences that have become available on conventional clinical scanners can be used to obtain good estimates of brain cerebrospinal fluid anatomical distribution and volume.

Multi-level adaptive segmentation of multi-parameter MR brain images

MR brain image segmentation into several tissue classes is of significant interest to visualize and quantify individual anatomical structures. Traditionally, the segmentation is performed manually in a clinical environment that is operator dependent and may be difficult to reproduce. Though several algorithms have been investigated in the literature for computerized automatic segmentation of MR brain images, they are usually targeted to classify image into a limited number of classes such as white matter, gray matter, cerebrospinal fluid and specific lesions. We present a novel model-based method for the automatic segmentation and classification of multi-parameter MR brain images into a larger number of tissue classes of interest. Our model employs 15 brain tissue classes instead of the commonly used set of four classes, which were of clinical interest to neuroradiologists for following-up with patients suffering from cerebrovascular deficiency (CVD) and/or stroke. The model approximates the spatial distribution of tissue classes by a Gauss Markov random field and uses the maximum likelihood method to estimate the class probabilities and transitional probabilities for each pixel of the image. Multi-parameter MR brain images with T(1), T(2), proton density, Gd+T(1), and perfusion imaging were used in segmentation and classification. In the development of the segmentation model, true class-membership of measured parameters was determined from manual segmentation of a set of normal and pathologic brain images by a team of neuroradiologists. The manual segmentation was performed using a human-computer interface specifically designed for pixel-by-pixel segmentation of brain images. The registration of corresponding images from different brains was accomplished using an elastic transformation. The presented segmentation method uses the multi-parameter model in adaptive segmentation of brain images on a pixel-by-pixel basis. The method was evaluated on a set of multi-parameter MR brain images of a twelve-year old patient 48h after suffering a stroke. The results of classification as compared to the manual segmentation of the same data show the efficacy and accuracy of the presented methods as well as its capability to create and learn new tissue classes.

Robust parameter estimation of intensity distributions for brain magnetic resonance images

This paper presents two new methods for robust parameter estimation of mixtures in the context of magnetic resonance (MR) data segmentation. The head is constituted of different types of tissue that can be modeled by a finite mixture of multivariate Gaussian distributions. Our goal is to estimate accurately the statistics of desired tissues in presence of other ones of lesser interest. These latter can be considered as outliers and can severely bias the estimates of the former. For this purpose, we introduce a first method, which is an extension of the expectation-maximization (EM) algorithm, that estimates parameters of Gaussian mixtures but incorporates an outlier rejection scheme which allows to compute the properties of the desired tissues in presence of atypical data. The second method is based on genetic algorithms and is well suited for estimating the parameters of mixtures of different kind of distributions. We use this property by adding a uniform distribution to the Gaussian mixture for modeling the outliers. The proposed genetic algorithm can efficiently estimate the parameters of this extended mixture for various initial settings. Also, by changing the minimization criterion, estimates of the parameters can be obtained by histogram fitting which considerably reduces the computational cost. Experiments on synthetic and real MR data show that accurate estimates of the gray and white matters parameters are computed.

Automatic tumor segmentation using knowledge-based techniques

A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRI's) of the human brain is presented. The MRI's consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised k-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

Markov random field segmentation of brain MR images

We describe a fully-automatic three-dimensional (3-D)-segmentation technique for brain magnetic resonance (MR) images. By means of Markov random fields (MRF's) the segmentation algorithm captures three features that are of special importance for MR images, i.e., nonparametric distributions of tissue intensities, neighborhood correlations, and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. In particular, the impact of noise, inhomogeneity, smoothing, and structure thickness are analyzed quantitatively. Even single-echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone, and background. A simulated annealing and an iterated conditional modes implementation are presented.

Statistical approach to segmentation of single-channel cerebral MR images

A statistical model is presented that represents the distributions of major tissue classes in single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). The model accounts for random noise, magnetic field inhomogeneities, and biological variations of the tissues. Intensity measurements are modeled by a finite Gaussian mixture. Smoothness and piecewise contiguous nature of the tissue regions are modeled by a three-dimensional (3-D) Markov random field (MRF). A segmentation algorithm, based on the statistical model, approximately finds the maximum a posteriori (MAP) estimation of the segmentation and estimates the model parameters from the image data. The proposed scheme for segmentation is based on the iterative conditional modes (ICM) algorithm in which measurement model parameters are estimated using local information at each site, and the prior model parameters are estimated using the segmentation after each cycle of iterations. Application of the algorithm to a sample of clinical MR brain scans, comparisons of the algorithm with other statistical methods, and a validation study with a phantom are presented. The algorithm constitutes a significant step toward a complete data driven unsupervised approach to segmentation of MR images in the presence of the random noise and intensity inhomogeneities.

Multispectral magnetic resonance images segmentation using fuzzy Hopfield neural network

This paper demonstrates a fuzzy Hopfield neural network for segmenting multispectral MR brain images. The proposed approach is a new unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique. Its implementation consists of the combination of 2-D Hopfield neural network and fuzzy c-means clustering algorithm in order to make parallel implementation for segmenting multispectral MR brain images feasible. For generating feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need for finding weighting factors in the energy function which is formulated and based on a basic concept commonly used in pattern classification, called the 'within-class scatter matrix' principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The experimental results show that a near optimal solution can be obtained using the fuzzy Hopfield neural network based on the within-class scatter matrix.

Multispectral image visualization with nonlinear projections

Two visualization techniques based on nonlinear projections for fusing multispectral image data sets into a single most informative gray scale image are described. These techniques are fast, theoretically attractive, complement linear techniques, and may be of value in highlighting specific regions where the component images should be examined in detail.

Functional magnetic resonance imaging of primary visual processing using a 1.0 Tesla scanner

Recent advances in functional magnetic resonance imaging (fMRI) at > or = 1.5 T magnetic field strength and with high speed single-shot echo planar imaging techniques have made it possible to monitor local changes in cerebral blood volume, cerebral blood flow, and blood oxygenation level in response to sensory stimulation, simple motor activity, and possibly also to more complex cognitive processing. However, fMRI has also been accomplished on conventional MR scanners of medium field strength (approximately 1.0 T) using special pulse sequences and appropriate methods for image analysis. We present results from six subjects on photic stimulation using a standard 1.0 T MR scanner together with special software for off-line image analysis. Continuous serial T2-weighted imaging were performed for 6 minutes in the plane of the calcarine fissure. There were 3 repetitions of 1 minute resting state of darkness (OFF) and 1 minute activated state (ON) with 8 Hz flicker stimulation. To directly map these functional images to the underlying anatomy we also acquired a high resolution T1-weighted image from the same axial slice. The results demonstrated that stimulus-related signals can be obtained from primary visual cortex with a conventional 1.0 T MR scanner. Further methodological improvements are discussed and related to present and future possibilities for the use of fMRI within psychophysiology.

Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images

Presents a new method to segment brain parenchyma and cerebrospinal fluid spaces automatically in routine axial spin echo multispectral MR images. The algorithm simultaneously incorporates information about anatomical boundaries (shape) and tissue signature (grey scale) using a priori knowledge. The head and brain are divided into four regions and seven different tissue types. Each tissue type c is modeled by a multivariate Gaussian distribution N(mu(c),Sigma(c)). Each region is associated with a finite mixture density corresponding to its constituent tissue types. Initial estimates of tissue parameters {mu(c),Sigma(c )}(c=1,...,7) are obtained from k-means clustering of a single slice used for training. The first algorithmic step uses the EM-algorithm for adjusting the initial tissue parameter estimates to the MR data of new patients. The second step uses a recently developed model of dynamic contours to detect three simply closed nonintersecting curves in the plane, constituting the arachnoid/dura mater boundary of the brain, the border between the subarachnoid space and brain parenchyma, and the inner border of the parenchyma toward the lateral ventricles. The model, which is formulated by energy functions in a Bayesian framework, incorporates a priori knowledge, smoothness constraints, and updated tissue type parameters. Satisfactory maximum a posteriori probability estimates of the closed contour curves defined by the model were found using simulated annealing.