Su Ruan

Brain tissue classification of magnetic resonance images using partial volume modeling

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, the authors 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 DAgostino-Pearson normality test is used to assess the risk or 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.

A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images

A framework of fuzzy information fusion is proposed in this paper to automatically segment tumor areas of human brain from multispectral magnetic resonance imaging (MRI) such as T1-weighted, T2-weighted and proton density (PD) images. A priori knowledge about tumors described by radiology experts for different types of MRI are very helpful to guide a automatic and a precise segmentation. However, the terminology used by radiology experts are variable in term of image signal. In order to benefit of these descriptions, we propose to modellize them by fuzzy models. One fuzzy model is built for one type of MRI sequence. The segmentation is finally based on a fusion of different fuzzy information obtained from different types of MRI images. Our algorithm consists of four stages: the registration of multispectral MR images, the creation of fuzzy models describing the characteristics of tumor, the fusion based on fuzzy fusion operators and the adjustment by fuzzy region growing based on fuzzy connecting. The comparison between the obtained results and the hand-tracings of a radiology expert shows that the proposed algorithm is efficient. An average probability of correct detection 96% and an average probability of false detection 5% are obtained through studies of four patients.

On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series.

The aim of this paper is to present an exploratory data-driven strategy based on Unsupervised Fuzzy Clustering Analysis (UFCA) and its potential for fMRI data analysis in the temporal domain. The a priori definition of the number of clusters is addressed and solved using heuristics. An original validity criterion is proposed taking into account data geometry and the partition Membership Functions (MFs). From our simulations, this criterion is shown to outperform other indices used in the literature. The influence of the fuzziness index was studied using simulated activation combined with real life noise data acquired from subjects under a resting state. Receiver Operating Characteristics (ROC) methodology is implemented to assess the performance of the proposed UFCA with respect to the fuzziness index. An interval of choice around 2, a value widely used in FCA, is shown to yield the best performance.

Fuzzy Markovian segmentation in application of magnetic resonance images

In this paper, we present a fuzzy Markovian method for brain tissue segmentation from magnetic resonance images. Generally, there are three main brain tissues in a brain dataset: gray matter, white matter, and cerebrospinal fluid. However, due to the limited resolution of the acquisition system, many voxels may be composed of multiple tissue types (partial volume effects). The proposed method aims at calculating a fuzzy membership in each voxel to indicate the partial volume degree, which is statistically modeled. Since our method is unsupervised, it first estimates the parameters of the fuzzy Markovian random field model using a stochastic gradient algorithm. The fuzzy Markovian segmentation is then performed automatically. The accuracy of the proposed method is quantitatively assessed on a digital phantom using an absolute average error and qualitatively tested on real MRI brain data. A comparison with the widely used fuzzy C-means algorithm is carried out to show numerous advantages of our method.

TUMOR SEGMENTATION FROM A MULTISPECTRAL MRI IMAGES BY USING SUPPORT VECTOR MACHINE CLASSIFICATION

The goal of this paper is to present a supervised system aimed at tracking the tumor volume during a therapeutic treatment from multispectral MRI volumes. Four types of MRI are used in our study: T1, T2, proton density (PD) and fluid attenuated inversion recovery (FLAIR). For decreasing the processing time, the proposed method employs a multi-scale scheme to identify firstly the abnormal field and extract then the tumor region. Both steps use support vector machines (SVMs). The training is carried out only on the first MRI examination (at the beginning of the treatment). The tracking process at the time point t takes the tumor region obtained in the examination at t-1 as its initialization. Only the second step is performed for others examinations to extract the tumor region. The results obtained show that the proposed system achieves promising results in terms of effectiveness and time consuming.

Fuzzy kappa for the agreement measure of fuzzy classifications

In this paper, we propose an assessment method of agreement between fuzzy sets, called fuzzy Kappa which is deduced from the concept of Cohens Kappa statistic. In fuzzy case, the Cohens Kappa coefficient can be calculated generally by transforming the fuzzy sets into some crisp @a-cut subsets. While the proposed fuzzy Kappa allows to directly evaluate an overall agreement between two fuzzy sets. Hence, it is an efficient agreement measure between a given fuzzy ground truth or reference and a result of fuzzy classification or fuzzy segmentation. Based on membership function, we define its agreement function and its probability distribution to formulate the deduction of the expectation agreement. So the fuzzy Kappa is calculated from the proportion of the observed agreement and the agreement expected by chance. All the definitions and deductions are detailed in this paper. Both Cohens Kappa and the fuzzy Kappa are then used to evaluate the agreement between a fuzzy classification of brain tissues on MRI images and its ground truth. A comparison of the two types of Kappa coefficient is carried out and shows the advantage of the fuzzy Kappa and some limitations of Cohens Kappa in the fuzzy case.

Knowledge-based segmentation and labeling of brain structures from MRI images

In this paper, we propose a new knowledge-based method illustrated in the context of segmentation, which labels internal brain structures viewed by magnetic resonance imaging (MRI). In order to improve the accuracy of the labeling, we introduce a fuzzy model of regions of interest (ROI) by analogy with the electrostatic potential distribution, to represent more appropriately the knowledge of distance, shape and relationship of structures. The knowledge is mainly derived from the Talairach stereotaxic atlas. The labeling is achieved by the regionwise labeling using genetic algorithms (GAs), followed by a voxelwise amendment using parallel region growing. The fuzzy model is used both to design the fitness function of GAs, and to guide the region growing. The performance of our proposed method has been quantitatively validated by six indices with respect to manually labeled images.

Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence

In this paper, the multi-kernel SVM (Support Vector Machine) classification, integrated with a fusion process, is proposed to segment brain tumor from multi-sequence MRI images (T2, PD, FLAIR). The objective is to quantify the evolution of a tumor during a therapeutic treatment. As the procedure develops, a manual learning process about the tumor is carried out just on the first MRI examination. Then the follow-up on coming examinations adapts the learning automatically and delineates the tumor. Our method consists of two steps. The first one classifies the tumor region using a multi-kernel SVM which performs on multi-image sources and obtains relative multi-result. The second one ameliorates the contour of the tumor region using both the distance and the maximum likelihood measures. Our method has been tested on real patient images. The quantification evaluation proves the effectiveness of the proposed method.

Phantom-based performance evaluation: Application to brain segmentation from magnetic resonance images

This paper presents a new technique for assessing the accuracy of segmentation algorithms, applied to the performance evaluation of brain editing and brain tissue segmentation algorithms for magnetic resonance images. We propose performance evaluation criteria derived from the use of the realistic digital brain phantom Brainweb. This ground truth allows us to build distance-based discrepancy features between the edited brain or the segmented brain tissues (such as cerebro-spinal fluid, grey matter and white matter) and the phantom model, taken as a reference. Furthermore, segmentation errors can be spatially determined, and ranged in terms of their distance to the reference. The brain editing method used is the combination of two segmentation techniques. The first is based on binary mathematical morphology and a region growing approach. It represents the initialization step, the results of which are then refined with the second method, using an active contour model. The brain tissue segmentation used is based on a Markov random field model. Segmentation results are shown on the phantom for each method, and on real magnetic resonance images for the editing step; performance is evaluated by the new distance-based technique and corroborates the effective refinement of the segmentation using active contours. The criteria described here can supersede biased visual inspection in order to compare, evaluate and validate any segmentation algorithm. Moreover, provided a ground truth is given, we are able to determine quantitatively to what extent a segmentation algorithm is sensitive to internal parameters, noise, artefacts or distortions.

Unsupervised fuzzy clustering analysis of fMRI series

The potential of an fMRI data analysis strategy using a paradigm independent unsupervised fuzzy clustering is presented. The power of the method is demonstrated to discriminate different types of responses without prior knowledge relative to the paradigm in contrast with classic statistical methods which require a priori knowledge about the stimulus. Different performance measure functions are proposed to solve the cluster validity problem and to detect the number of substructures present in the data. The results are presented on both simulated data (with Contrast to Noise Ratios similar to those observed in fMRT), and in vivo EPI data for a motor paradigm. This new data analysis procedure may be very useful for optimization of data analysis and quality in fMRI.