Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.

Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)

A novel interactive approach called Enhanced Intelligent Scissors (EIS) is presented for segmenting regions of interest in medical images. The proposed interactive medical image segmentation algorithm addresses the issues associated with segmenting medical images and allows for fast, robust, and flexible segmentation without requiring accurate manual tracing. A robust complex wavelet phase-based representation is used as an external local cost to address issues associated with contrast non-uniformities and noise typically found in medical images. The boundary extraction problem is formulated as a Hidden Markov Model (HMM) and the novel approach to the second-order Viterbi algorithm with state pruning is used to find the optimal boundary in a robust and efficient manner based on the extracted external and internal local costs, thus handling much inexact user boundary definitions than existing methods. Experimental results using MR and CT images show that the proposed algorithm achieves accurate segmentation in medical images without the need for accurate boundary definition as per existing Intelligent Scissors methods. Furthermore, usability testing indicate that the proposed algorithm requires significantly less user interaction than Intelligent Scissors.

Segmentation of MR images for computer-assisted surgery of the lumbar spine

This paper describes a segmentation algorithm designed to separate bone from soft tissue in magnetic resonance (MR) images developed for computer-assisted surgery of the spine. The algorithm was applied to MR images of the spine of healthy volunteers. Registration experiments were carried out on a physical model of a spine generated from computed tomography (CT) data of a surgical patient. Segmented CT, manually segmented MR and MR images segmented using the developed algorithm were compared. The algorithm performed well at segmenting bone from soft tissue on images taken of healthy volunteers. Registration experiments showed similar results between the CT and MR data. The MR data, which were manually segmented, performed worse on visual verification experiments than both the CT and semi-automatic segmented data. The algorithm developed performs well at segmenting bone from soft tissue in MR images of the spine as measured using registration experiments.

Current methods in medical image segmentation

Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. We present a critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images. Terminology and important issues in image segmentation are first presented. Current segmentation approaches are then reviewed with an emphasis on the advantages and disadvantages of these methods for medical imaging applications. We conclude with a discussion on the future of image segmentation methods in biomedical research.

Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models--'snakes'

Many diagnostic and therapeutic procedures depend on medical images. In order to overcome imperfections of the obtained images, which are due to the acquisition process, and to extract new information from the available images, many techniques have been developed. In this study, a new method of image segmentation and 3D reconstruction based on active contour models ('snakes') was applied in analyzing computed tomography (CT) images in patients with acute head trauma. Using this method, lesion to brain (LBR) and ventricle to brain ratio (VBR) parameters, as well as 3D reconstruction of traumatic lesion, was obtained accurately. In our study group, 215 patients (mean age 42.4+/-23.5 years, 138/215 (64.2%) males) were included. Among them, 72 (33.5%) did not survive during hospitalisation in the Emergency Department. LBR correlated with the Glasgow Coma Score and the intrahospital outcome (r=-0.457 and r=0.515, respectively). Besides, non-survivors had greater LTB values (0.042+/-0.034) than survivors (0.005+/-0.011). However, VBR did not correlate with these clinical parameters. In addition, LBR was significantly higher in the patients with other pathologic CT findings. The proposed methodology, based on extracting maximum information from available CT scans, could be a basis for further medical decision making in patients with acute head trauma.

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.

Normal brain volume measurements using multispectral MRI segmentation

The performance of a supervised k-nearest neighbor (kNN) classifier and a semisupervised fuzzy c-means (SFCM) clustering segmentation method are evaluated for reproducible measurement of the volumes of normal brain tissues and cerebrospinal fluid. The stability of the two segmentation methods is evaluated for (a) operator selection of training data, (b) reproducibility during repeat imaging sessions to determine any variations in the sensor performance over time, (c) variations in the measured volumes between different subjects, and (d) variability with different imaging parameters. The variations were found to be dependent on the type of measured tissue and the operator performing the segmentations. The variability during repeat imaging sessions for the SFCM method was < 3%. The absolute volumes of the brain matter and cerebrospinal fluid between subjects varied quite large, ranging from 9% to 13%. The intraobserver and interobserver reproducibility for SFCM were < 4% for the soft tissues and 6% for cerebrospinal fluid. The corresponding results for the kNN segmentation method were higher compared to the SFCM method.

Three- to five-dimensional biomedical multisensor imaging for the assessment of neurological (dys) function

This report describes techniques and protocols implemented at the Geneva Canton University Hospitals (HUG) for the combination of various biomedical imaging modalities and sensors including electromagnetic tomography, to study, assess, and localize neurological (dys) function. The interest for this combination stems from the broad variety of information brought out by (functional) magnetic resonance imaging, magnetic resonance spectroscopy, computed tomography, single-photon emission tomography, positron emission tomography, and electromagnetic tomography. Combining these data allows morphology, metabolism, and function to be studied simultaneously, the complementary nature of the information from these modalities becoming evident when studying pathologies reflected by metabolic or electrophysiologic dysfunctions. Compared with other current multimodality approaches, the one at the HUG is totally compatible with both clinical and research protocols, and efficiently addresses the multidimensional registration and visualization issues. It also smoothly integrates electrophysiology and related data as fully featured modalities.

Cerebral magnetic resonance image segmentation using data fusion

OBJECTIVE

A semiautomated method is described for segmenting dual echo MR head scans into gray and white matter and CSF. The method is applied to brain scans of 80 healthy children and adolescents.

MATERIALS AND METHODS

A probabilistic data fusion equation was used to combine simultaneously acquired T2-weighted and proton density head scans for tissue segmentation. The fusion equation optimizes the probability of a voxel being a particular tissue type, given the corresponding probabilities from both images. The algorithm accounts for the intensity inhomogeneities present in the images by fusion of local regions of the images.

RESULTS

The method was validated using a phantom (agarose gel with iron oxide particles) and hand-segmented images. Gray and white matter volumes for subjects aged 20-30 years were close to those previously published. White matter and CSF volume increased and gray matter volume decreased significantly across ages 4-18 years. White matter, gray matter, and CSF volumes were larger for males than for females. Males and females showed similar change of gray and white matter volumes with age.

CONCLUSION

This simple, reliable, and valid method can be employed in clinical research for quantification of gray and white matter and CSF volumes in MR head scans. Increase in white matter volume may reflect ongoing axonal growth and myelination, and gray matter reductions may reflect synaptic pruning or cell death in the age span of 4-18 years.

A technique for single-channel MR brain tissue segmentation: application to a pediatric sample

A segmentation method is presented for gray matter, white matter, and cerebrospinal fluid (CSF) in thin-sliced single-channel brain magnetic resonance (MR) scans. The method is based on probabilistic modeling of intensity distributions and on a region growing technique. Interrater and intrarater reliabilities for the method were high, and comparison with phantom studies and hand-traced results from an experienced rater indicated good validity. The method was designed to account for spatially dependent image intensity inhomogeneities. Segmentation of MR brain scans of 105 (56 male and 49 female) healthy children and adolescents showed that although the total brain volume was stable over age 4-18, white matter increased and gray matter decreased significantly. There were no sex differences in total gray and white matter growth after correction for total brain volume. White matter volume increased the most in superior and posterior regions and laterality effects were seen in hemisphere tissue volumes. These findings are consistent with other reports, and further validate the segmentation technique.

Three-dimensional anatomical model-based segmentation of MR brain images through Principal Axes Registration

Model-based segmentation and analysis of brain images depends on anatomical knowledge which may be derived from conventional atlases. Classical anatomical atlases are based on the rigid spatial distribution provided by a single cadaver. Their use to segment internal anatomical brain structures in a high-resolution MR brain image does not provide any knowledge about the subject variability, and therefore they are not very efficient in analysis. We present a method to develop three-dimensional computerized composite models of brain structures to build a computerized anatomical atlas. The composite models are developed using the real MR brain images of human subjects which are registered through the Principal Axes Transformation. The composite models provide probabilistic spatial distributions, which represent the variability of brain structures and can be easily updated for additional subjects. We demonstrate the use of such a composite model of ventricular structure to help segmentation of the ventricles and Cerebrospinal Fluid (CSF) of MR brain images. In this paper, a composite model of ventricles using a set of 22 human subjects is developed and used in a model-based segmentation of ventricles, sulci, and white matter lesions. To illustrate the clinical usefulness, automatic volumetric measurements on ventricular size and cortical atrophy for an additional eight alcoholics and 10 normal subjects were made. The volumetric quantitative results indicated regional brain atrophy in chronic alcoholics.

MRI segmentation: methods and applications

The current literature on MRI segmentation methods is reviewed. Particular emphasis is placed on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Image pre-processing and registration are discussed, as well as methods of validation. The application of MRI segmentation for tumor volume measurements during the course of therapy is presented here as an example, illustrating problems associated with inter- and intra-observer variations inherent to supervised methods.

Multispectral analysis of the brain using magnetic resonance imaging

The authors demonstrate an improved differentiation of the most common tissue types in the human brain and surrounding structures by quantitative validation using multispectral analysis of magnetic resonance images. This is made possible by a combination of a special training technique and an increase in the number of magnetic resonance channel images with different pulse acquisition parameters. The authors give a description of the tissue-specific multivariate statistical distributions of the pixel intensity values and discuss how their properties may be explored to improve the statistical modeling further. A statistical method to estimate the tissue-specific longitudinal and transverse relaxation times is also given. It is concluded that multispectral analysis of magnetic resonance images is a valuable tool to recognize the most common normal tissue types in the brain and surrounding structures.

Parameter estimation and tissue segmentation from multispectral MR images

A statistical method is developed to classify tissue types and to segment the corresponding tissue regions from relaxation time T(1 ), T(2), and proton density P(D) weighted magnetic resonance images. The method assumes that the distribution of image intensities associated with each tissue type can be expressed as a multivariate likelihood function of three weighted signal intensity values (T(1), T(2), P(D)) at each location within that tissue regions. The method further assumes that the underlying tissue regions are piecewise contiguous and can be characterized by a Markov random field prior. In classifying the tissue types, the method models the likelihood of realizing the images as a finite multivariate-mixture function. The class parameters associated with the tissue types (i.e. the weighted intensity means, variances and correlation coefficients of the multivariate function, as well as the number of voxels within regions of the tissue types of are estimated by maximum likelihood. The estimation fits the class parameters to the image data via the expectation-maximization algorithm. The number of classes associated with the tissue types is determined by the information criterion of minimum description length. The method segments the tissue regions, given the estimated class parameters, by maximum a posteriori probability. The prior is constructed by the tissue-region membership of the first- and second-order neighborhood. The method is tested by a few sets of T(1), T(2), and P(D) weighted images of the brain acquired with a 1.5 Tesla whole body scanner. The number of classes and the associated class parameters are automatically estimated. The regions of different brain tissues are satisfactorily segmented.

Using kriging for 3D medical imaging

We describe our implementation of kriging for interpolation of scalar values in three-dimensional medical image surface rendering and for slice interpolation. Kriging is an interpolation technique developed in the geosciences for estimating ore deposit spatial distributions. Kriging has been mathematically proven to be the best (statistically optimal) linear unbiased estimation technique for spatially distributed data. As a byproduct of the kriging technique, kriging can calculate the estimation error for the interslice interpolated values. Kriging also offers the potential for quantifying the interpolation error in slices computed by the estimation technique. This paper presents the initial results obtained using kriging for the pre-processing operations of slice interpolation by slice-value interpolation and interpolating voxel values during iso-surface extraction. We found that kriging is an accurate interpolation technique for surface rendering and for slice interpolation. Our results indicate that kriging can duplicate the rendering results obtained with other interpolation techniques and it offers the potential for providing visually "better" images than are obtained using the other interpolation techniques we tested.