Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions

Three-dimensional reconstruction of magnetic resonance images of carp brain for brain control technology

BACKGROUND

In the field of animal robot control, brain control technology is currently used to achieve control. It is usually necessary to accurately implant brain electrodes into the animal's brain movement area with the help of a brain stereotaxic apparatus, and apply electrical stimulation to achieve control of the animal. The prerequisite for accurate electrode implantation is to study the internal tissues of the carp skull.

NEW METHOD

With the help of 3.0 T magnetic resonance imaging (MRI) instrument and 8_CH MRI scanning coil, carp brain magnetic resonance images was obtained. The visualization tool package VTK and the marching cube algorithm were used for surface rendering, the ray casting algorithm was used for volume rendering and reconstruction.

RESULTS

The three-dimensional reconstruction results could show the carp skull surface contour and internal tissue details, and the measured coordinates after three-dimensional reconstruction of magnetic resonance images could be transformed into three-dimensional positioning coordinates suitable for brain stereotaxic apparatus.

COMPARISON WITH EXISTING METHODS

The three-dimensional reconstruction images based on magnetic resonance could analyze the relative spatial position relationship between the surface structure of the carp's brain and the internal tissue at any angle, and the three-dimensional positioning coordinates of the brain could be obtained quickly and accurately.

CONCLUSIONS

The visualization of carp brain magnetic resonance images based on marching cubes algorithm and ray projection algorithm could obtain ideal reconstruction effects, which could be used in the brain control technology of carp robot.

Brain tissue segmentation using improved kernelized rough-fuzzy C-means with spatio-contextual information from MRI

Segmentation of brain tissues from MRI often becomes crucial to properly investigate any region of the brain in order to detect abnormalities. However, the accurate segmentation of the brain tissues is a challenging task as the different tissue regions are usually imprecise, indiscernible, ambiguous, and overlapping. Additionally, different tissue regions are non-linearly separable. Noises and other artifacts may also present in the brain MRI. Therefore, conventional segmentation techniques may not often achieve desired accuracy. To deal those challenges, a robust kernelized rough fuzzy C-means clustering with spatial constraints (KRFCMSC) is proposed in this article for brain tissue segmentation. Here, the brain tissue segmentation from MRI is considered as a clustering of pixels problem. The basic idea behind the proposed technique is the judicious integration of the fuzzy set, rough set, and kernel trick along with spatial constraints (in the form of contextual information) to increase the clustering (segmentation) performance. The use of rough and fuzzy set theory in the clustering process handles the ambiguity, indiscernibility, vagueness and overlappingness of different brain tissue regions. While, the kernel trick increases the chance of linear separability of the complex regions which are otherwise not linearly separable in its original feature space. In order to deal the noisy pixels, here in the clustering process, the spatio-contextual information is introduced from the neighbouring pixels. Experiments are carried out on different real and synthetic benchmark brain MRI datasets (publicly available from Brainweb, and IBSR) without and with added noise. The performance of the proposed method is compared with five other counterpart clustering based segmentation techniques and evaluated using various supervised as well as unsupervised validity indices such as, overall accuracy, precision, recall, kappa, Jaccard, dice, and kernelized Xie-Beni index. Experimental results justify the superiority and robustness of the proposed method over other state-of-the-art methods on both benchmark real life and synthetic brain MRI datasets with and without added noise. Statistical significance of the better segmentation accuracy can be confirmed from the paired t-test results in favour of the proposed method compared to other counterpart methods.

A novel hybrid MCDM model for performance evaluation of research and technology organizations based on BSC approach

Balanced Scorecard (BSC) is a strategic evaluation tool using both financial and non-financial indicators to determine the business performance of organizations or companies. In this paper, a new integrated approach based on the Balanced Scorecard (BSC) and multi-criteria decision making (MCDM) methods are proposed to evaluate the performance of research centers of research and technology organization (RTO) in Iran. Decision-Making Trial and Evaluation Laboratory (DEMATEL) are employed to reflect the interdependencies among BSC perspectives. Then, Analytic Network Process (ANP) is utilized to weight the indices influencing the considered problem. In the next step, we apply four MCDM methods including Additive Ratio Assessment (ARAS), Complex Proportional Assessment (COPRAS), Multi-Objective Optimization by Ratio Analysis (MOORA), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking of alternatives. Finally, the utility interval technique is applied to combine the ranking results of MCDM methods. Weighted utility intervals are computed by constructing a correlation matrix between the ranking methods. A real case is presented to show the efficacy of the proposed approach.

Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments

Uterine fibroids are benign tumors that can affect female patients during reproductive years. Magnetic resonance-guided focused ultrasound (MRgFUS) represents a noninvasive approach that uses thermal ablation principles to treat symptomatic fibroids. During traditional treatment planning, uterus, fibroids, and surrounding organs at risk must be manually marked on MR images by an operator. After treatment, an operator must segment, again manually, treated areas to evaluate the non-perfused volume (NPV) inside the fibroids. Both pre- and post-treatment procedures are time-consuming and operator-dependent. This paper presents a novel method, based on an advanced direct region detection model, for fibroid segmentation in MR images to address MRgFUS post-treatment segmentation issues. An incremental procedure is proposed: split-and-merge algorithm results are employed as multiple seed-region selections by an adaptive region growing procedure. The proposed approach segments multiple fibroids with different pixel intensity, even in the same MR image. The method was evaluated using area-based and distance-based metrics and was compared with other similar works in the literature. Segmentation results, performed on 14 patients, demonstrated the effectiveness of the proposed approach showing a sensitivity of 84.05 %, a specificity of 92.84 %, and a speedup factor of 1.56× with respect to classic region growing implementations (average values).

BREAST MASS SEGMENTATION ON BREAST MRI USING THE SHAPE-BASED LEVEL SET METHOD

Breast cancer is the most common threat to the health of women. Breast masses are usually important signs of breast cancer. Therefore, a level set method (LSM) with a shape model is proposed to segment breast masses in magnetic resonance imaging (MRI) images in this paper. Since the SM proposed by Chan and Vese does not work well on breast mass segmentation, this paper adds shape knowledge into the segmentation method. We first apply the Chan–Vese LSM to obtain a pre-segmented breast mass and then the position and size of the pre-segmented breast mass are calculated to establish the initial shape model. This paper uses dilation processing to calculate the distance to the shape model contour since it takes into consideration the need to update the level set function. Finally, the proposed method is applied to segment the breast mass in the MRI image of the breast. In order to eliminate noise interference in other regions of the breast, we also address the concept of region of interest (ROI). In the experiment, the proposed method is compared with the Chan–Vese method to prove that the proposed method can achieve better performance. The experimental results show that the breast mass can be correctly segmented by the above mechanism.

Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI

Dynamic MRI has been widely used to track the motion of the tongue and measure its internal deformation during speech and swallowing. Accurate segmentation of the tongue is a prerequisite step to define the target boundary and constrain the tracking to tissue points within the tongue. Segmentation of 2D slices or 3D volumes is challenging because of the large number of slices and time frames involved in the segmentation, as well as the incorporation of numerous local deformations that occur throughout the tongue during motion. In this paper, we propose a semi-automatic approach to segment 3D dynamic MRI of the tongue. The algorithm steps include seeding a few slices at one time frame, propagating seeds to the same slices at different time frames using deformable registration, and random walker segmentation based on these seed positions. This method was validated on the tongue of five normal subjects carrying out the same speech task with multi-slice 2D dynamic cine-MR images obtained at three orthogonal orientations and 26 time frames. The resulting semi-automatic segmentations of a total of 130 volumes showed an average dice similarity coefficient (DSC) score of 0.92 with less segmented volume variability between time frames than in manual segmentations.

The brain MR Image segmentation techniques and use of diagnostic packages

RATIONALE AND OBJECTIVES

This article provides a survey of segmentation methods for medical images. Usually, classification of segmentation methods is done based on the approaches adopted and the domain of application.

MATERIALS AND METHODS

This survey is conducted on the recent segmentation methods used in biomedical image processing and explores the methods useful for better segmentation. A critical appraisal of the current status of semiautomated and automated methods is made for the segmentation of anatomical medical images emphasizing the advantages and disadvantages. Computer-aided diagnosis (CAD) used by radiologists as a second opinion has become one of the major research areas in medical imaging and diagnostic radiology. A picture archiving communication system (PACS) is an integrated workflow system for managing images and related data that is designed to streamline operations throughout the whole patient care delivery process.

RESULTS

By using PACS, the medical image interpretation may be changed from conventional hard-copy images to soft-copy studies viewed on the systems workstations.

CONCLUSION

The automatic segmentations assist the doctors in making quick diagnosis. The CAD need not be comparable to that of physicians, but is surely complementary.

Dynamic changes of territories 17 and 18 during EBV-infection of human lymphocytes

Interphase chromosomes form distinct spatial domains called chromosome territories (CTs). The arrangement of CTs is non-random and correlated with cellular processes such as differentiation. The purpose of this study is to provide some behavior information of CTs during lymphocyte EBV-infection, which is thought to be a general extra-biological model. Three-dimensional fluorescence in situ hybridization (3D-FISH) was performed on human lymphocytes every 24 h over 96 h periods in EBV-infection. Chromosomes 17 and 18 were selected as target territories for similar size and different gene density. The data indicate that the radial position of territories 17 was altered with time, whereas territories 18 showed relative stable localization. The relative CT volume of CTs 18 to 17 also changed with infection. Our study is the first to examine the timely changes of chromatin positioning and folding in EBV-lymphocyte infection. Dynamic changes in position and folding status of target chromosomes reflected an impact of EBV infection on genome stability.

A multiresolution diffused expectation-maximization algorithm for medical image segmentation

In this paper a new method for segmenting medical images is presented, the multiresolution diffused expectation-maximization (MDEM) algorithm. The algorithm operates within a multiscale framework, thus taking advantage of the fact that objects/regions to be segmented usually reside at different scales. At each scale segmentation is carried out via the expectation-maximization algorithm, coupled with anisotropic diffusion on classes, in order to account for the spatial dependencies among pixels. This new approach is validated via experiments on a variety of medical images and its performance is compared with more standard methods.

Fetal Brain MRI: Segmentation and Biometric Analysis of the Posterior Fossa

This paper presents a novel approach to fetal magnetic resonance image segmentation and biometric analysis of the posterior fossa's midline structures. We developed a semi-automatic segmentation method (based on a region growing technique) and tested the algorithm on images of 104 normal fetuses. Using the segmented regions of interest (posterior fossa, vermis, and brainstem), we computed four relative area ratios. Statistical and clinical analysis of our results showed that the relative development of these structures appears to be independent of pregnancy term. In an additional study of 23 pathological cases, one of the four measurements was always significantly different from the corresponding value observed in normal cases.

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.