Homogeneity Measures for Multiphase Level Set Segmentation of Brain MRI

This paper presents a new homogeneity measure for variational segmentation with multiple level set functions. We propose to modify the quadratic homogeneity measure to trade off the convexity of the function against a faster rate of convergence. We tested in two series of experiments the performance of this new homogeneity force at converging to appropriate partitioning of brain MRI data sets, over a large range of image spatial resolution and image quality, in terms of tissue homogeneity and contrast

Variational segmentation framework in prolate spheroidal coordinates for 3D real-time echocardiography

This paper presents a new formulation of a deformable model segmentation in prolate spheroidal coordinates for segmentation of 3D cardiac echocardiography data. The prolate spheroidal coordinate system enables a representation of the segmented surface with descriptors specifically adapted to the "ellipsoidal" shape of the ventricle. A simple data energy term, based on gray-level information, guides the segmentation. The segmentation framework provides a very fast and simple algorithm to evolve an initial ellipsoidal object towards the endocardial surface of the myocardium with near real-time deformations. With near real-time performance, additional constraints on landmark points, can be used interactively to prevent leakage of the surface

Assessment of visual quality and spatial accuracy of fast anisotropic diffusion and scan conversion algorithms for real-time three-dimensional spherical ultrasound

Three-dimensional ultrasound machines based on matrix phased-array transducers are gaining predominance for real-time dynamic screening in cardiac and obstetric practice. These transducers array acquire three-dimensional data in spherical coordinates along lines tiled in azimuth and elevation angles at incremental depth. This study aims at evaluating fast filtering and scan conversion algorithms applied in the spherical domain prior to visualization into Cartesian coordinates for visual quality and spatial measurement accuracy. Fast 3d scan conversion algorithms were implemented and with different order interpolation kernels. Downsizing and smoothing of sampling artifacts were integrated in the scan conversion process. In addition, a denoising scheme for spherical coordinate data with 3d anisotropic diffusion was implemented and applied prior to scan conversion to improve image quality. Reconstruction results under different parameter settings, such as different interpolation kernels, scaling factor, smoothing options, and denoising, are reported. Image quality was evaluated on several data sets via visual inspections and measurements of cylinder objects dimensions. Error measurements of the cylinder's radius, reported in this paper, show that the proposed fast scan conversion algorithm can correctly reconstruct three-dimensional ultrasound in Cartesian coordinates under tuned parameter settings. Denoising via three-dimensional anisotropic diffusion was able to greatly improve the quality of resampled data without affecting the accuracy of spatial information after the modification of the introduction of a variable gradient threshold parameter

State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities

Segmentation of medical images is an important step in various applications such as visualization, quantitative analysis and image-guided surgery. Numerous segmentation methods have been developed in the past two decades for extraction of organ contours on medical images. Low-level segmentation methods, such as pixel-based clustering, region growing, and filter-based edge detection, require additional pre-processing and post-processing as well as considerable amounts of expert intervention or information of the objects of interest. Furthermore the subsequent analysis of segmented objects is hampered by the primitive, pixel or voxel level representations from those region-based segmentation. Deformable models, on the other hand, provide an explicit representation of the boundary and the shape of the object. They combine several desirable features such as inherent connectivity and smoothness, which counteract noise and boundary irregularities, as well as the ability to incorporate knowledge about the object of interest. However, parametric deformable models have two main limitations. First, in situations where the initial model and desired object boundary differ greatly in size and shape, the model must be re-parameterized dynamically to faithfully recover the object boundary. The second limitation is that it has difficulty dealing with topological adaptation such as splitting or merging model parts, a useful property for recovering either multiple objects or objects with unknown topology. This difficulty is caused by the fact that a new parameterization must be constructed whenever topology change occurs, which requires sophisticated schemes. Level set deformable models, also referred to as geometric deformable models, provide an elegant solution to address the primary limitations of parametric deformable models. These methods have drawn a great deal of attention since their introduction in 1988. Advantages of the contour implicit formulation of the deformable model over parametric formulation include: (1) no parameterization of the contour, (2) topological flexibility, (3) good numerical stability, (4) straightforward extension of the 2D formulation to n-D. Recent reviews on the subject include papers from Suri. In this chapter we give a general overview of the level set segmentation methods with emphasize on new frameworks recently introduced in the context of medical imaging problems. We then introduce novel approaches that aim at combining segmentation and registration in a level set formulation. Finally we review a selective set of clinical works with detailed validation of the level set methods for several clinical applications

Classification of Micro Array Protein Crystal Images With Laplacian Pyramidal Filters and Neural Networks

This project is part of the Northeast Structural Genomics Consortium (NESG). The goal of this consortium is to develop efficient and integrated technologies for high-throughput (HTP) protein production and 3D structure determination. This project focuses on the design of an image analysis system to classify protein crystal structures in a production oriented environment. The method presented performs classification of microscopic images as protein precipitates or crystals versus clear solutions. Preliminary experiments are presented, which provided high classification accuracy on large datasets of protein crystallization experiments using expert classification for ground truth

Multi-Phase Three-Dimensional Level Set Segmentation of Brain MRI

Segmentation of cortical structures on clinical brain MRI data is applied for clinical study of depression on elderly subjects. Segmentation of clinical MRI data is more challenging than with simulated data due to intensity overlap among cortical structures and non-homogeneity within each tissue

Segmentation and quantitative evaluation of brain MRI data with a multi-phase three-dimensional implicit deformable model

Segmentation of three-dimensional anatomical brain images into tissue classes has applications in both clinical and research settings. This paper presents the implementation and quantitative evaluation of a four-phase three-dimensional active contour implemented with a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to speed up numerical computation and avoid the need for a priori information. This random initialization ensures robustness of the method to variation of user expertise, biased a priori information and errors in input information that could be influenced by variations in image quality. Experimentation on three MRI brain data sets showed that an optimal partitioning successfully labeled regions that accurately identified white matter, gray matter and cerebrospinal fluid in the ventricles. Quantitative evaluation of the segmentation was performed with comparison to manually labeled data and computed false positive and false negative assignments of voxels for the three organs. We report high accuracy for the two comparison cases. These results demonstrate the efficiency and flexibility of this segmentation framework to perform the challenging task of automatically extracting brain tissue volume contours

Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study

This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: “idealized” intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method

Real-Time Segmentation of 4D Ultrasound by Active Geometric Functions

Four-dimensional ultrasound based on matrix phased array transducers can capture the complex 4D cardiac motion in a complete and real-time fashion. However, the large amount of information residing in 4D ultrasound scans and novel applications under interventional settings pose a big challenge in efficiency for workflow and computer-aided diagnostic algorithms such as segmentation. In this context, a novel formulation framework of the minimal surface problem, called active geometric functions (AGF), is proposed to reach truly real-time performance in segmenting 4D ultrasound data. A specific instance of AGF based on finite element modeling and Hermite surface descriptors was implemented and evaluated on 35 4D ultrasound data sets with a total of 425 time frames. Quantitative comparison to manual tracing showed that the proposed method provides LV contours close to manual segmentation and that the discrepancy was comparable to inter-observer tracing variability. The ability of such realtime segmentation will not only facilitate the diagnoses and workflow, but also enables novel applications such as interventional guidance and interactive image acquisition with online segmentation

Quantification of LV Volumes with 4D Real-Time Echocardiography

This paper presents a new 4D (3D+Time) expansion of echocardiographic volumes on complex exponential wavelet-like basis functions called Brushlets. Brushlet functions offer good localization in time and frequency and decompose a signal into distinct patterns of oriented textures, invariant to intensity and contrast range. Automatic left ventricle (LV) endocardial border detection is carried out in the transform domain where speckle noise is attenuated while cardiac structure location is preserved. Quantitative validation and clinical applications of this new spatio-temporal analysis tool are reported with results on phantoms and clinical data sets to quantify LV volumes and ejection fraction