Carlos Santiago

2D Segmentation Using a Robust Active Shape Model With the EM Algorithm

Statistical shape models have been extensively used in a wide range of applications due to their effectiveness in providing prior shape information for object segmentation problems. The most popular method is the active shape model (ASM). However, accurately fitting the shape model to an object boundary under a cluttered environment is a challenging task. Under such assumptions, the model is often attracted toward invalid observations (outliers), leading to meaningless estimates of the object boundary. In this paper, we propose a novel algorithm that improves the robustness of ASM in the presence of outliers. The proposed framework assumes that both type of observations (valid observations and outliers) are detected in the image. A new strategy is devised for treating the data in different ways, depending on the observations being considered as valid or invalid. The proposed algorithm assigns a different weight to each observation. The shape parameters are recursively updated using the expectation-maximization method, allowing a correct and robust fit of the shape model to the object boundary in the image. Two estimation criteria are considered: 1) the maximum likelihood criterion and 2) the maximum a posteriori criterion that use priors for the unknown parameters. The methods are tested with synthetic and real images, comprising medical images of the heart and image sequences of the lips. The results are promising and show that this approach is robust in the presence of outliers, leading to a significant improvement over the standard ASM and other state-of-the-art methods.

Automatic 3-D Segmentation of Endocardial Border of the Left Ventricle From Ultrasound Images

The segmentation of the left ventricle (LV) is an important task to assess the cardiac function in ultrasound images of the heart. This paper presents a novel methodology for the segmentation of the LV in three-dimensional (3-D) echocardiographic images based on the probabilistic data association filter (PDAF). The proposed methodology begins by initializing a 3-D deformable model either semiautomatically, with user input, or automatically, and it comprises the following feature hierarchical approach: 1) edge detection in the vicinity of the surface (low-level features); 2) edge grouping to obtain potential LV surface patches (mid-level features); and 3) patch filtering using a shape-PDAF framework (high-level features). This method provides good performance accuracy in 20 echocardiographic volumes, and compares favorably with the state-of-the-art segmentation methodologies proposed in the recent literature.

Robust 3D Active Shape Model for the Segmentation of the Left Ventricle in MRI

3D Active shape models use a set of annotated volumes to learn a shape model. The shape model is defined by a fixed number of landmarks at specific locations and takes shape constraints into account in the segmentation process. A relevant problem in which these models can be used is the segmentation of the left ventricle in 3D MRI volumes. In this problem, the annotations correspond to a set of contours that define the LV border at each volume slice. However, each volume has a different number of slices (i.e., a different number of landmarks), which makes model learning difficult. Furthermore, motion artifacts and the large distance between slices make interpolation of voxel intensities a bad choice when applying the learned model to a test volume. These two problems raise the following questions: (1) how can we learn a shape model from volumes with a variable number of slices? and (2) how can we segment a test volume without interpolating voxel intensities between slices? This paper provides an answer to these questions and proposes a 3D active shape model that can be used to segment the left ventricle in cardiac MRI.

Segmentation of the left ventricle in cardiac MRI using a probabilistic data association active shape model

The 3D segmentation of endocardium of the left ventricle (LV) in cardiac MRI volumes is a challenging problem due to the intrinsic properties of this image modality. Typically, the object shape and position are estimated to fit the observed features collected from the images. The difficulty inherent to the LV segmentation in MRI is that the images contain outliers (i.e., observations not belonging to the LV border) due to the presence of other structures. This paper proposes a robust approach based on the Active Shape Model (ASM) that is able to circumvent the above problem. More specifically, the ASM will be guided by probabilistic data association filtering (PDAF) of strokes (i.e. line segments) computed in the neighborhood of the shape model. Thus, the proposed approach, termed herein as ASM-PDAF, will perform the following main steps: 1) edge detection (low-level features) in the vicinity of the shape model; 2) edge grouping (mid-level features) to obtain potential LV strokes; and 3) filtering using a PDAF framework (high-level features) to update the ASM. Experimental results on a public cardiac MRI database show that the proposed approach outperforms previous literature research.

A Robust Deformable Model for 3D Segmentation of the Left Ventricle from Ultrasound Data

This paper presents a novel bottom-up deformable-based model for the segmentation of the Left Ventricle (LV) in 3D ultrasound data. The methodology presented here is based on Probabilistic Data Association Filter (PDAF). The main steps that characterize the proposed approach can be summarized as follows. After a rough initialization given by the user, the following steps are performed: (1) low-level transition edge points are detected based on a prior model for the intensity of the LV, (2) middle-level features or patch formation is accomplished by linking the low-level information, (3) data interpretations are computed (hypothesis) based on the reliability (belonging or not to the LV boundary) of the previously obtained patches, (4) a confidence degree is assigned to each data interpretation and the model is updated taking into account all data interpretations.Results testify the usefulness of the approach in both synthetic and real LV volumes data. The obtained LV segmentations are compared with expert’s manual segmentations, yielding an average distance of 3 mm between them.

A robust active shape model using an expectation-maximization framework

Active shape models (ASM) have been extensively used in object segmentation problems because they constrain the solution, using shape statistics. However, accurately fitting an ASM to an image prone to outliers is difficult and poor results are often obtained. To overcome this difficulty we propose a robust algorithm based on the Expectation-Maximization framework that assigns different weights (confidence degrees) to the observations extracted from the image. This reduces the influence of outliers since they often receive low weights. We tested the proposed algorithm with synthetic and real images (e.g., lip images and cardiac ultrasound images) achieving promising results. The proposed algorithm performs significantly better than the standard ASM implementation.

3D left ventricular segmentation in echocardiography using a probabilistic data association deformable model

The segmentation of the left ventricle (LV) is an important tool to assess the cardiac function in ultrasound images of the heart. This paper presents a methodology for the segmentation of the LV in 3D echocardiography that is based on the probabilistic data association filter (PDAF). The proposed methodology comprises the following feature hierarchical approach: (i) edge detection in the vicinity of the surface (low level features); (ii) edge grouping to obtain potential LV surface patches (mid-level features); and (iii) patch filtering using a shape-PDAF framework (high level features). Also, we propose an automatic procedure to initialize the 3D deformable model. We show that the proposed methodology achieves remarkable accuracy between the obtained contour and expertise medical ground truth and compares favorably with the state-of-the-art segmentation methodologies.

Fast segmentation of the left ventricle in cardiac MRI using dynamic programming

BACKGROUND AND OBJECTIVE The segmentation of the left ventricle (LV) in cardiac magnetic resonance imaging is a necessary step for the analysis and diagnosis of cardiac function. In most clinical setups, this step is still manually performed by cardiologists, which is time-consuming and laborious. This paper proposes a fast system for the segmentation of the LV that significantly reduces human intervention. METHODS A dynamic programming approach is used to obtain the border of the LV. Using very simple assumptions about the expected shape and location of the segmentation, this system is able to deal with many of the challenges associated with this problem. The system was evaluated on two public datasets: one with 33 patients, comprising a total of 660 magnetic resonance volumes and another with 45 patients, comprising a total of 90 volumes. Quantitative evaluation of the segmentation accuracy and computational complexity was performed. RESULTS The proposed system is able to segment a whole volume in 1.5 seconds and achieves an average Dice similarity coefficient of 86.0% and an average perpendicular distance of 2.4 mm, which compares favorably with other state-of-the-art methods. CONCLUSIONS A system for the segmentation of the left ventricle in cardiac magnetic resonance imaging is proposed. It is a fast framework that significantly reduces the amount of time and work required of cardiologists.

A new ASM framework for left ventricle segmentation exploring slice variability in cardiac MRI volumes

Three-dimensional active shape models use a set of annotated volumes to learn a shape model. Using unique landmarks to define the surface models in the training set, the shape model is able to learn the expected shape and variation modes of the segmentation. This information is then used during the segmentation process to impose shape constraints. A relevant problem in which these models are used is the segmentation of the left ventricle in 3D MRI volumes. In this problem, the annotations correspond to a set of contours that define the LV border at each volume slice. However, each volume has a different number of slices (thus, a different number of landmarks), which makes model learning difficult. Furthermore, motion artifacts and the large distance between slices make interpolation of voxel intensities a bad choice when applying the learned model to a test volume. These two problems raise the following questions: (1) how can we learn a shape model from volumes with a variable number of slices? and (2) how can we segment a test volume without interpolating voxel intensities between slices? This paper provides an answer to these questions by proposing a framework to deal with the variable number of slices in the training set and a resampling strategy for the test phase to segment the left ventricle in cardiac MRI volumes with any number of slices. The proposed method was evaluated on a public database with 660 volumes of both healthy and diseased patients, with promising results.

Performance evaluation of point matching algorithms for left ventricle motion analysis in MRI

Finding correspondences between contour points in consecutive frames is crucial for the left ventricular motion analysis. In many medical applications, point correspondences can be determined by using distinctive anatomical features, called anatomical landmarks. However, in the case of cardiac images, these landmarks are scarce and insufficient for the registration. Several methods have been proposed using semi-landmarks, but this may lead to incorrect correspondences. This paper proposes and evaluates the performance of three point matching algorithm. Results show that the matching by resampling method leads to the best overall correspondences and compares favorably with the performance of a state of the art shape alignment algorithm [9].