Cardiac image processing techniques

Pose robust face tracking by combining view-based AAMs and temporal filters

PurposeRepresentation of anatomy appearance is one of the key problems in medical image analysis. An appearance model represents the anatomies with parametric forms, which are then vectorised for prior learning, segmentation and classification tasks.MethodsWe propose a part-based parametric appearance model we refer to as a deformable appearance pyramid (DAP). The parts are delineated by multi-scale local feature pyramids extracted from an image pyramid. Each anatomy is represented by an appearance pyramid, with the variability within a population approximated by local translations of the multi-scale parts and linear appearance variations in the assembly of the parts. We introduce DAPs built on two types of image pyramids, namely Gaussian and wavelet pyramids, and present two approaches to model the prior and fit the model, one explicitly using a subspace Lucas–Kanade algorithm and the other implicitly using the supervised descent method (SDM).ResultsWe validate the performance of the DAP instances with difference configurations on the problem of lumbar spinal stenosis for localising the landmarks and classifying the pathologies. We also compare them with classic methods such as active shape models, active appearance models and constrained local models. Experimental results show that the DAP built on wavelet pyramids and fitted with SDM gives the best results in both landmark localisation and classification.ConclusionA new appearance model is introduced with several configurations presented and evaluated. The DAPs can be readily applied for other clinical problems for the tasks of prior learning, landmark detection and pathology classification.

Statistical models of the shape and appearance of deformable objects have become widely used in Computer Vision and Medical Image Analysis. Here we give an overview of such models and of two efficient algorithms for matching such models to new images (Active Shape Models and Active Appearance Models). We also describe recent work on automatically constructing such models from minimally labelled training images.KeywordsMinimum Description LengthFace ModelActive Appearance ModelActive Shape ModelMedical Image AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Segmentation of diseased lungs in CT images is a nontrivial problem. As Active Appearance Model (AAM) has been applied effectively in this field, we propose a new approach for the construction of traditional AAM to segment the lung fields more accurately and efficiently: Matrixes based AAM (MatAAM). MatAAM is based on two-dimensional image matrixes rather one-dimensional vectors. Its appearance matrix does not need to be transformed into a vector prior to computing the appearance parameter. Instead, a covariance matrix is constructed directly using the normalized appearance matrixes and its eigenvectors are derived for the appearance parameter. The experiment results were compared to other landmark-based methods: Snake, Active Shape Model (ASM), AAM and several modified versions of them. For segmentation of lungs especially diseased lungs, MatAAM performed a superior result in both precision and efficiency.

Feature competition and partial sparse shape modeling for cardiac image sequences segmentation

Active Appearance Models (AAM), which have been recently introduced by Cootes et al., describe the shape of objects and gray level appearance from a set of example images. An AAM is created from user-placed contours defining the shape of objects of interest in each training image. The information about shape changes observed in the training set is used to model the shape variation. Principle component analysis (PCA) is utilized to model gray level variation observed in the training set. The resulting model describes objects as a linear combination of eigen vectors both in shape and gray levels applied to the mean image. The main purpose of this work is to investigate the clinical potential of AAMs for segmentation of cardiovascular MR images acquired in routine clinical practice. An AAM was constructed using 102 end- diastolic short-axis cardiac MR images at the papillary muscle level from normals and patients with varying pathologies. The resulting AAM is a compact representation consisting of a mean image and a limited number of coefficients of eigen vectors, representing 97% of shape and gray level variation observed in the training set. The segmentation performance is tested in 60 end-diastolic short-axis cardiac MR images from different patients.

Feature point detection is an important pre-processing step for quantitative evaluation of facial paralysis. Since the conventional methods such as active shape model (ASM) or active appearance model (AAM) are trained by using normal face and they are not possible to detect the feature points accurately for the face with paralysis. In this paper, we propose an automatic and accurate feature point detection method for quantitative evaluation of facial paralysis using deep convolutional neural networks (DCNN). The proposed method consists of two steps. We first use AAM for initial feature point detection. In the second step, a patch with the detected point at the center is used as an input of DCNN for refinement. Experiments demonstrated that the proposed method can significantly improve the detection accuracy of the conventional AAM.

In this work we automatically segment the left ventricle (LV) in cardiac MR images in the end-diastole (ED) and end-systole (ES) phases using a novel approach that combines statistical and deterministic deformable models. A 3D Active Appearance Model (AAM) is used to segment the ED phase. The AAM texture model is trained on radial samples from gradient magnitude images to make the fitting process faster and more discriminative. A trained ED-to-ES shape correspondence model is used to map a given ED shape to an ES shape. Once the AAM model converges to a shape in ED, the correspondence model is used to get an approximate ES shape. We segment the LV in the ES phase by first fitting a deformable superquadric to the AAM converged shape (in ED) using data range forces and then tracking the LV using image and data range forces (for the ES shape obtained from correspondence model). We test our approach by performing leave-one-out training on a 35 patient datasets. The data comprises 19 normal patients and 16 patients having heart abnormalities (cardiomyopathy and myocardial infarction). The composition makes it a challenging data collection with significant shape variation. The performance of our method is evaluated by measuring the mismatch between automatically segmented and expert delineated contours using the Mean Perpendicular Distance (MPD) and Dice metrics. The average MPD is 2.6mm for ED and 3.7mm for ES (error mostly towards the apex and base). The average Dice is 0.9 for ED and 0.8 for ES. These results show good potential for clinical use.

Facial feature extraction attracts much research interest due to its importance value in applications including human-computer interaction and visual surveillance. Model-based algorithms such as active shape models and active appearance models are efficient for facial feature extraction in color images. However, the complexity of the images and the local nature of active shape models optimization presents a challenge to the localization problem . We propose a new bottom-up method in model fitting stage of ASM, which decompose the whole face model search into several sub-feature search.lt enables the models to be combined with other feature location methods as to get good initialization and lead to more accurate estimates. We demonstrate the experiment results of successfully applying this schema on facial feature extraction.

Active Appearance Models (AAMs) and Constrained Local Models (CLMs) are classical approaches for model-based image segmentation in medical image analysis. AAMs consist of global statistical models of shape and appearance, are known to be hard to fit, and often suffer from insufficient generalization capabilities in case of limited training data. CLMs model appearance only for local patches and relax or completely remove the global appearance constraint. They are, therefore, much easier to optimize but in certain cases they lack the robustness of AAMs. In this paper, we present a framework for patch-based active appearance modeling, which elegantly combines strengths of AAMs and CLMs. Our models provide global shape and appearance constraints and we make use of recent methodological advances from computer vision for efficient joint optimization of shape and appearance parameters during model fitting. Furthermore, the insufficient generalization abilities of those global models are tackled by incorporating and extending a recent approach for learning representative statistical shape models from small training populations. We evaluate our approach on publicly available chest radiographs and cardiac MRI data. The results show that the proposed framework leads to competitive results in terms of segmentation accuracy for challenging multi-object segmentation problems even when only few training samples are available.

HomeRadiologyVol. 160, No. 1 PreviousNext ReviewsCardiac Imaging and Image ProcessingDavid S. MartinDavid S. MartinDavid S. MartinPublished Online:Jul 1 1986https://doi.org/10.1148/radiology.160.1.220MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Article HistoryPublished in print: July 1986 FiguresReferencesRelatedDetailsRecommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 160, No. 1 Metrics Altmetric Score PDF download

Heart disease is a fatal disease that causes significant mortality rates worldwide. The accurate and early detection of heart diseases is the most challenging task to save valuable lives. To avoid these issues, the Deep Convolutional Generative Adversarial Network (DCGAN) model is proposed that generates synthetic cardiac images. Here, two types of heart disease datasets such as the Sunnybrook Cardiac Dataset (SCD) and the Automated Cardiac Diagnosis Challenge (ACDC) dataset are selected to choose real cardiac images for implementation. The quality and consistency of the cardiac images are enhanced by preprocessed real cardiac images. In the DCGAN model, the generator is used for converting real cardiac images into synthetic images and the discriminator is responsible for differentiating real and synthetic cardiac images by binary classification decisions. To enhance the model's robustness and generalization ability, diverse augmentation techniques are implemented. The VGG16 model is applied in this paper for the image classification task and fine-tuned its parameters to optimize model convergence. For experimental validation, some of the significance metrics such as accuracy, precision, diagnostic time, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), false positive rate (FPR), false negative rate (FNR), and mean squared error (MSE) are utilized. The extensive experimental evaluations are carried out based on this metrics and attained a performance rate of the proposed method as 98.83%, 1.17%, 3.2%, 41.78, 4.52, 0.932, and 1.6s from accuracy, FPR, FNR, PSNR, MSE, SSIM, and diagnostic time, respectively. The experimental evaluation results demonstrate that the proposed heart disease diagnosis model attains superior performances than state-of-the-art methods.

Active appearance models (AAMs) have been successfully used for a variety of segmentation tasks in medical image analysis. However, gross disturbances of objects can occur in routine clinical setting caused by pathological changes or medical interventions. This poses a problem for AAM-based segmentation, since the method is inherently not robust. In this paper, a novel robust AAM (RAAM) matching algorithm is presented. Compared to previous approaches, no assumptions are made regarding the kind of gray-value disturbance and/or the expected magnitude of residuals during matching. The method consists of two main stages. First, initial residuals are analyzed by means of a mean-shift-based mode detection step. Second, an objective function is utilized for the selection of a mode combination not representing the gross outliers. We demonstrate the robustness of the method in a variety of examples with different noise conditions. The RAAM performance is quantitatively demonstrated in two substantially different applications, diaphragm segmentation and rheumatoid arthritis assessment. In all cases, the robust method shows an excellent behavior, with the new method tolerating up to 50% object area covered by gross gray-level disturbances.

In this paper, we present a novel convolutional neural network architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Since the shape prior is computed offline only once, the execution of our model is not limited by its calculation. Our system takes as input raw magnetic resonance images, requires no manual preprocessing or image cropping and is trained to segment the endocardium and epicardium of the left ventricle, the endocardium of the right ventricle, as well as the center of the left ventricle. With its multiresolution grid architecture, the network learns both high and low-level features useful to register the shape prior as well as accurately localize the borders of the cardiac regions. Experimental results obtained on the Automatic Cardiac Diagnostic Challenge - Medical Image Computing and Computer Assisted Intervention (ACDC-MICCAI) 2017 dataset show that our model segments multislices CMRI (left and right ventricle contours) in 0.18 s with an average Dice coefficient of [Formula: see text] and an average 3-D Hausdorff distance of [Formula: see text]mm.

Left ventricle Hermite-based segmentation