Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.

Sub-Riemannian Fast Marching in SE(2)

Abstract: We propose a Fast Marching based implementation for computing sub-Riemanninan (SR) geodesics in the roto-translation group SE(2), with a metric depending on a cost induced by the image data. The key ingredient is a Riemannian approximation of the SR-metric. Then, a state of the art Fast Marching solver that is able to deal with extreme anisotropies is used to compute a SR-distance map as the solution of a corresponding eikonal equation. Subsequent backtracking on the distance map gives the geodesics. To validate the method, we consider the uniform cost case in which exact formulas for SR-geodesics are known and we show remarkable accuracy of the numerically computed SR-spheres. We also show a dramatic decrease in computational time with respect to a previous PDE-based iterative approach. Regarding image analysis applications, we show the potential of considering these data adaptive geodesics for a fully automated retinal vessel tree segmentation.

Classification of the distance transformation algorithms under the mathematical morphology approach

Abstract: The distance transform (DT) is a morphological erosion of the binary image by a given structuring function, that dictates the distance metric in the transformation. There are many known algorithms and structuring function decompositions to efficiently implement a morphological erosion. Most of the erosion algorithms are classified as parallel, sequential raster (and anti-raster), and propagation. Based on this classification and decomposition, we review and classify most of the DT algorithms reported in the literature. As a result of this study, we have contributed not only to better classify and understand the diversity of the DT algorithms in the literature, but also to create a collection of efficient erosion algorithms suitable to different computer architectures.

Locating Anatomical Landmarks on 2D Lateral Cephalograms Through Adversarial Encoder-Decoder Networks

Abstract: Locating anatomical landmarks in a cephalometric X-ray image is a crucial step in cephalometric analysis. Manual landmark localization suffers from inter- and intra-observer variability, which makes developing automated localization methods urgent in clinics. Most of the existing techniques follow the routine thoughts which estimate numerical values of displacements or coordinates for the target landmarks. Additionally, there are no reported applications of generative adversarial networks (GAN) in cephalometric landmark localization. Motivated by these facts, we propose a new automated cephalometric landmark localization method under the framework of GAN. The principle behind our approach is fundamentally different from the conventional ones. It trained an adversarial network under the framework of GAN to learn the mapping from features to the distance map of a specific target landmark. Namely, the output of the adversarial network in this paper is image data, instead of displacements or coordinates as the conventional approaches. Based on the trained networks, we can predict the distance maps of all target landmarks in a new cephalometric image. Subsequently, the target landmarks are detected from the predicted distance maps by an approach similar to regression voting. Experimental results validate the good performance of our method in localization of cephalometric landmarks in dental X-ray images.

Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention

Abstract: We propose an end-to-end deep neural network (DNN) which can simultaneously segment the left atrial (LA) cavity and quantify LA scars. The framework incorporates the continuous spatial information of the target by introducing a spatially encoded (SE) loss based on the distance transform map. Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for deep learning-based methods. To fully utilize the inherent spatial relationship between LA and LA scars, we further propose a shape attention (SA) mechanism through an explicit surface projection to build an end-to-end-trainable model. Specifically, the SA scheme is embedded into a two-task network to perform the joint LA segmentation and scar quantification. Moreover, the proposed method can alleviate the severe class-imbalance problem when detecting small and discrete targets like scars. We evaluated the proposed framework on 60 LGE MRI data from the MICCAI2018 LA challenge. For LA segmentation, the proposed method reduced the mean Hausdorff distance from 36.4 mm to 20.0 mm compared to the 3D basic U-Net using the binary cross-entropy loss. For scar quantification, the method was compared with the results or algorithms reported in the literature and demonstrated better performance.

Feature-Point Detection Using Distance Transforms: Application to Tracking Tropical Convective Complexes

Abstract: The identification, tracking, and statistical analysis of tropical convective complexes using satellite imagery is explored in the context of identifying feature points suitable for tracking. The feature points are determined based on the shape of complexes using the distance transform technique. This approach has been applied to the determination feature points for tropical convective complexes identified in a time series of global cloud imagery. The feature points are used to track the complexes, and from the tracks statistical diagnostic fields are computed. This approach allows the nature and distribution of organized deep convection in the Tropics to be explored.