Fast and scalable parallel algorithms for Euclidean distance transform on LARPBS

2004 ◽  
Author(s):  
Ling Chen ◽  
Yi Pan ◽  
Xiao-hua Xu
Keyword(s):  
Parallel Algorithms ◽  
Euclidean Distance ◽  
Distance Transform ◽  
Euclidean Distance Transform

Level Set Method Optimized with the Euclidean Distance Transform

2019 ◽  
Author(s):  
Luis Fernando Segalla ◽  
Alexandre Zabot ◽  
Diogo Nardelli Siebert ◽  
Fabiano Wolf
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Euclidean Distance ◽  
Distance Transform ◽  
Euclidean Distance Transform

Retinal image blood vessel extraction and quantification with Euclidean distance transform approach

2020 ◽  
Author(s):  
Kuryati Kipli ◽  
Mohammed Enamul Hoque ◽  
Lik Thai Lim ◽  
Tengku Mohd Afendi Zulcaffle ◽  
Siti Kudnie Sahari ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Retinal Image ◽  
Euclidean Distance ◽  
Distance Transform ◽  
Euclidean Distance Transform ◽  
Vessel Extraction

scholarly journals Identification of protein pockets and cavities by Euclidean Distance Transform

2018 ◽  
Author(s):  
Sebastian Daberdaku
Keyword(s):  
Ligand Binding ◽  
Active Sites ◽  
Euclidean Distance ◽  
Protein Structures ◽  
Ligand Docking ◽  
Distance Transform ◽  
Structure Based Drug Design ◽  
Distance Map ◽  
Euclidean Distance Transform ◽  
Protein Pockets

Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This paper presents a novel purely geometric algorithm for the detection of ligand binding protein pockets and cavities based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii depending on the shape of the candidate ligand. The pocket regions consist of the voxels located between the two surfaces, which exhibit a certain minimum depth value from the outer surface. The distance map values computed by the EDT algorithm during the second surface computation can be used to elegantly determine the depth of each candidate pocket and to rank them accordingly. Cavities on the other hand, are identified by scanning the inside of the protein for voids. The algorithm determines and outputs the best k candidate pockets and cavities, i.e. the ones that are more likely to bind to the given ligand. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities, and was shown to outperform most of the previously developed purely geometric pocket and cavity search methods.

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