Fast computation of the 3-D Euclidean distance transform on the EREW PRAM model

2001 ◽  
Author(s):  
Yu-Hua Lee ◽  
Shi-Jinn Horng ◽  
J. Seitzer
Keyword(s):  
Euclidean Distance ◽  
Distance Transform ◽  
Fast Computation ◽  
Euclidean Distance Transform ◽  
Erew Pram ◽  
Pram Model

scholarly journals N-D Linear Time Exact Signed Euclidean Distance Transform

2003 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Marcelo Siqueira ◽  
James Gee
Keyword(s):  
Computer Vision ◽  
Euclidean Distance ◽  
Linear Time ◽  
Small Time ◽  
Direct Application ◽  
Distance Transform ◽  
Fast Computation ◽  
Distance Transforms ◽  
Euclidean Distance Transform ◽  
Image Pixels

Fast computation of distance transforms find direct application in various computer vision problems. Currently there exists two image filters in the ITK library which can be used to generate distance maps. Unfortunately, these filters produce only approximations to the Euclidean Distance Transform (EDT). We introduce into the ITK library a third EDT filter which was developed by Maurer {} . In contrast to other algorithms, this algorithm produces the exact signed squared EDT using integer arithmetic. The complexity, which is formally verified, is O(n) O(n) with a small time constant where n n is the number of image pixels.

EFFICIENT ALGORITHMS FOR THE EUCLIDEAN DISTANCE TRANSFORM

1995 ◽  
Vol 05 (02) ◽  
pp. 205-212 ◽  
Author(s):  
SANDY PAVEL ◽  
SELIM G. AKL
Keyword(s):  
Parallel Algorithm ◽  
Euclidean Distance ◽  
Sequential Algorithm ◽  
Distance Transform ◽  
Binary Images ◽  
Euclidean Distance Transform ◽  
Erew Pram ◽  
Time Optimal ◽  
Time Bounds ◽  
And Robotics

The Euclidean Distance Transform is an important computational tool for the processing of binary images, with applications in many areas such as computer vision, pattern recognition and robotics. We investigate the properties of this transform and describe an O(n2) time optimal sequential algorithm. A deterministic EREW-PRAM parallel algorithm which runs in O( log n) time using O(n2) processors and O(n2) space is also derived. Further, a cost optimal randomized parallel algorithm which runs within the same time bounds with high probability, is given.

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.

Sign in / Sign up

Export Citation Format

Share Document