A fully automatic registration method for laser scanner data

2013 ◽  
Author(s):  
K. Al-Manasir
Keyword(s):  
Laser Scanner ◽  
Automatic Registration ◽  
Scanner Data ◽  
Registration Method ◽  
Fully Automatic

scholarly journals Vessel segmentation for automatic registration of untracked laparoscopic ultrasound to CT of the liver

2021 ◽  
Author(s):  
Nina Montaña-Brown ◽  
João Ramalhinho ◽  
Moustafa Allam ◽  
Brian Davidson ◽  
Yipeng Hu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Liver Resection ◽  
Image Guidance ◽  
Laparoscopic Ultrasound ◽  
Multiple Models ◽  
Automatic Registration ◽  
Registration Method ◽  
Fully Automatic ◽  
Tracking Device

Abstract Purpose: Registration of Laparoscopic Ultrasound (LUS) to a pre-operative scan such as Computed Tomography (CT) using blood vessel information has been proposed as a method to enable image-guidance for laparoscopic liver resection. Currently, there are solutions for this problem that can potentially enable clinical translation by bypassing the need for a manual initialisation and tracking information. However, no reliable framework for the segmentation of vessels in 2D untracked LUS images has been presented. Methods: We propose the use of 2D UNet for the segmentation of liver vessels in 2D LUS images. We integrate these results in a previously developed registration method, and show the feasibility of a fully automatic initialisation to the LUS to CT registration problem without a tracking device. Results: We validate our segmentation using LUS data from 6 patients. We test multiple models by placing patient datasets into different combinations of training, testing and hold-out, and obtain mean Dice scores ranging from 0.543 to 0.706. Using these segmentations, we obtain registration accuracies between 6.3 and 16.6 mm in 50% of cases. Conclusions: We demonstrate the first instance of deep learning (DL) for the segmentation of liver vessels in LUS. Our results show the feasibility of UNet in detecting multiple vessel instances in 2D LUS images, and potentially automating a LUS to CT registration pipeline.

Automatic Registration Based on Covariance Matrix Eigenvector Direction of Feature Point

2012 ◽  
Vol 538-541 ◽  
pp. 2842-2845
Author(s):  
Li Hua Fan ◽  
Qi Chen ◽  
Bao Ling Xie ◽  
Bo Liu
Keyword(s):  
Rigid Body ◽  
Covariance Matrix ◽  
Computational Cost ◽  
Laser Scanner ◽  
Statistical Error ◽  
Feature Point ◽  
Automatic Registration ◽  
Initial Estimate ◽  
Registration Method ◽  
Feature Extraction Method

A number of range images taken from different views to be merged to construct an entire 3D model are a common problem which is called registration in RE. The enormous computational cost is required, because this process is usually repeated on searching optimal rigid body coordinate transformation parameters and evaluated in a statistical error distance between two data. This paper proposes a new registration method based on the covariance matrix eigenvector direction of feature point. First, a very fast feature extraction method is presented. Then, covariance matrix eigenvector direction of feature points and those neighborhood points are calculated. Finally, an initial estimate for relative rigid-body transform can be realized, matching these eigenvector directions using an approach of Hough transform. Experimental results of 3D images taken by laser scanner are carried out to compare the convergence and registration error. The proposed registration approach can realize automatic registration without any assumptions about their initial positions and overcome the problems of traditional ICP in low overlapping and bad initial estimate.

Obtaining morphometric variables from gullies using two methods of interpolation laser scanner data: the case study of Vassouras, Brazil

2020 ◽  
Vol 17 (12) ◽  
pp. 3012-3023
Author(s):  
Carlos Magno Moreira de Oliveira ◽  
Márcio Rocha Francelino ◽  
Bruno Araujo Furtado de Mendonça ◽  
Isabela Queiroz Ramos
Keyword(s):  
Laser Scanner ◽  
Scanner Data

scholarly journals Combining optical satellite data and airborne laser scanner data for vegetation classification

2011 ◽  
Vol 3 (5) ◽  
pp. 393-401 ◽  
Author(s):  
Karin Nordkvist ◽  
Ann-Helen Granholm ◽  
Johan Holmgren ◽  
Håkan Olsson ◽  
Mats Nilsson
Keyword(s):  
Satellite Data ◽  
Laser Scanner ◽  
Vegetation Classification ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Optical Satellite Data
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