scholarly journals Elastic Registration of Biological Images Using Vector-Spline Regularization

2005 ◽  
Vol 52 (4) ◽  
pp. 652-663 ◽  
Author(s):  
C.O.S. Sorzano ◽  
P. Thevenaz ◽  
M. Unser
Keyword(s):  
Elastic Registration ◽  
Biological Images ◽  
Vector Spline

scholarly journals Consistent and Elastic Registration of Histological Sections Using Vector-Spline Regularization

2006 ◽  
pp. 85-95 ◽  
Author(s):  
Ignacio Arganda-Carreras ◽  
Carlos O. S. Sorzano ◽  
Roberto Marabini ◽  
José María Carazo ◽  
Carlos Ortiz-de-Solorzano ◽  
...  
Keyword(s):  
Elastic Registration ◽  
Histological Sections ◽  
Vector Spline

A Similarity Searching System for Biological Phenotype Images Using Deep Convolutional Encoder-decoder Architecture

2019 ◽  
Vol 14 (7) ◽  
pp. 628-639 ◽  
Author(s):  
Bizhi Wu ◽  
Hangxiao Zhang ◽  
Limei Lin ◽  
Huiyuan Wang ◽  
Yubang Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Retrieval System ◽  
Sequence Similarity ◽  
Local Alignment ◽  
Similarity Searching ◽  
Loss Of Function ◽  
Biological Images ◽  
The Neural Network ◽  
Convolutional Autoencoder ◽  
Biological Phenotype

Background: The BLAST (Basic Local Alignment Search Tool) algorithm has been widely used for sequence similarity searching. Analogously, the public phenotype images must be efficiently retrieved using biological images as queries and identify the phenotype with high similarity. Due to the accumulation of genotype-phenotype-mapping data, a system of searching for similar phenotypes is not available due to the bottleneck of image processing. Objective: In this study, we focus on the identification of similar query phenotypic images by searching the biological phenotype database, including information about loss-of-function and gain-of-function. Methods: We propose a deep convolutional autoencoder architecture to segment the biological phenotypic images and develop a phenotype retrieval system to enable a better understanding of genotype–phenotype correlation. Results: This study shows how deep convolutional autoencoder architecture can be trained on images from biological phenotypes to achieve state-of-the-art performance in a phenotypic images retrieval system. Conclusion: Taken together, the phenotype analysis system can provide further information on the correlation between genotype and phenotype. Additionally, it is obvious that the neural network model of image segmentation and the phenotype retrieval system is equally suitable for any species, which has enough phenotype images to train the neural network.

Handling biological images and videos in the bioimage database

2000 ◽  
Author(s):  
D.M. Shotton
Keyword(s):  
Biological Images

scholarly journals Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images

2021 ◽  
Vol 30 ◽  
pp. 2045-2059
Author(s):  
Dongnan Liu ◽  
Donghao Zhang ◽  
Yang Song ◽  
Heng Huang ◽  
Weidong Cai
Keyword(s):  
Feature Fusion ◽  
Biological Images ◽  
Instance Segmentation

scholarly journals Quality of biological images, reconstructed using localization microscopy data

2017 ◽  
Vol 34 (5) ◽  
pp. 845-852 ◽  
Author(s):  
Błażej Ruszczycki ◽  
Tytus Bernas
Keyword(s):  
Localization Microscopy ◽  
Biological Images ◽  
Microscopy Data

878 poster VALIDATION OF ELASTIC REGISTRATION TO STUDY PAROTID DEFORMATION IN HEAD AND NECK TOMOTHERAPY

2011 ◽  
Vol 99 ◽  
pp. S339 ◽  
Author(s):  
E. Scalco ◽  
E. Faggiano ◽  
S. Liberini ◽  
C. Fiorino ◽  
E. Maggiulli ◽  
...  
Keyword(s):  
Head And Neck ◽  
Elastic Registration

Robust and staining-invariant elastic registration of a series of images from histologic slices

2005 ◽  
Author(s):  
Stefan Wirtz ◽  
Nils Papenberg ◽  
Bernd Fischer ◽  
Oliver Schmitt
Keyword(s):  
Elastic Registration

Computer determination of the constituent structure of biological images

1971 ◽  
Vol 4 (3) ◽  
pp. 315-328 ◽  
Author(s):  
Russell A. Kirsch
Keyword(s):  
Constituent Structure ◽  
Biological Images ◽  
Computer Determination

scholarly journals TIMER: Tensor Image Morphing for Elastic Registration

2009 ◽  
Author(s):  
Pew-Thian Yap ◽  
Guorong Wu ◽  
Hongtu Zhu ◽  
Weili Lin ◽  
Dinggang Shen
Keyword(s):  
Elastic Registration ◽  
Image Morphing
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