Segmentation of clusters nuclei based on intensity and texture in phase contrast image using h-maxima transformation

2016 ◽  
Author(s):  
A. Vinothini ◽  
B. Prasad
Keyword(s):  
Phase Contrast ◽  
Phase Contrast Image ◽  
Contrast Image

Experimental verification of phase contrast image synthesis system

1996 ◽  
Author(s):  
Jesper Glueckstad ◽  
Haruyoshi Toyoda ◽  
Narihiro Yoshida ◽  
Tamiki Takemori ◽  
Tsutomu Hara
Keyword(s):  
Experimental Verification ◽  
Phase Contrast ◽  
Image Synthesis ◽  
Phase Contrast Image ◽  
Synthesis System ◽  
Contrast Image

scholarly journals Green Fluorescent Protein and Phase-Contrast Image Fusion via Generative Adversarial Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Tang ◽  
Yu Liu ◽  
Chao Zhang ◽  
Juan Cheng ◽  
Hu Peng ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Fluorescent Protein ◽  
Structural Information ◽  
Generative Adversarial Networks ◽  
Phase Contrast Image ◽  
Functional Information ◽  
Adversarial Networks ◽  
Fused Image ◽  
Green Fluorescent ◽  
Contrast Image

In the field of cell and molecular biology, green fluorescent protein (GFP) images provide functional information embodying the molecular distribution of biological cells while phase-contrast images maintain structural information with high resolution. Fusion of GFP and phase-contrast images is of high significance to the study of subcellular localization, protein functional analysis, and genetic expression. This paper proposes a novel algorithm to fuse these two types of biological images via generative adversarial networks (GANs) by carefully taking their own characteristics into account. The fusion problem is modelled as an adversarial game between a generator and a discriminator. The generator aims to create a fused image that well extracts the functional information from the GFP image and the structural information from the phase-contrast image at the same time. The target of the discriminator is to further improve the overall similarity between the fused image and the phase-contrast image. Experimental results demonstrate that the proposed method can outperform several representative and state-of-the-art image fusion methods in terms of both visual quality and objective evaluation.

X-ray phase contrast image simulation

2007 ◽  
Vol 254 (2) ◽  
pp. 307-318 ◽  
Author(s):  
A. Peterzol ◽  
J. Berthier ◽  
P. Duvauchelle ◽  
C. Ferrero ◽  
D. Babot
Keyword(s):  
Phase Contrast ◽  
Phase Contrast Image ◽  
Image Simulation ◽  
X Ray ◽  
Contrast Image

Hard X-ray pure phase-contrast image obtained directly by experiment

2013 ◽  
Vol 30 (2) ◽  
pp. 129-132
Author(s):  
Jinchuan Guo ◽  
Qinlao Yang ◽  
Bin Zhou ◽  
Hanben Niu
Keyword(s):  
Phase Contrast ◽  
Phase Contrast Image ◽  
X Ray ◽  
Pure Phase ◽  
Contrast Image

Phase contrast image segmentation using a Laue analyser crystal

2011 ◽  
Vol 56 (3) ◽  
pp. 515-534 ◽  
Author(s):  
Marcus J Kitchen ◽  
David M Paganin ◽  
Kentaro Uesugi ◽  
Beth J Allison ◽  
Robert A Lewis ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Phase Contrast ◽  
Phase Contrast Image ◽  
Contrast Image

scholarly journals A Fusion Algorithm for GFP Image and Phase Contrast Image of Arabidopsis Cell Based on SFL-Contourlet Transform

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Peng Feng ◽  
Jing Wang ◽  
Biao Wei ◽  
Deling Mi
Keyword(s):  
Visual Information ◽  
Phase Contrast ◽  
Fluorescent Protein ◽  
Contourlet Transform ◽  
Phase Contrast Image ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Objective Criterion ◽  
Green Fluorescent ◽  
Contrast Image

A hybrid multiscale and multilevel image fusion algorithm for green fluorescent protein (GFP) image and phase contrast image of Arabidopsis cell is proposed in this paper. Combining intensity-hue-saturation (IHS) transform and sharp frequency localization Contourlet transform (SFL-CT), this algorithm uses different fusion strategies for different detailed subbands, which include neighborhood consistency measurement (NCM) that can adaptively find balance between color background and gray structure. Also two kinds of neighborhood classes based on empirical model are taken into consideration. Visual information fidelity (VIF) as an objective criterion is introduced to evaluate the fusion image. The experimental results of 117 groups of Arabidopsis cell image from John Innes Center show that the new algorithm cannot only make the details of original images well preserved but also improve the visibility of the fusion image, which shows the superiority of the novel method to traditional ones.

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