scholarly journals Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection-a first study

2013 ◽  
Vol 253 (1) ◽  
pp. 54-64 ◽  
Author(s):  
D. HU ◽  
P. SARDER ◽  
P. RONHOVDE ◽  
S. ORTHAUS ◽  
S. ACHILEFU ◽  
...  
Keyword(s):  
Community Detection ◽  
Fluorescence Lifetime ◽  
Automatic Segmentation ◽  
Microscopy Images

scholarly journals DSeg: A Dynamic Image Segmentation Program to Extract Backbone Patterns for Filamentous Bacteria and Hyphae Structures

2019 ◽  
Vol 25 (3) ◽  
pp. 711-719 ◽  
Author(s):  
Hanqing Zhang ◽  
Niklas Söderholm ◽  
Linda Sandblad ◽  
Krister Wiklund ◽  
Magnus Andersson
Keyword(s):  
Time Series ◽  
Automatic Segmentation ◽  
Persistence Length ◽  
Synthetic Data ◽  
Image Data ◽  
Growth Direction ◽  
Process Time ◽  
Transmission Electron ◽  
Size Constraints ◽  
Microscopy Images

AbstractAnalysis of numerous filamentous structures in an image is often limited by the ability of algorithms to accurately segment complex structures or structures within a dense population. It is even more problematic if these structures continuously grow when recording a time-series of images. To overcome these issues we present DSeg; an image analysis program designed to process time-series image data, as well as single images, to segment filamentous structures. The program includes a robust binary level-set algorithm modified to use size constraints, edge intensity, and past information. We verify our algorithms using synthetic data, differential interference contrast images of filamentous prokaryotes, and transmission electron microscopy images of bacterial adhesion fimbriae. DSeg includes automatic segmentation, tools for analysis, and drift correction, and outputs statistical data such as persistence length, growth rate, and growth direction. The program is available at Sourceforge.

Automatic Identification of the Activity Area in Brain with Deep Neural Networks

2014 ◽  
Vol 556-562 ◽  
pp. 4941-4944
Author(s):  
Li Xiang Shi ◽  
Li Peng ◽  
Lu Lu Yue ◽  
Zhi Xing Huang
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Segmentation ◽  
Threshold Value ◽  
Automatic Identification ◽  
Post Processing ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Central Pixel ◽  
Microscopy Images

We use deep max-pooling convolutional neural networks to address a problem of neuroanatomy, namely, the automatic segmentation of cerebral cortex structures of laboratory rat depicted in stacks of Two-photon microscopy images and detect the change areas when stimulation occurs. We classify each pixel in the image by training a CNN network, using a square window to predict the probability of the central pixel for each class. After classification, we perform the post-processing on the output produced by CNN. At last, we depict the areas that we interested through a threshold value.

Automatic Segmentation and Classification of Human Intestinal Parasites From Microscopy Images

2013 ◽  
Vol 60 (3) ◽  
pp. 803-812 ◽  
Author(s):  
C. T. N. Suzuki ◽  
J. F. Gomes ◽  
A. X. Falcao ◽  
J. P. Papa ◽  
S. Hoshino-Shimizu
Keyword(s):  
Intestinal Parasites ◽  
Automatic Segmentation ◽  
Microscopy Images

Automatic segmentation and automatic seed point selection of nasopharyngeal carcinoma from microscopy images using region growing based approach

2017 ◽  
Vol 20 ◽  
pp. 61-69 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
Raed Ibraheem Hamed ◽  
Mohamad Khir Abdullah ◽  
Dheyaa Ahmed Ibrahim
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Automatic Segmentation ◽  
Region Growing ◽  
Point Selection ◽  
Seed Point ◽  
Microscopy Images ◽  
Selection Of
Sign in / Sign up

Export Citation Format

Share Document