scholarly journals ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 333
Author(s):  
David Legland ◽  
Marie-Françoise Devaux
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
User Interface ◽  
Image Data ◽  
Image Visualization ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Spatial Calibration ◽  
User Friendly ◽  
Efficient Software

Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

scholarly journals Robust extraction of quantitative structural information from high-variance histological images of livers from necropsied Soay sheep

2017 ◽  
Vol 4 (7) ◽  
pp. 170111 ◽  
Author(s):  
Q. Caudron ◽  
R. Garnier ◽  
J. G. Pilkington ◽  
K. A. Watt ◽  
C. Hansen ◽  
...  
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Image Data ◽  
Spatial Relations ◽  
Quantitative Information ◽  
Current Application ◽  
Soay Sheep ◽  
Histological Images ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Quantitative information is essential to the empirical analysis of biological systems. In many such systems, spatial relations between anatomical structures is of interest, making imaging a valuable data acquisition tool. However, image data can be difficult to analyse quantitatively. Many image processing algorithms are highly sensitive to variations in the image, limiting their current application to fields where sample and image quality may be very high. Here, we develop robust image processing algorithms for extracting structural information from a dataset of high-variance histological images of inflamed liver tissue obtained during necropsies of wild Soay sheep. We demonstrate that features of the data can be measured in a fully automated manner, providing quantitative information which can be readily used in statistical analysis. We show that these methods provide measures that correlate well with a manual, expert operator-led analysis of the same images, that they provide advantages in terms of sampling a wider range of information and that information can be extracted far more quickly than in manual analysis.

scholarly journals Matlab Framework for Image Processing and Feature Extraction Flexible Algorithm Design

Proceedings ◽  
2021 ◽  
Vol 63 (1) ◽  
pp. 72
Author(s):  
Razvan Cazacu
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
User Interface ◽  
Algorithm Design ◽  
End Users ◽  
Image Preprocessing ◽  
Speed Up ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Image processing and the analysis of images in order to extract relevant data is an ever-growing topic of research. Although there are numerous methods readily available, the task of image preprocessing and feature extraction requires developing specific algorithms for specific problems by combining different functions and tweaking their parameters. This paper proposes a framework that allows the flexible construction of image processing algorithms. Its user interface and architecture are designed to ease and speed up the process of algorithm creation and testing as well as serve as an application for the use of these algorithms by end users. The framework was built in Matlab and makes use of its integrated Image Processing toolbox.

Otoconia perfect/imperfect crystals

1994 ◽  
Vol 52 ◽  
pp. 42-43
Author(s):  
César D. Fermin ◽  
Dale Martin
Keyword(s):  
Image Processing ◽  
Room Temperature ◽  
Phosphotungstic Acid ◽  
Gallus Domesticus ◽  
Crystal Matrix ◽  
Organic Templates ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Diffraction Patterns

Otoconia of higher vertebrates are interesting biological crystals that display the diffraction patterns of perfect crystals (e.g., calcite for birds and mammal) when intact, but fail to produce a regular crystallographic pattern when fixed. Image processing of the fixed crystal matrix, which resembles the organic templates of teeth and bone, failed to clarify a paradox of biomineralization described by Mann. Recently, we suggested that inner ear otoconia crystals contain growth plates that run in different directions, and that the arrangement of the plates may contribute to the turning angles seen at the hexagonal faces of the crystals.Using image processing algorithms described earlier, and Fourier Transform function (2FFT) of BioScan Optimas®, we evaluated the patterns in the packing of the otoconia fibrils of newly hatched chicks (Gallus domesticus) inner ears. Animals were fixed in situ by perfusion of 1% phosphotungstic acid (PTA) at room temperature through the left ventricle, after intraperitoneal Nembutal (35mg/Kg) deep anesthesia. Negatives were made with a Hitachi H-7100 TEM at 50K-400K magnifications. The negatives were then placed on a light box, where images were filtered and transferred to a 35 mm camera as described.

Elastic Graph-based Semi-supervised Embedding with Adaptive Loss Regression

2020 ◽  
Keyword(s):  
Image Processing ◽  
International Symposium ◽  
Fast Track ◽  
Electronic Imaging ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Image Processing: Algorithms and Systems proceedings.

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