Image-Processing Techniques Applied to the Study of the Musculoskeletal System and Its Interactions with Biomaterials

MRS Bulletin ◽  
2000 ◽  
Vol 25 (1) ◽  
pp. 38-42
Author(s):  
A. Merolli ◽  
P. Tranquilli Leali
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Geometric Analysis ◽  
Computer Applications ◽  
Source Image ◽  
Computer Assisted ◽  
Surface Metrology ◽  
Computerized Image Processing ◽  
Long Time ◽  
Histological Images

Over the past decade, orthopedic surgery and related research have made increasing use of the opportunities offered by computers. Many clinical and research procedures rely on medical images (e.g., x-ray films and histology) and their numerical evaluation (e.g., geometric analysis and surface metrology). This has led to the development of computer applications in orthopedics, with computer graphics playing a fundamental role. The image-processing field has also been studied in greater depth, with a view toward enhancing systems automation.Image processing relies on capturing measurable variations in some image parameters (e.g., light intensity, color, and signal-to-noise ratio). The extracted numerical values then can be used to modify the source image. From this definition, image processing can be considered a measurement technique, and in this light it has been introduced into histomorpho-metrical practice.Image processing speeds up routine analysis procedures by evaluating, in a less tedious and quicker way, a greater number of images, parameters, or both. Semiautomated computerized image-processing systems have been in use for a long time, based on computer-assisted analysis of objects whose contours were tracked manually by a digitizing cursor. Histological images were, generally, projected from the microscope over a digitizing tablet, with the operator visually recognizing the structure of interest and picking up its contours. The critical advance brought to histomorphometry by semiautomated techniques has been the practical measurement of surface-related parameters.

New polarized-light microscope for fast and orientation independent measurement of birefringent fine structure

1993 ◽  
Vol 51 ◽  
pp. 82-83
Author(s):  
Rudolf Oldenbourg
Keyword(s):  
Light Microscope ◽  
Polarized Light ◽  
Video Camera ◽  
Limited Range ◽  
Image Point ◽  
Computer Assisted ◽  
Optical Modulators ◽  
Anisotropic Structures ◽  
Long Time ◽  
Computer Assisted Image

The polarized light microscope has the unique potential to measure submicroscopic molecular arrangements dynamically and non-destructively in living cells and other specimens. With the traditional pol-scope, however, single images display only those anisotropic structures that have a limited range of orientations with respect to the polarization axes of the microscope. Furthermore, rapid measurements are restricted to a single image point or single area that exhibits uniform birefringence or other form of optical anisotropy, while measurements comparing several image points take an inordinately long time.We are developing a new kind of polarized light microscope which combines speed and high resolution in its measurement of the specimen anisotropy, irrespective of its orientation. The design of the new pol-scope is based on the traditional polarized light microscope with two essential modifications: circular polarizers replace linear polarizers and two electro-optical modulators replace the traditional compensator. A video camera and computer assisted image analysis provide measurements of specimen anisotropy in rapid succession for all points of the image comprising the field of view.

Computer-Assisted High-Re Solution TEM

1990 ◽  
Vol 48 (1) ◽  
pp. 162-163
Author(s):  
F.A. Ponce ◽  
H. Hikashi
Keyword(s):  
Signal To Noise Ratio ◽  
Accurate Determination ◽  
Computer Software ◽  
Video Camera ◽  
Image Contrast ◽  
Objective Lens ◽  
Computer Assisted ◽  
Optical Parameters ◽  
Astigmatism Correction

The determination of the atomic positions from HRTEM micrographs is only possible if the optical parameters are known to a certain accuracy, and reliable through-focus series are available to match the experimental images with calculated images of possible atomic models. The main limitation in interpreting images at the atomic level is the knowledge of the optical parameters such as beam alignment, astigmatism correction and defocus value. Under ordinary conditions, the uncertainty in these values is sufficiently large to prevent the accurate determination of the atomic positions. Therefore, in order to achieve the resolution power of the microscope (under 0.2nm) it is necessary to take extraordinary measures. The use of on line computers has been proposed [e.g.: 2-5] and used with certain amount of success.We have built a system that can perform operations in the range of one frame stored and analyzed per second. A schematic diagram of the system is shown in figure 1. A JEOL 4000EX microscope equipped with an external computer interface is directly linked to a SUN-3 computer. All electrical parameters in the microscope can be changed via this interface by the use of a set of commands. The image is received from a video camera. A commercial image processor improves the signal-to-noise ratio by recursively averaging with a time constant, usually set at 0.25 sec. The computer software is based on a multi-window system and is entirely mouse-driven. All operations can be performed by clicking the mouse on the appropiate windows and buttons. This capability leads to extreme friendliness, ease of operation, and high operator speeds. Image analysis can be done in various ways. Here, we have measured the image contrast and used it to optimize certain parameters. The system is designed to have instant access to: (a) x- and y- alignment coils, (b) x- and y- astigmatism correction coils, and (c) objective lens current. The algorithm is shown in figure 2. Figure 3 shows an example taken from a thin CdTe crystal. The image contrast is displayed for changing objective lens current (defocus value). The display is calibrated in angstroms. Images are stored on the disk and are accessible by clicking the data points in the graph. Some of the frame-store images are displayed in Fig. 4.

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

scholarly journals Simulation of the Photodetection in the PPS CMOS Sensors

2013 ◽  
Vol 8-9 ◽  
pp. 611-618
Author(s):  
Florin Toadere ◽  
Radu Arsinte
Keyword(s):  
Image Processing ◽  
Input Data ◽  
Signal To Noise Ratio ◽  
Image Processing Algorithm ◽  
Signal To Noise ◽  
Spectral Image ◽  
Image Capture ◽  
Visible Image ◽  
Optical Part ◽  
Electrical Part

The paper contains an analysis and simulation of passive pixel based sensors. The passive pixel CMOS image acquisition sensor (PPS) is the key part of a visible image capture systems. The PPS is a complex circuit composed by an optical part and an electrical part, both analog and digital. The goal of this paper is to simulate the functionality of the photodetection process that happens in the PPS sensor. The photodetector is responsible with the conversion from photons to electrical charges and then into current. In the optical part, the sensor is analyzed by a spectral image processing algorithm which uses as input data: the lenses array transmittance, the red, green and blue filters and the quantum efficiency of the PPS. In the electrical part of simulation, the program is computing the signal to noise ratio of the sensor taking into account the photon shot, white and fixed pattern noises. Our basic analysis is based on camera equation to which we add the noises.

scholarly journals A method for presenting biomedical images based on a model of receptive fields of human visual perception

2021 ◽  
Vol 2091 (1) ◽  
pp. 012027
Author(s):  
V E Antsiperov ◽  
V A Kershner
Keyword(s):  
Image Processing ◽  
Visual Perception ◽  
Bayesian Learning ◽  
Signal To Noise Ratio ◽  
Receptive Fields ◽  
New Method ◽  
Human Visual Perception ◽  
Signal To Noise ◽  
Biomedical Images ◽  
Local Characteristics

Abstract The paper is devoted to the development of a new method for presenting biomedical images based on local characteristics of the intensity of their shape. The proposed method of image processing is focused on images that have low indicators of the intensity of the recorded radiation, resolution, contrast and signal-to-noise ratio. The method is based on the principles of machine (Bayesian) learning and on samples of random photo reports. This paper presents the results of the method and its connection with modern approaches in the field of image processing.

scholarly journals SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA

2011 ◽  
Vol 19 (3) ◽  
pp. 189
Author(s):  
Karsten Rodenacker ◽  
Klaus Hahn ◽  
Gerhard Winkler ◽  
Dorothea P Auer
Keyword(s):  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Digital Data ◽  
Linear Filters ◽  
Brain Functions ◽  
Non Linear ◽  
Long Time ◽  
Spatio Temporal ◽  
Sensory Activation ◽  
Temporal Data Analysis

Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.

Image Processing using Open Source Tools and their Implementation in the Analysis of Complex Microstructures

2021 ◽  
Vol 58 (8) ◽  
pp. 484-506
Author(s):  
U. P. Nayak ◽  
M. Müller ◽  
D. Britz ◽  
M.A. Guitar ◽  
F. Mücklich
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
User Interface ◽  
Open Source ◽  
Microstructural Analysis ◽  
Microstructural Characterization ◽  
Source Image ◽  
Versatile Tool ◽  
Processing Software ◽  
Image Processing Software

Abstract Considering the dependance of materials’ properties on the microstructure, it is imperative to carry out a thorough microstructural characterization and analysis to bolster its development. This article is aimed to inform the users about the implementation of FIJI, an open source image processing software for image segmentation and quantitative microstructural analysis. The rapid advancement of computer technology in the past years has made it possible to swiftly segment and analyze hundreds of micrographs reducing hours’ worth of analysis time to a mere matter of minutes. This has led to the availability of several commercial image processing software programs primarily aimed at relatively inexperienced users. Despite the advantages like ‘one-click solutions’ offered by commercial software, the high licensing cost limits its widespread use in the metallographic community. Open-source platforms on the other hand, are free and easily available although rudimentary knowledge of the user-interface is a pre-requisite. In particular, the software FIJI has distinguished itself as a versatile tool, since it provides suitable extensions from image processing to segmentation to quantitative stereology and is continuously developed by a large user community. This article aims to introduce the FIJI program by familiarizing the user with its graphical user-interface and providing a sequential methodology to carry out image segmentation and quantitative microstructural analysis.

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