Application of Composite Small Calibration Objects in Traffic Accident Scene Photogrammetry

In order to address the difficulty of arranging large calibration objects and the low measurement accuracy of small calibration objects in traffic accident scene photogrammetry, a photogrammetric method based on a composite of small calibration objects is proposed. Several small calibration objects are placed around the traffic accident scene, and the coordinate system of the composite calibration object is given based on one of them. By maintaining the relative position and coplanar relationship of the small calibration objects, the local coordinate system of each small calibration object is transformed into the coordinate system of the composite calibration object. The two-dimensional direct linear transformation method is improved based on minimizing the reprojection error of the calibration points of all objects. A rectified image is obtained using the nonlinear optimization method. The increased accuracy of traffic accident scene photogrammetry using a composite small calibration object is demonstrated through the analysis of field experiments and case studies.     

A New Rapid On-Line Imaging Method to Determine Particle Size Distribution of Granules

The purpose of this research was to study the feasibility of the new image analysis method in the particle size determination of the granules. The method is capable of forming a three-dimensional topographic image of a sample surface from a digital picture. In the method, a flat granule bed surface was illuminated from three different directions, using the three primary colors (red, green, and blue). One color picture was taken by a digital camera, after which a topographic image of the object surface was constructed. The particle size distribution was then calculated from the image data. The particle size analysis method was tested both off-line and on-line. Off-line particle size measurement results determined by the image analysis method corresponded quite well to those of sieve analysis in the size fraction range 250–1,000 μm. In on-line application, images were successfully retrieved and median granule size trend could be calculated and followed during fluid bed granulations.     

Model-based simulation of dynamic magnetic resonance imaging signals

This paper presents a model-based method to efficiently simulate dynamic magnetic resonance imaging signals. Using an analytical spatiotemporal object model, the method can approximate time-varying k-space signals such as those from objects in motion and/or during dynamic contrast enhancement. Both rigid-body and non-rigid-body motions can be simulated using the proposed method. In addition, it can simulate data with arbitrary data sampling order and/or non-uniform k-space trajectory. A set of simulated images were compared with real data acquired from a rat model on a 4.7 T scanner to verify the model. The efficient simulation method is expected to be useful for rapid testing of various imaging and image analysis algorithms such as image reconstruction, image registration, motion compensation, and kinetic parameter mapping.     

Fast processing of microscopic images using object-based extended depth of field

Background

Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue.

Methods

The OEDoF algorithm consists of four major modules: 1) color conversion, 2) object region identification, 3) good contrast pixel identification and 4) detail merging. First, the algorithm employs color conversion to enhance contrast followed by identification of foreground pixels. A composite image is constructed using only these foreground pixels, which dramatically reduces the computational time.

Results

We used 250 images obtained from 45 specimens of confirmed malaria infections to test our proposed algorithm. The resulting composite images with all in-focus objects were produced using the proposed OEDoF algorithm. We measured the performance of OEDoF in terms of image clarity (quality) and processing time. The features of interest selected by the OEDoF algorithm are comparable in quality with equivalent regions in images processed by the state-of-the-art complex wavelet EDoF algorithm; however, OEDoF required four times less processing time.

Conclusions

This work presents a modification of the extended depth of field approach for efficiently enhancing microscopic images. This selective object processing scheme used in OEDoF can significantly reduce the overall processing time while maintaining the clarity of important image features. The empirical results from parasite-infected red cell images revealed that our proposed method efficiently and effectively produced in-focus composite images. With the speed improvement of OEDoF, this proposed algorithm is suitable for processing large numbers of microscope images, e.g., as required for medical diagnosis.

     

Peripheral electrosensory imaging by weakly electric fish

Different species have developed different solutions to the problem of constructing a representation of the environment from sensory images projected onto sensory surfaces. Comprehension of how these images are formed is an essential first step in understanding the representation of external reality by a given sensory system. Modeling of the electrical sensory images of objects began with the discovery of electroreception and continues to provide general insights into the mechanisms of imaging. Progress in electric image research has made it possible to establish the physical basis of electric imaging, as well as methods to accurately predict the electric images of objects alone and as a part of a natural electric scene. In this review, we show the following. (1) The internal low resistance of the fish’s body shapes the image in two different ways: by funneling the current generated by the electric organ to the sensory surface, it increases the fields rostrally, thus enhancing the perturbation produced by nearby objects; and by increasing the projected image. (2) The electric fish’s self-generated currents are modified by capacitive objects in a distinctive manner. These modulations can be detected by different receptor types, yielding the possibility of “electric color.” (3) The effects of different objects in a scene interact with each other, generating an image that is different from the simple addition of the images of individual objects, thus causing strong contextual effects.     

Standardization of karyotyping plant chromosomes by a newly developed chromosome image analyzing system (CHIAS)

Summary A chromosome image analyzing system (CHIAS) has been developed especially for plant chromosomes. A standard karyotyping method using CHIAS is also described. The characteristics of the CHIAS are as follows: 1) the main objects of the analysis are plant chromosomes, 2) it constructs a man-machine interactive system to put researchers' decisions into the analytical process, 3) it can automate the routine part of an analysis as much as possible, and 4) it digitizes the image information of chromosomes and analyzes them. Software for karyotype analysis of plant chromosomes has been developed. Thus, in the case of rye chromosomes, it is possible to get quantitative data for all chromosomes and a karyogram within 25 min.     

Root length and diameter measurement using NIH Image: application of the line-intercept principle for diameter estimation

The objective of this study was to develop an image analysis algorithm for estimating the length versus diameter distribution of washed root samples. Image analysis was performed using a Macintosh computer and the public domain NIH Image program. After an appropriate binary image of roots was obtained, the image was processed to get the thinned image to calculate the length of the roots. The edge pixel of the binary image was then deleted and root length was calculated again. This `edge deletion–length calculation' cycle was repeated until no root pixel was left in the image. Repeated edge deletion removed one pixel layer from around the periphery of root objects in each iteration. The number of edge deletions, which is equivalent to the intercept length, can be used to estimate the root diameter. We used the vertical or horizontal intercept length, whichever was shorter. The accuracy of diameter estimation due to orientation of objects varied from 89.1 to 126.0%. Branching root systems consist of several orders of laterals, and as the root branches to a higher order, the diameter of the roots becomes smaller. Therefore, edge deletions eliminate sequentially from the highest order roots, which have the smallest diameter, to the lowest order roots, which have the widest diameter. Thus, the length and diameter of each root order can be calculated by the proposed method. For verification, images of copper wire of 0.23, 0.50, and 1.0 mm diameter were analyzed. The results showed reasonable agreement with the expected distribution of length versus diameter for randomly oriented objects, and consequently the wire length of each diameter could be estimated. The proposed method was tested for primary and secondary roots of water-cultured rice (Oryza sativa L.), and it was proven that the method can provide accurate length and diameter measurements for each root order.     

Detection of blob objects in microscopic zebrafish images based on gradient vector diffusion.

The zebrafish has become an important vertebrate animal model for the study of developmental biology, functional genomics, and disease mechanisms. It is also being used for drug discovery. Computerized detection of blob objects has been one of the important tasks in quantitative phenotyping of zebrafish. We present a new automated method that is able to detect blob objects, such as nuclei or cells in microscopic zebrafish images. This method is composed of three key steps. The first step is to produce a diffused gradient vector field by a physical elastic deformable model. In the second step, the flux image is computed on the diffused gradient vector field. The third step performs thresholding and nonmaximum suppression based on the flux image. We report the validation and experimental results of this method using zebrafish image datasets from three independent research labs. Both sensitivity and specificity of this method are over 90%. This method is able to differentiate closely juxtaposed or connected blob objects, with high sensitivity and specificity in different situations. It is characterized by a good, consistent performance in blob object detection.     

Theoretical analysis of pre-receptor image conditioning in weakly electric fish

Electroreceptive fish detect nearby objects by processing the information contained in the pattern of electric currents through the skin. The distribution of local transepidermal voltage or current density on the sensory surface of the fish's skin is the electric image of the surrounding environment. This article reports a model study of the quantitative effect of the conductance of the internal tissues and the skin on electric image generation in Gnathonemus petersii (Günther 1862). Using realistic modelling, we calculated the electric image of a metal object on a simulated fish having different combinations of internal tissues and skin conductances. An object perturbs an electric field as if it were a distribution of electric sources. The equivalent distribution of electric sources is referred to as an object's imprimence. The high conductivity of the fish body lowers the load resistance of a given object's imprimence, increasing the electric image. It also funnels the current generated by the electric organ in such a way that the field and the imprimence of objects in the vicinity of the rostral electric fovea are enhanced. Regarding skin conductance, our results show that the actual value is in the optimal range for transcutaneous voltage modulation by nearby objects. This result suggests that "voltage" is the answer to the long-standing question as to whether current or voltage is the effective stimulus for electroreceptors. Our analysis shows that the fish body should be conceived as an object that interacts with nearby objects, conditioning the electric image. The concept of imprimence can be extended to other sensory systems, facilitating the identification of features common to different perceptual systems.     

A two-stage adaptive thresholding segmentation for noisy low-contrast images

Image recognition is the process of recognizing and classifying objects with machine learning algorithms. Image binarization is the first and most challenging step in image recognition, in which foreground objects are separated from their background. When foreground objects have complex morphological structure and background noise is strong, foreground objects are often being fractured into subcomponents. To address the over-segmentation issue of organisms with complex structures, we propose a 2-stage adaptive binarization approach based on Sauvola's binarization algorithm. We tested the effectiveness of the new approach on a set of underwater images with jellyfish collected in nearshore waters using a shadowgraph underwater plankton imaging system, PlanktonScope, because jellyfish have relatively complex structure and are often over-segemented. The results showed that the 2-stage approach improved the integrity of extracted jellyfish compared to traditional binarization methods, including Sauvola's algorithm. The analysis of local entropy values showed that the first stage effectively suppresses redundant information in the image and reduces the number of Region of Interests (ROIs), and the second stage preserves relatively weak and low-intensity signals to ensure the integrity of the extracted targets. The 2-stage approach improves hardware resource utilization and computational efficiency. It is robust for images acquired in sub-optimal conditions and enhances the accuracy of analytical results in the study of marine organisms using imaging systems.     

Electric images of two low resistance objects in weakly electric fish

Electroreceptive fish detect nearby objects by processing the information contained in the pattern of electric currents through their skin. In weakly electric fish, these currents arise from a self-generated field (the electric organ discharge), depending on the electrical properties of the surrounding medium. The electric image can be defined as the pattern of transepidermal voltage distributed over the receptive surface. To understand electrolocation it is necessary to know how electric image of objects are generated. In pulse mormyrids, the electric organ is localized at the tail, far from the receptors and fires a short biphasic pulse. Consequently, if all the elements in the environment are resistive, the stimulus at every point on the skin has the same waveform. Then, any measure of the amplitude (for example, the peak to peak amplitude) could be the unique parameter of the stimulus at any point of the skin. We have developed a model to calculate the image, corroborating that images are spread over the whole sensory surface and have an opposite center-surround, "Mexican-hat" shape. As a consequence, the images of different objects superimpose. We show theoretically and by simulation that the image of a pair of objects is not the simple addition of the individual images of these objects.     

CT image segmentation using FEM with optimized boundary condition

The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery.     

Multispectral/hyperspectral image enhancement for biological cell analysis.

BACKGROUND: Microscopes form projected images from illuminated objects, such as cellular tissue, which are recorded at a distance through the optical system's field of view. A telescope on a satellite or airplane also forms images with a similar optical projection of objects on the ground. Typical visible illuminations form a displayed set of three-color channels (Red Green Blue [RGB]) that are combined from three image sensor arrays (e.g., focal plane arrays) into a single pixel coding for each color present in the image. Analysis of these RGB color images develops a qualitative image representation of the objects. METHODS: Independent component analysis (ICA) is used for analysis and enhancement of multispectral images, and compared with the similar and widely used principal component analysis. RESULTS: The data examples indicate that the ICA enhancement, and the resulting RGB image combination display, can be useful in processing datacubes of cellular data where isolation of unknown subtle image elements representing objects is desired. CONCLUSIONS: ICA image enhancement can aid processing of datacubes of cellular data by clarifying subtle image elements. These parallelizable algorithms can be implemented for real-time, online analysis.     

3-D reconstruction of biological objects using underwater video technique and image processing

This paper describes a 3-D reconstruction method which allows accurate measurements of volume, surface area and other morphometric measurements of three-dimensional biological objects, without removing them from the sea. It represents a novel approach based on multiple views (eight resulted to be sufficient) from underwater video images and a new image processing procedure (MOD3D), whose application has met the basic requirements (i.e. to work on images recorded in turbid waters, with nonuniform lighting, to investigate large areas and in reasonable time, etc.) imposed when operating in the marine environment with simple, easy-to-use and nonprofessional equipment. It is a noninvasive, nondestructive and in the field fast method, thus suitable for sampling also at relevant depth, whose applicability has specifically been set up for a range of growth forms from massive to submassive and irregularly shaped. The accuracy of the method was assessed using models with three levels of 3-D complexity: simple, moderate and complex morphology. A high accuracy of volume measurements made through MOD3D image analysis software was achieved when compared with the laboratory water displacement method, which represents the most accurate method for volume measurement, with an overall mean percent error of about 1.7% (S.D. 2.2%). For all three levels of morphologic complexity, no significant differences (p>0.05) were found. Volume measurements obtained in field based on geometric approximation resulted rough, with significant differences from the MOD3D values (p<0.05). The geometric approximation was lower than MOD3D for simple and moderate morphology, and variable for complex morphology. For all three models, MOD3D values for surface area computation were consistently lower (mean error 13%) than the foil-wrapping values (p<0.05), due to overlap error when foil wrapping. Two applications were made with the bryozoan Pentapora fascialis and the coral Cladocora caespitosa to quantify carbonate standing stock and biomass of these two carbonate framework builders, whose importance has been recently recognised among the temperate sublittoral benthic species. Time required for the 3-D reconstruction method (about 3 h) makes it suitable for routine application particularly for relatively large area investigations, with irregularly shaped objects on rough substrate and several biological objects within the area.     

Image analysis as a tool for quantitative phycology: a computational approach to cyanobacterial taxa identification

In the following work we discuss the application of image processing and pattern recognition to the field of quantitative phycology. We overview the area of image processing and review previously published literature pertaining to the image analysis of phycological images and, in particular, cyanobacterial image processing. We then discuss the main operations used to process images and quantify data contained within them. To demonstrate the utility of image processing to cyanobacteria classification, we present details of an image analysis system for automatically detecting and classifying several cyanobacterial taxa of Lake Biwa, Japan. Specifically, we initially target the genus Microcystis for detection and classification from among several species of Anabaena. We subsequently extend the system to classify a total of six cyanobacteria species. High-resolution microscope images containing a mix of the above species and other nontargeted objects are analyzed, and any detected objects are removed from the image for further analysis. Following image enhancement, we measure object properties and compare them to a previously compiled database of species characteristics. Classification of an object as belonging to a particular class membership (e.g., “Microcystis,”“A. smithii,”“Other,” etc.) is performed using parametric statistical methods. Leave-one-out classification results suggest a system error rate of approximately 3%. Received: September 6, 1999 / Accepted: February 6, 2000     

Movable computer ruler (MCR): a new method for measuring the size of Toxoplasma gondii cysts, tachyzoites and other selected parasites

A new method for measuring the size of parasites and other objects using optical microscopy was developed using a specifically designed movable computer ruler (MCR) derived from digital images of a stage micrometer. Subsequently, MCR can be superimposed on images of parasites to measure their size. MCR derived from the stage micrometer under a particular objective lens can be used to measure the size of an object acquired by the same lens/microscope/camera system. The conditions are fixed for every superimposed image including width, height, pixel number and density. The MCR was tested using selected parasites, and shown to be as accurate as the ocular micrometer disk, screw micrometer eyepiece and image analysis software. The lower technical complexity of the MCR method makes it applicable even in laboratories with limited resources.     

Higuchi dimension of digital images

There exist several methods for calculating the fractal dimension of objects represented as 2D digital images. For example, Box counting, Minkowski dilation or Fourier analysis can be employed. However, there appear to be some limitations. It is not possible to calculate only the fractal dimension of an irregular region of interest in an image or to perform the calculations in a particular direction along a line on an arbitrary angle through the image. The calculations must be made for the whole image. In this paper, a new method to overcome these limitations is proposed. 2D images are appropriately prepared in order to apply 1D signal analyses, originally developed to investigate nonlinear time series. The Higuchi dimension of these 1D signals is calculated using Higuchi's algorithm, and it is shown that both regions of interests and directional dependencies can be evaluated independently of the whole picture. A thorough validation of the proposed technique and a comparison of the new method to the Fourier dimension, a common two dimensional method for digital images, are given. The main result is that Higuchi's algorithm allows a direction dependent as well as direction independent analysis. Actual values for the fractal dimensions are reliable and an effective treatment of regions of interests is possible. Moreover, the proposed method is not restricted to Higuchi's algorithm, as any 1D method of analysis, can be applied.     

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Weakly-scattering objects, such as small colloidal particles and most biological cells, are frequently encountered in microscopy. Indeed, a range of techniques have been developed to better visualize these phase objects; phase contrast and DIC are among the most popular methods for enhancing contrast. However, recording position and shape in the out-of-imaging-plane direction remains challenging. This report introduces a simple experimental method to accurately determine the location and geometry of objects in three dimensions, using digital inline holographic microscopy (DIHM). Broadly speaking, the accessible sample volume is defined by the camera sensor size in the lateral direction, and the illumination coherence in the axial direction. Typical sample volumes range from 200 µm x 200 µm x 200 µm using LED illumination, to 5 mm x 5 mm x 5 mm or larger using laser illumination. This illumination light is configured so that plane waves are incident on the sample. Objects in the sample volume then scatter light, which interferes with the unscattered light to form interference patterns perpendicular to the illumination direction. This image (the hologram) contains the depth information required for three-dimensional reconstruction, and can be captured on a standard imaging device such as a CMOS or CCD camera. The Rayleigh-Sommerfeld back propagation method is employed to numerically refocus microscope images, and a simple imaging heuristic based on the Gouy phase anomaly is used to identify scattering objects within the reconstructed volume. This simple but robust method results in an unambiguous, model-free measurement of the location and shape of objects in microscopic samples.     

Feature extraction from three-dimensional images in quantitative microscopy

Different methods are investigated in selecting and generating the appropriate microscope images for analysis of three-dimensional objects in quantitative microscopy. Traditionally, the ‘best’ focused image from a set is used for quantitative analysis. Such an objectively determined image is optimal for the extraction of some features, but may not be the best image for the extraction of all features. Various methods using multiple images are here developed to obtain a tighter distribution for all features.Three different approaches for analysis of images of stained cervical cells were analyzed. In the first approach, features are extracted from each image in the set. The feature values are then averaged to give the final result. In the second approach, a set of varying focused images are reconstructed to obtain a set of in-focus images. Features are then extracted from this set and averaged. In the third approach, a set of images in the three-dimensional scene is compressed into a single two-dimensional image. Four different compression methods are used. Features are then extracted from the resulting two-dimensional image. The third approach is employed on both the raw and transformed images.Each approach has its advantages and disadvantages. The first approach is fast and produces reasonable results. The second approach is more computationally expensive but produces the best results. The last approach overcomes the memory storage problem of the first two approaches since the set of images is compressed into one. The method of compression using the highest gradient pixel produces better results overall than other data reduction techniques and produces results comparable to the first approach.     

Simple method for quantification of fast plasma membrane movements.

We present a method for the quantification of the fast plasma membrane movements that are involved in ruffling, blebbing, fast shape change, and fast translocation. The method is based on the Kontron Vidas image analysis computer program. Video images from cells viewed through an inverted microscope were transmitted to the computer. The procedure was as follows: 4 consecutive video images were averaged (image 1); 28 s later a second set of 4 video images was averaged (image 2); image 2 was subtracted from image 1 and the grey level of each pixel of the resulting image was increased with 128 grey level units, resulting in the subtraction image, showing a uniform grey background speckled with brighter and darker spots corresponding to areas of movement. These spots were discriminated and turned into white objects against a black background. Interactive editing was used to delete artefacts that resulted from floating debris. The total area of the discriminated objects was measured, and the parameter motile area in micron2 per cell was calculated. We have applied our method to the study of motility induced in epithelial cell lines by the tumor promoter 12-O-tetradecanoyl-phorbol-13-acetate and by epidermal growth factor.