A novel image-analysis technique for kinematic study of growth and curvature

Kinematic analysis has provided important insights into the biology of growth by revealing the distribution of expansion within growing organs. Modern methods of kinematic analysis have made use of new image-tracking algorithms and computer-assisted evaluation, but these methods have yet to be adapted for examination of growth in a variety of plant species or for analysis of graviresponse. Therefore, a new image-analysis program, KineRoot, was developed to study spatio-temporal patterns of growth and curvature of roots. Graphite particles sprinkled on the roots create random patterns that can be followed by image analysis. KineRoot tracks the displacement of patterns created by the graphite particles over space and time using three search algorithms. Following pattern tracking, the edges of the roots are identified automatically by an edge detection algorithm that provides root diameter and root midline. Local growth rate of the root is measured by projecting the tracked points on the midline. From the shape of the root midline, root curvature is calculated. By combining curvature measurement with root diameter, the differential growth ratio between the greater and lesser curvature edges of a bending root is calculated. KineRoot is capable of analyzing a large number of images to generate local root growth and root curvature data over several hours, permitting kinematic analysis of growth and gravitropic responses for a variety of root types.     

Growth and Movement of Spot Inoculated Rhizobium meliloti on the Root Surface of Alfalfa

Inoculum droplets of approximately 10 nanoliter volume and containing about 10 Rhizobium meliloti cells were placed onto the root surface of alfalfa seedlings in plastic growth pouches at either the root tip, the position of the smallest emergent root hairs, or at a site midway between these points. The droplets were initially confined to an area of about 0.2 square millimeter at the point of application. By 48 and 96 hours after inoculation, the inoculum bacteria and their progeny were distributed over several centimeters of the root between the initial site of deposition and the growing root tip, reaching densities of 10³ to 10⁴ bacteria per centimeter near the site of initial deposition and decreasing exponentially from that point toward the root tip. Graphite particles deposited on the root surface close to the growing tip were similarly distributed along the root length by 48 and 96 hours, suggesting that passive displacement by root cell elongation was primarily responsible for the spread of bacteria. A nonmotile mutant of R. meliloti colonized alfalfa roots to the same extent as the wild type and was usually distributed in the same manner, indicating that bacterial motility contributed little under these conditions to long distance spread of the bacteria. However, when applied in low numbers, R. meliloti mutants defective in motility or chemotaxis were considerably less efficient in initiating nodules near the point of inoculation than the wild type. This implies that motility and/or chemotaxis contribute significantly to local exploration for suitable infection sites. Almost all nodules on the primary root formed within a few millimeters of the spot-inoculation site, indicating that, under our experimental conditions, movement and multiplication of R. meliloti on the root surface were not sufficient to maintain an adequate population in the infectible region of the root during root growth.     

Accuracy of Image Guidance Using Free-Breathing Cone-Beam Computed Tomography for Stereotactic Lung Radiotherapy

Movement of the target object during cone-beam computed tomography (CBCT) leads to motion blurring artifacts. The accuracy of manual image matching in image-guided radiotherapy depends on the image quality. We aimed to assess the accuracy of target position localization using free-breathing CBCT during stereotactic lung radiotherapy. The Vero4DRT linear accelerator device was used for the examinations. Reference point discrepancies between the MV X-ray beam and the CBCT system were calculated using a phantom device with a centrally mounted steel ball. The precision of manual image matching between the CBCT and the averaged intensity (AI) images restructured from four-dimensional CT (4DCT) was estimated with a respiratory motion phantom, as determined in evaluations by five independent operators. Reference point discrepancies between the MV X-ray beam and the CBCT image-guidance systems, categorized as left-right (LR), anterior-posterior (AP), and superior-inferior (SI), were 0.33 ± 0.09, 0.16 ± 0.07, and 0.05 ± 0.04 mm, respectively. The LR, AP, and SI values for residual errors from manual image matching were -0.03 ± 0.22, 0.07 ± 0.25, and -0.79 ± 0.68 mm, respectively. The accuracy of target position localization using the Vero4DRT system in our center was 1.07 ± 1.23 mm (2 SD). This study experimentally demonstrated the sufficient level of geometric accuracy using the free-breathing CBCT and the image-guidance system mounted on the Vero4DRT. However, the inter-observer variation and systematic localization error of image matching substantially affected the overall geometric accuracy. Therefore, when using the free-breathing CBCT images, careful consideration of image matching is especially important.     

A new image processing technique for determination of cell-generated deformations on substrata

This paper introduces a new image processing technique that determines the displacement field of a given substrate from “null-force” and “force-loaded” images. In this method, fluorescent elements used to track motion, which will be referred to as beads, can be seen in these images by locating the gray value that is normally distributed around their central point. Next comes a two-step process of matching the beads with displacements. The first step matches the beads with a small displacement using the correlation function of the characteristic pixels. Based on results from this initial step, another correlation function determines a pair of beads with a relatively large displacement. The entire matching process is done in this way, gradually working from the small displacement to the large one. Finally, using the cubic spline weight function, the whole displacement field is interpolated and filtered out of those displacements, which were initially found with the matched beads. Applying this new method on the cell migration yields satisfying results. Based on the particle tracking, the displacement field obtained by this new image processing technique has clear physical meaning. More importantly, this new method completes the matching of the displacement using the features of the displacement field, thus avoiding the direct matching with the image gray values for the relatively large strain of the substrate around the cell. Accordingly, it greatly decreases mismatching, making data checking unnecessary.     

A new image processing technique for determination of cell-generated deformations on substrata

This paper introduces a new image processing technique that determines the displacement field of a given substrate from "null-force" and "force-loaded" images. In this method, fluorescent elements used to track motion, which will be referred to as beads, can be seen in these images by locating the gray value that is normally distributed around their central point. Next comes a two-step process of matching the beads with displacements. The first step matches the beads with a small displacement using the correlation function of the characteristic pixels. Based on results from this initial step, another correlation function determines a pair of beads with a relatively large displacement. The entire matching process is done in this way, gradually working from the small displacement to the large one. Finally, using the cubic spline weight function, the whole displacement field is interpolated and filtered out of those displacements, which were initially found with the matched beads. Applying this new method on the cell migration yields satisfying results. Based on the particle tracking, the displacement field obtained by this new image processing technique has clear physical meaning. More importantly, this new method completes the matching of the displacement using the features of the displacement field, thus avoiding the direct matching with the image gray values for the relatively large strain of the substrate around the cell. Accordingly, it greatly decreases mismatching, making data checking unnecessary.     

RootAnalyzer: A Cross-Section Image Analysis Tool for Automated Characterization of Root Cells and Tissues

The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image’s background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.     

Automatic particle pickup method using a neural network has high accuracy by applying an initial weight derived from eigenimages: a new reference free method for single-particle analysis

The single-particle analysis is a structure-determining method for electron microscope (EM) images which does not require crystal. In this method, the projections are picked up and averaged by the images of similar Euler angles to improve the signal to noise ratio, and then create a 3-D reconstruction. The selection of a large number of particles from the cryo-EM micrographs is a pre-requisite for obtaining a high resolution. To pickup a low-contrast cryo-EM protein image, we have recently found that a three-layer pyramidal-type neural network is successful in detecting such a faint image, which had been difficult to detect by other methods. The connection weights between the input and hidden layers, which work as a matching filter, have revealed that they reflect characters of the particle projections in the training data. The images stored in terms of the connection weights were complex, more similar to the eigenimages which are created by the principal component analysis of the learning images rather than to the averages of the particle projections. When we set the initial learning weights according to the eigenimages in advance, the learning period was able to be shortened to less than half the time of the NN whose initial weights had been set randomly. Further, the pickup accuracy increased from 90 to 98%, and a combination of the matching filters were found to work as an integrated matching filter there. The integrated filters were amazingly similar to averaged projections and can be used directly as references for further two-dimensional averaging. Therefore, this research also presents a brand-new reference-free method for single-particle analysis.     

An automated program to find animals and crop photographs for individual recognition

Detailed data on individual animals are critical to ecological and evolutionary studies, but attaching identifying marks can alter individual fates and behavior leading to biases in parameter estimates and ethical issues. Individual-recognition software has been developed to assist in identifying many species from non-invasive photographic data. These programs utilize algorithms to find unique individual characteristics and compare images to a catalogue of known individuals. Currently, all applications for individual identification require manual processing to crop images so only the area of interest remains, or the area of interest must be manually delineated in each image. Thus, one of the main bottlenecks in processing data from photographic capture-recapture surveys is in cropping to an area of interest so that matching algorithms can identify the individual. Here, we describe the development and testing of an automated cropping program. The methods and techniques we describe are broadly applicable to any system where raw photos must be cropped down to a specific area of interest before pattern recognition software can be used for individual identification. We developed and tested the program for use with identification photos of wild giraffes.     

MicroFilament Analyzer,an image analysis tool for quantifying fibrillar orientation,reveals changes in microtubule organization during gravitropism

Image acquisition is an important step in the study of cytoskeleton organization. As visual interpretations and manual measurements of digital images are prone to errors and require a great amount of time, a freely available software package named MicroFilament Analyzer (MFA) was developed. The goal was to provide a tool that facilitates high‐throughput analysis to determine the orientation of filamentous structures on digital images in a more standardized, objective and repeatable way. Here, the rationale and applicability of the program is demonstrated by analyzing the microtubule patterns in epidermal cells of control and gravi‐stimulated Arabidopsis thaliana roots. Differential expansion of cells on either side of the root results in downward bending of the root tip. As cell expansion depends on the properties of the cell wall, this may imply a differential orientation of cellulose microfibrils. As cellulose deposition is orchestrated by cortical microtubules, the microtubule patterns were analyzed. The MFA program detects the filamentous structures on the image and identifies the main orientation(s) within individual cells. This revealed four distinguishable microtubule patterns in root epidermal cells. The analysis indicated that gravitropic stimulation and developmental age are both significant factors that determine microtubule orientation. Moreover, the data show that an altered microtubule pattern does not precede differential expansion. Other possible applications are also illustrated, including field emission scanning electron micrographs of cellulose microfibrils in plant cell walls and images of fluorescent actin.     

Distribution and redistribution of extension growth along vertical and horizontal gravireacting maize roots

Horizontal primary roots of Zea mays L. were photographed during the course of their gravireaction and during a preceding growth period in the vertical orientation. The displacement, by root elongation, of marker particles on the root surface was recorded. The particle-displacement rates were used to estimate the distribution of elemental elongation rates along opposite sides of the growing root apex. In the temperature range 21–25°C there was a stimulation of local elongation rates along the upper side of a gravireacting root and a reduction (and sometimes a cessation) of elongation along the lower side. Elemental elongation rates have been related to the development of root curvature, and the magnitude of the differential growth between upper and lower sides required for a particular rate of bending has also been estimated. The results complement, and are compatible with, findings relating to the distribution of certain endogenous growth regulators believed to participate in the gravireaction.Abbreviation RELEL relative elemental rate of elongation     

AnimalFinder: A semi-automated system for animal detection in time-lapse camera trap images

Although the use of camera traps in wildlife management is well established, technologies to automate image processing have been much slower in development, despite their potential to drastically reduce personnel time and cost required to review photos. We developed AnimalFinder in MATLAB® to identify animal presence in time-lapse camera trap images by comparing individual photos to all images contained within the subset of images (i.e. photos from the same survey and site), with some manual processing required to remove false positives and collect other relevant data (species, sex, etc.). We tested AnimalFinder on a set of camera trap images and compared the presence/absence results with manual-only review with white-tailed deer (Odocoileus virginianus), wild pigs (Sus scrofa), and raccoons (Procyon lotor). We compared abundance estimates, model rankings, and coefficient estimates of detection and abundance for white-tailed deer using N-mixture models. AnimalFinder performance varied depending on a threshold value that affects program sensitivity to frequently occurring pixels in a series of images. Higher threshold values led to fewer false negatives (missed deer images) but increased manual processing time, but even at the highest threshold value, the program reduced the images requiring manual review by ~ 40% and correctly identified > 90% of deer, raccoon, and wild pig images. Estimates of white-tailed deer were similar between AnimalFinder and the manual-only method (~ 1–2 deer difference, depending on the model), as were model rankings and coefficient estimates. Our results show that the program significantly reduced data processing time and may increase efficiency of camera trapping surveys.     

Fingerprint image enhancement using CNN filtering techniques

Due to noisy acquisition devices and variation in impression conditions, the ridgelines of fingerprint images are mostly corrupted by various kinds of noise causing cracks, scratches and bridges in the ridges as well as blurs. These cause matching errors in fingerprint recognition. For an effective recognition the correct ridge pattern is essential which requires the enhancement of fingerprint images. Segment by segment analysis of the fingerprint pattern yields various ridge direction and frequencies. By selecting a directional filter with correct filter parameters to match ridge features at each point, we can effectively enhance fingerprint ridges. This paper proposes a fingerprint image enhancement based on CNN Gabor-Type filters.     

Automated Confocal Laser Scanning Microscopy and Semiautomated Image Processing for Analysis of Biofilms

The purpose of this study was to develop and apply a quantitative optical method suitable for routine measurements of biofilm structures under in situ conditions. A computer program was designed to perform automated investigations of biofilms by using image acquisition and image analysis techniques. To obtain a representative profile of a growing biofilm, a nondestructive procedure was created to study and quantify undisturbed microbial populations within the physical environment of a glass flow cell. Key components of the computer-controlled processing described in this paper are the on-line collection of confocal two-dimensional (2D) cross-sectional images from a preset 3D domain of interest followed by the off-line analysis of these 2D images. With the quantitative extraction of information contained in each image, a three-dimensional reconstruction of the principal biological events can be achieved. The program is convenient to handle and was generated to determine biovolumes and thus facilitate the examination of dynamic processes within biofilms. In the present study, Pseudomonas fluorescens or a green fluorescent protein-expressing Escherichia coli strain, EC12, was inoculated into glass flow cells and the respective monoculture biofilms were analyzed in three dimensions. In this paper we describe a method for the routine measurements of biofilms by using automated image acquisition and semiautomated image analysis.     

Automated acquisition of electron microscopic random conical tilt sets

Single particle reconstruction using the random conical tilt data collection geometry is a robust method for the initial determination of macromolecular structures by electron microscopy. Unfortunately, the broad adoption of this powerful approach has been limited by the practical challenges inherent in manual data collection of the required pairs of matching high and low tilt images (typically 60 degrees and 0 degrees). The microscopist is obliged to keep the imaging area centered during tilting as well as to maintain accurate focus in the tilted image while minimizing the overall electron dose, a challenging and time consuming process. To help solve these problems, we have developed an automated system for the rapid acquisition of accurately aligned and focused tilt pairs. The system has been designed to minimize the dose incurred during alignment and focusing, making it useful in both negative stain and cryo-electron microscopy. The system includes a feature for montaging untilted images to ensure that all of the particles in the tilted image may be used in the reconstruction.     

Evaluation of image analysis and laser granulometry for microbial cell sizing

A direct cell size measurement technique and an image analysis based sizing method were developed. The former consisted of a manual size measurement of the two-dimensional cell images on a video screen, with automatic data recording. This method was chosen as the reference. The latter, a semiautomatic method took advantage of a commercial computer program designed for image processing and particle morphology analysis. It gave average and median size values which were compatible with the manual method. However, the performance of these time consuming methods is limited. Hence, the laser granulometry technique, intrinsically far more powerful while capable of analysing millions of sample objects in a short time delay, was applied. The comparison revealed that this method gives too low size values, particularly in disagreement with the known dimensions of the bacterial (Zymomonas mobilis) cells. A size correction method was developed to realign the granulometry results ofZ. mobilis cell samples with those of the direct manual measurement method.     

Nuclear volume estimation using different sampling, measurement and calculation methods

OBJECTIVE: To study the reliability of volume parameter measured on tissue sections through different sampling, measurement and calculation methods. STUDY DESIGN: The largest nuclear profile image under a 100x, NA 1.30 oil immersion objective of primary spermatocytes and spherical spermatoblasts on 11-micron-thick seminiferous tubule sections and tissue images, under a 20x objective, on 4-micron sections were captured. Their volumes were measured and calculated by the five methods provided by the Technology for Image and Graphics Engineering Research cell image analysis system. RESULTS: The nuclear volumes obtained by nucleator and area equivalent diameter on the largest nuclear profile image were almost the same, including binary images by automated and manual interactive nucleator and grey scale images only by the latter. Nuclear volumes, calculated by random Feret diameter and equivalent diameter of the perimeter, the minimal circumference of the largest nuclear profile binary image, were obviously larger than those of the nucleator and area equivalent diameter. Due to different-sized nuclear slices entrapped in the same section, those nuclear volumes from the seminiferous tubule tissue images were strikingly lower than that of the largest nuclei profile image. The shape factors of primary spermatocytes and spherical spermatoblast nuclei under 100x and 20x objectives were approximately the same. CONCLUSION: The sample preparation, sampling methods and calculation formulas suitable to nuclear form are necessary to obtain reproducible volume parameters.     

IMPACT OF THREE DIFFERENT MATCHING METHODS ON PATIENT SET-UP ERROR IN X-RAY VOLUMETRIC IMAGING FOR HEAD AND NECK CANCER

Impact of three different matching methods for delivery of Volumetric Modulated Arc Therapy (VMAT) in Cone-beam computed tomography (CBCT) on patient set-up error. As per institutional imaging protocol, 300 CBCT scans of 20 VMAT head and neck cancer patients treated with 60 Gy/30 fractions were chosen for the present study. Approved CT images of the plan were registered as a reference with the CBCT images on board. Grey-scale matching (GM), manual matching (MM), and bone matching (BM) between on-board CBCT and reference CT images were used to assess patient translation errors. Patient positioning verification was evaluated using the Clip-box registration in all three matching methods. Using the GM approach as a reference point, two additional matchings were rendered in offline mode using BM and MM. For analysis, random error (σ), systematic error (∑), maximum error (E) mean set-up error (M), mean displacement vector (R), matching time (Mt), and multiple comparisons using Post hoc Tukey's HSD test were performed. In MM, less random and systematic errors were found than in GM and BM with an insignificant difference (p > 0.05) Compared to BM and GM, the maximum error, mean set-up error, and displacement vector were marginally less in MM (p > 0.05). In MM, an increased Mt relative to BM and GM was observed (p > 0.05). Furthermore, an insignificant difference in set-up error was revealed in a multiple comparison test (p > 0.05). Any of the three matching methods can be used during CBCT to check patient translation errors for the delivery of the VMAT head and neck patients.     

Feature detection in human vision: a phase-dependent energy model

This paper presents a simple and biologically plausible model of how mammalian visual systems could detect and identify features in an image. We suggest that the points in a waveform that have unique perceptual significance as 'lines' and 'edges' are the points where the Fourier components of the waveform come into phase with each other. At these points 'local energy' is maximal. Local energy is defined as the square root of the sum of the squared response of sets of matched filters, of identical amplitude spectrum but differing in phase spectrum by 90 degrees: one filter type has an even-symmetric line-spread function, the other an odd-symmetric line-spread function. For a line the main contribution to the local energy peak is in the output of the even-symmetric filters, whereas for edges it is in the output of the odd-symmetric filters. If both filter types respond at the peak of local energy, both edges and lines are seen, either simultaneously or alternating in time. The model was tested with a series of images, and shown to predict well the position of perceived features and the organization of the images.