Quantitative morphology and shape classification of neurons by computerized image analysis

From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

Development of convolutional neural networks for recognition of tenogenic differentiation based on cellular morphology

BACKGROUND AND OBJECTIVE

The use of automated systems for image recognition is highly preferred for regenerative medicine applications to evaluate stem cell differentiation early in the culturing state with non-invasive methodologies instead of invasive counterparts. Bone marrow-derived mesenchymal stem cells (BMSCs) are able to differentiate into desired cell phenotypes, and thereby promise a proper cell source for tendon regeneration. The therapeutic success of stem cell therapy requires cellular characterization prior to the implantation of cells. The foremost problem is that traditional characterization techniques require cellular material which would be more useful for cell therapy, complex laboratory procedures, and human expertise. Convolutional neural networks (CNNs), a class of deep neural networks, have recently made great improvements in image-based classifications, recognition, and detection tasks. We, therefore, aim to develop a potential CNN model in order to recognize differentiated stem cells by learning features directly from image data of unlabelled cells.

METHODS

The differentiation of bone marrow mesenchymal stem cells (BMSCs) into tenocytes was induced with the treatment of bone morphogenetic protein-12 (BMP-12). Following the treatment and incubation step, the phase-contrast images of cells were obtained and immunofluorescence staining has been applied to characterize the differentiated state of BMSCs. CNN models were developed and trained with the phase-contrast cell images. The comparison of CNN models was performed with respect to prediction performance and training time. Moreover, we have evaluated the effect of image enhancement method, data augmentation, and fine-tuning training strategy to increase classification accuracy of CNN models. The best model was integrated into a mobile application.

RESULTS

All the CNN models can fit the biological data extracted from immunofluorescence characterization. CNN models enable the cell classification with satisfactory accuracies. The best result in terms of accuracy and training time is achieved by the model proposed based on Inception-ResNet V2 trained from scratch using image enhancement and data augmentation strategies (96.80%, 434.55 sec).

CONCLUSION

Our study reveals that the CNN models show good performance by identifying stem cell differentiation. Importantly this technique provides a faster and real-time tool in comparison to traditional methods enabling the adjustment of culture conditions during cultivation to improve the yield of therapeutic stem cells.

Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques

Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.

Statistical image processing quantifies the changes in cytoplasmic texture associated with aging in Caenorhabditis elegans oocytes

Background

Oocyte quality decreases with aging, thereby increasing errors in fertilization, chromosome segregation, and embryonic cleavage. Oocyte appearance also changes with aging, suggesting a functional relationship between oocyte quality and appearance. However, no methods are available to objectively quantify age-associated changes in oocyte appearance.

Results

We show that statistical image processing of Nomarski differential interference contrast microscopy images can be used to quantify age-associated changes in oocyte appearance in the nematode Caenorhabditis elegans. Max–min value (mean difference between the maximum and minimum intensities within each moving window) quantitatively characterized the difference in oocyte cytoplasmic texture between 1- and 3-day-old adults (Day 1 and Day 3 oocytes, respectively). With an appropriate parameter set, the gray level co-occurrence matrix (GLCM)-based texture feature Correlation (COR) more sensitively characterized this difference than the Max–min Value. Manipulating the smoothness of and/or adding irregular structures to the cytoplasmic texture of Day 1 oocyte images reproduced the difference in Max–min Value but not in COR between Day 1 and Day 3 oocytes. Increasing the size of granules in synthetic images recapitulated the age-associated changes in COR. Manual measurements validated that the cytoplasmic granules in oocytes become larger with aging.

Conclusions

The Max–min value and COR objectively quantify age-related changes in C. elegans oocyte in Nomarski DIC microscopy images. Our methods provide new opportunities for understanding the mechanism underlying oocyte aging.

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection

BACKGROUND

The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs.

PURPOSE

The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs.

MATERIALS AND METHODS

Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis.

RESULTS

For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively.

CONCLUSION

AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.

Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies

BACKGROUND

Brain tumor is the leading cause of death worldwide. It is obvious that the chances of survival can be increased if the tumor is identified and properly classified at an initial stage. MRI (Magnetic Resonance Imaging) is one source of brain tumors detection tool and is extensively used in the diagnosis of brain to detect blood clots. In the past, many researchers developed Computer-Aided Diagnosis (CAD) systems that help the radiologist to detect the abnormalities in an efficient manner.

OBJECTIVE

The aim of this research is to improve the brain tumor detection performance by proposing a multimodal feature extracting strategy and employing machine learning techniques.

METHODS

In this study, we extracted multimodal features such as texture, morphological, entropybased, Scale Invariant Feature Transform (SIFT), and Elliptic Fourier Descriptors (EFDs) from brain tumor imaging database. The tumor was detected using robust machine learning techniques such as Support Vector Machine (SVM) with kernels: polynomial, Radial Base Function (RBF), Gaussian; Decision Tree (DT), and Naïve Bayes. Most commonly used Jack-knife 10-fold Cross- Validation (CV) was used for testing and validation of dataset.

RESULTS

The performance was evaluated in terms of specificity, sensitivity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy (TA), Area under the receiver operating Curve (AUC), and P-value. The highest performance of 100% in terms of Specificity, Sensitivity, PPV, NPV, TA, AUC using Naïve Bayes classifiers based on entropy, morphological, SIFT and texture features followed by Decision Tree classifier with texture features (TA=97.81%, AUC=1.0) and SVM polynomial kernel with texture features (TA=94.63%). The highest significant p-value was obtained using SVM polynomial with texture features (P-value 2.65e-104) followed by SVM RB with texture features (P-value 1.96e-98).

CONCLUSION

The results reveal that Naïve Bayes followed by Decision Tree gives highest detection accuracy based on entropy, morphological, SIFT and texture features.

Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation

Microscopic images of neuronal cells provide essential structural information about the key constituents of the brain and form the basis of many neuroscientific studies. Computational analyses of the morphological properties of the captured neurons require first converting the structural information into digital tree-like reconstructions. Many dedicated computational methods and corresponding software tools have been and are continuously being developed with the aim to automate this step while achieving human-comparable reconstruction accuracy. This pursuit is hampered by the immense diversity and intricacy of neuronal morphologies as well as the often low quality and ambiguity of the images. Here we present a novel method we developed in an effort to improve the robustness of digital reconstruction against these complicating factors. The method is based on probabilistic filtering by sequential Monte Carlo estimation and uses prediction and update models designed specifically for tracing neuronal branches in microscopic image stacks. Moreover, it uses multiple probabilistic traces to arrive at a more robust, ensemble reconstruction. The proposed method was evaluated on fluorescence microscopy image stacks of single neurons and dense neuronal networks with expert manual annotations serving as the gold standard, as well as on synthetic images with known ground truth. The results indicate that our method performs well under varying experimental conditions and compares favorably to state-of-the-art alternative methods.

A Topological Representation of Branching Neuronal Morphologies

Many biological systems consist of branching structures that exhibit a wide variety of shapes. Our understanding of their systematic roles is hampered from the start by the lack of a fundamental means of standardizing the description of complex branching patterns, such as those of neuronal trees. To solve this problem, we have invented the Topological Morphology Descriptor (TMD), a method for encoding the spatial structure of any tree as a "barcode", a unique topological signature. As opposed to traditional morphometrics, the TMD couples the topology of the branches with their spatial extents by tracking their topological evolution in 3-dimensional space. We prove that neuronal trees, as well as stochastically generated trees, can be accurately categorized based on their TMD profiles. The TMD retains sufficient global and local information to create an unbiased benchmark test for their categorization and is able to quantify and characterize the structural differences between distinct morphological groups. The use of this mathematically rigorous method will advance our understanding of the anatomy and diversity of branching morphologies.

Analysis of Different Computational Techniques for Calculating the Polarizability Tensors of Stem Cells with Realistic Three-Dimensional Morphologies

Recently, the National Institute of Standards and Technology has developed a database of three-dimensional (3D) stem cell morphologies grown in ten different scaffolds to study the effect of the cells' environments on their morphologies. The goal of this work is to study the polarizability tensors of these stem cell morphologies, using three independent computational techniques, to quantify the effect of the environment on the electric properties of these cells. We show excellent agreement between the three techniques, validating the accuracy of our calculations. These computational methods allowed us to investigate different meshing resolutions for each stem cell morphology. After validating our results, we use a fast and accurate Pad' approximation formulation to calculate the polarizability tensors of stem cells for any contrast value between their dielectric permittivity and the dielectric permittivity of their environment. We also performed statistical analysis of our computational results to identify which environment generates cells with similar electric properties. The computational analysis and the results reported herein can be used for shedding light on the response of stem cells to electric fields in applications such as dielectrophoresis and electroporation and for calculating the electric properties of similar biological structures with complex 3D shapes.

Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

An Automated Strategy for Unbiased Morphometric Analyses and Classifications of Growth Cones In Vitro

During neural circuit development, attractive or repulsive guidance cue molecules direct growth cones (GCs) to their targets by eliciting cytoskeletal remodeling, which is reflected in their morphology. The experimental power of in vitro neuronal cultures to assay this process and its molecular mechanisms is well established, however, a method to rapidly find and quantify multiple morphological aspects of GCs is lacking. To this end, we have developed a free, easy to use, and fully automated Fiji macro, Conographer, which accurately identifies and measures many morphological parameters of GCs in 2D explant culture images. These measurements are then subjected to principle component analysis and k-means clustering to mathematically classify the GCs as “collapsed” or “extended”. The morphological parameters measured for each GC are found to be significantly different between collapsed and extended GCs, and are sufficient to classify GCs as such with the same level of accuracy as human observers. Application of a known collapse-inducing ligand results in significant changes in all parameters, resulting in an increase in ‘collapsed’ GCs determined by k-means clustering, as expected. Our strategy provides a powerful tool for exploring the relationship between GC morphology and guidance cue signaling, which in particular will greatly facilitate high-throughput studies of the effects of drugs, gene silencing or overexpression, or any other experimental manipulation in the context of an in vitro axon guidance assay.

Automated colon cancer detection using hybrid of novel geometric features and some traditional features

Automatic classification of colon into normal and malignant classes is complex due to numerous factors including similar colors in different biological constituents of histopathological imagery. Therefore, such techniques, which exploit the textural and geometric properties of constituents of colon tissues, are desired. In this paper, a novel feature extraction strategy that mathematically models the geometric characteristics of constituents of colon tissues is proposed. In this study, we also show that the hybrid feature space encompassing diverse knowledge about the tissues׳ characteristics is quite promising for classification of colon biopsy images. This paper thus presents a hybrid feature space based colon classification (HFS-CC) technique, which utilizes hybrid features for differentiating normal and malignant colon samples. The hybrid feature space is formed to provide the classifier different types of discriminative features such as features having rich information about geometric structure and image texture. Along with the proposed geometric features, a few conventional features such as morphological, texture, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) are also used to develop a hybrid feature set. The SIFT features are reduced using minimum redundancy and maximum relevancy (mRMR). Various kernels of support vector machines (SVM) are employed as classifiers, and their performance is analyzed on 174 colon biopsy images. The proposed geometric features have achieved an accuracy of 92.62%, thereby showing their effectiveness. Moreover, the proposed HFS-CC technique achieves 98.07% testing and 99.18% training accuracy. The better performance of HFS-CC is largely due to the discerning ability of the proposed geometric features and the developed hybrid feature space.

Dendritic tree extraction from noisy maximum intensity projection images in C. elegans

BACKGROUND

Maximum Intensity Projections (MIP) of neuronal dendritic trees obtained from confocal microscopy are frequently used to study the relationship between tree morphology and mechanosensory function in the model organism C. elegans. Extracting dendritic trees from noisy images remains however a strenuous process that has traditionally relied on manual approaches. Here, we focus on automated and reliable 2D segmentations of dendritic trees following a statistical learning framework.

METHODS

Our dendritic tree extraction (DTE) method uses small amounts of labelled training data on MIPs to learn noise models of texture-based features from the responses of tree structures and image background. Our strategy lies in evaluating statistical models of noise that account for both the variability generated from the imaging process and from the aggregation of information in the MIP images. These noisy models are then used within a probabilistic, or Bayesian framework to provide a coarse 2D dendritic tree segmentation. Finally, some post-processing is applied to refine the segmentations and provide skeletonized trees using a morphological thinning process.

RESULTS

Following a Leave-One-Out Cross Validation (LOOCV) method for an MIP databse with available "ground truth" images, we demonstrate that our approach provides significant improvements in tree-structure segmentations over traditional intensity-based methods. Improvements for MIPs under various imaging conditions are both qualitative and quantitative, as measured from Receiver Operator Characteristic (ROC) curves and the yield and error rates in the final segmentations. In a final step, we demonstrate our DTE approach on previously unseen MIP samples including the extraction of skeletonized structures, and compare our method to a state-of-the art dendritic tree tracing software.

CONCLUSIONS

Overall, our DTE method allows for robust dendritic tree segmentations in noisy MIPs, outperforming traditional intensity-based methods. Such approach provides a useable segmentation framework, ultimately delivering a speed-up for dendritic tree identification on the user end and a reliable first step towards further morphological characterizations of tree arborization.

Recent advances in morphological cell image analysis

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

A rapid, inexpensive high throughput screen method for neurite outgrowth

Neurite outgrowth assays are the most common phenotypic screen to assess chemical effects on neuronal cells. Current automated assays involve expensive equipment, lengthy sample preparation and handling, costly reagents and slow rates of data acquisition and analysis. We have developed a high throughput screen (HTS) for neurite outgrowth using a robust neuronal cell model coupled to fast and inexpensive visualization methods, reduced data volume and rapid data analysis. Neuroscreen-1 (NS-1) cell, a subclone of PC12, possessing rapid growth and enhanced sensitivity to NGF was used as a model neuron. This method reduces preparation time by using cells expressing GFP or native cells stained with HCS CellMask(™) Red in a multiplexed 30 min fixation and staining step. A 2x2 camera binning process reduced both image data files and analysis times by 75% and 60% respectively, compared to current protocols. In addition, eliminating autofocus steps during montage generation reduced data collection time. Pharmacological profiles for stimulation and inhibition of neurite outgrowth by NGF and SU6656 were comparable to current standard method utilizing immunofluorescence detection of tubulin. Potentiation of NGF-induced neurite outgrowth by members of a 1,120-member Prestwick compound library as assayed using this method identified six molecules, including etoposide, isoflupredone acetate, fludrocortisone acetate, thioguanosine, oxyphenbutazone and gibberellic acid, that more than doubled the neurite mass primed by 2 ng/ml NGF. This simple procedure represents an important routine approach in high throughput screening of large chemical libraries using the neurite outgrowth phenotype as a measure of the effects of chemical molecules on neuronal cells.

Neuron tracing in perspective

The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. (c) 2010 International Society for Advancement of Cytometry.

Interactive image analysis programs for quantifying injury-induced axonal beading and microtubule disruption

Focal axonal beading and focal disruption of microtubule structure are characteristic to traumatic axonal injury. We have recently reproduced these morphological and structural changes in our in vitro model system [D. Kilinc, G. Gallo, K.A. Barbee, Mechanically induced membrane poration causes axonal beading and localized cytoskeletal damage, Exp. Neurol. 212 (2008) 422-430]. In order to measure bead formation objectively, an observer-independent quantification of beading was necessary. In addition, a quantitative measure for the extent of co-localization of axonal beads and microtubule disruptions was required to establish a causal relationship between focal cytoskeletal damage and bead formation. In this paper we describe Matlab-based, interactive image analysis programs for axonal beading quantification and co-localization analysis. Injury-induced increases in the axonal beading could be successfully detected using the bead analysis program.

Automated screening of neurite outgrowth

Outgrowth of neurites in culture is used for assessing neurotrophic activity. Neurite measurements have been performed very slowly using manual methods or more efficiently with interactive image analysis systems. In contrast, medium-throughput and noninteractive image analysis of neurite screens has not been well described. The authors report the performance of an automated image acquisition and analysis system (IN Cell Analyzer 1000) in the neurite assay. Neuro-2a (N2a) cells were plated in 96-well plates and were exposed to 6 conditions of retinoic acid. Immunofluorescence labeling of the cytoskeleton was used to detect neurites and cell bodies. Acquisition of the images was automatic. The image set was then analyzed by both manual tracing and automated algorithms. On 5 relevant parameters (number of neurites, neurite length, total cell area, number of cells, neurite length per cell), the authors did not observe a difference between the automated analysis and the manual analysis done by tracing. These data suggest that the automated system addresses the same biology as human scorers and with the same measurement precision for treatment effects. However, throughput of the automated system is orders of magnitude higher than with manual methods.

Computer-vision-based extraction of neural dendrograms

This work introduces a new approach to the characterization of neural cells by means of semi-automated generation of dendrograms; data structures which describe the inherently hierarchical nature of neuronal arborizations. Dendrograms describe the branched structure of neurons in terms of the length, average thickness and bending energy of each of the dendritic segments and allow in a straightforward manner, the inclusion of additional measures. The bending energy quantifies the complexity of the shape and can be used to characterize the spatial coverage of the arborizations (the bending energy is an alternative for other complexity measures such as the fractal dimension). The new approach is based on the partitioning of the cell's outer contour as a function of the high curvature points followed by a syntactical analysis of the segmented contours. The semi-automated method is robust and is an improvement on the time consuming manual generation of the dendrograms. Several experimental results are included in this paper which illustrate and corroborate the effectiveness of the approach. The technique presented in this paper is limited to planar neurons but could be extended to a 3D approach.

Image processing techniques for quantitative analysis of skin structures

Computer-based image processing and analysis techniques were developed for quantitative analysis of skin structures in color histological sections. Performance was compared with traditional non-image processing counting methods. Skin sections were stained with Masson's trichrome, hematoxylin and eosin, picrosirius red, or one of several elastin stains. The image processing software identified the top of the cellular epidermis and the dermal-epidermal junction and then calculated the volume of the cellular layer of the epidermis, epidermal thickness, and the ratio of the dermal-epidermal junction surface area to the in-plane surface area. It also identified cells and collagen and calculated cellular densities and collagen densities in the papillary and reticular layers of the dermis. Attempts to computationally process elastin-stained sections to determine elastin density were unsuccessful. The described techniques were used in a preliminary study to compare mechanically stressed skin with control skin. Results showed significant differences in cellular density in the papillary dermis and collagen density in the reticular dermis for skin subjected to combined shear/compression or tension compared with an unstressed control. Measurements made with the computer technique and traditional technique showed comparable results; the mean difference in measurements for epidermal features was 5.33% while for dermal features it was 2.76%. Significance testing between control and experimental groups showed similar results, though for three of the 28 comparisons the computer method identified a significant difference while the traditional method did not. The computer method took longer to conduct than the traditional method, though with recent advances in computer hardware this time difference would be eliminated.

Quantitative assessment of angiogenesis in the chick embryo and its chorioallantoic membrane by computerised analysis of angiographic images

We studied, in vivo, the angiogenesis process in the chick embryo and its chorioallantoic membrane (CAM) using digital subtraction angiography (DSA) in conjunction with computer-assisted image analysis. In a series of fertilised eggs, angiography was carried out at days 8, 10, 12 and 14 of embryonic development. The angiographic images were digitised and subsequently processed for a specific image analysis. A set of specific morphological parameters has been defined to allow an analytical characterisation of the vascularity status. Vessels were classified into three categories according to their diameter (50-100, 100-200, and > 200 microm). The data were normalised and statistically evaluated. Graphs showing the development of angiogenesis were obtained. Total vascular area revealed a continuous rise, whereas, total vascular length increased until day 12 and then it started decreasing. These morphometric parameters in the first two vessel categories progressively increased throughout the entire period of development, whereas in the third category they increased until day 10 and then they started decreasing. By applying a vascular casting technique CAM vessels were visualised and compared with those extracted from the processed angiographic image. The comparison revealed that there is exact matching for the first two vessel categories (diameters higher than 100 microm) while the matching of the third category (diameters between 50 and 100 microm) is approximate.

A simple procedure for morphometric analysis of processes and growth cones of neurons in culture using parameters derived from the contour and convex hull of the object

Morphometric estimation of neuronal processes is currently laborious and time-consuming, since the individual processes (axons and dendrites) have to be traced manually. In order to facilitate the measurement of cellular processes, we have tested a series of parameters derived from the contour and the convex hull of an object and estimated to which extent they reflect process length and number. The parameters included the area, perimeter and form factor of the object and convex hull, their ratios as well as object length, breadth, width, length/width and spreading index. Some new parameters derived from the contour and convex hull of the object, were also computed: process index (the number of areas contained within the convex hull outside the object contour), process domain (the total area contained within the convex hull outside the object contour), their ratio and the square root of the process domain (SR process domain). In total, 18 parameters were estimated. Populations of motoneurons, growth cones of cerebellar granule cells and N2a neuroblastoma cells were utilized due to their diversity in morphological features. The processes of each object were drawn by hand to establish the actual length and number. Total process length per object correlated strongly with object perimeter, process domain and SR process domain. The number of processes per object correlated well with perimeter ratio, process index and form factor, whereas object length, convex hull perimeter and spreading index correlated acceptably with the average process length. Using these parameters for the evaluation of neurite outgrowth in developing of hippocampal neurons in vitro, variables such as object perimeter, process domain and SR process domain were found to be very well suited for estimation of the total length of neurites. We conclude that based on the contour and convex hull of an object it is possible to calculate a series of parameters which may substitute direct measurements of process length.