Segmentation Of Fibers Research Articles

Reconstruction of White Matter Tracts via Repeated Deterministic Streamline Tracking – Initial Experience

Diffusion Tensor Imaging (DTI) and fiber tractography are established methods to reconstruct major white matter tracts in the human brain in-vivo. Particularly in the context of neurosurgical procedures, reliable information about the course of fiber bundles is important to minimize postoperative deficits while maximizing the tumor resection volume. Since routinely used deterministic streamline tractography approaches often underestimate the spatial extent of white matter tracts, a novel approach to improve fiber segmentation is presented here, considering clinical time constraints. Therefore, fiber tracking visualization is enhanced with statistical information from multiple tracking applications to determine uncertainty in reconstruction based on clinical DTI data. After initial deterministic fiber tracking and centerline calculation, new seed regions are generated along the result’s midline. Tracking is applied to all new seed regions afterwards, varying in number and applied offset. The number of fibers passing each voxel is computed to model different levels of fiber bundle membership. Experimental results using an artificial data set of an anatomical software phantom are presented, using the Dice Similarity Coefficient (DSC) as a measure of segmentation quality. Different parameter combinations were classified to be superior to others providing significantly improved results with DSCs of 81.02%±4.12%, 81.32%±4.22% and 80.99%±3.81% for different levels of added noise in comparison to the deterministic fiber tracking procedure using the two-ROI approach with average DSCs of 65.08%±5.31%, 64.73%±6.02% and 65.91%±6.42%. Whole brain tractography based on the seed volume generated by the calculated seeds delivers average DSCs of 67.12%±0.86%, 75.10%±0.28% and 72.91%±0.15%, original whole brain tractography delivers DSCs of 67.16%, 75.03% and 75.54%, using initial ROIs as combined include regions, which is clearly improved by the repeated fiber tractography method.

LABELING WHITE MATTER TRACTS IN HARDI BY FUSING MULTIPLE TRACT ATLASES WITH APPLICATIONS TO GENETICS.

Accurate identification of white matter structures and segmentation of fibers into tracts is important in neuroimaging and has many potential applications. Even so, it is not trivial because whole brain tractography generates hundreds of thousands of streamlines that include many false positive fibers. We developed and tested an automatic tract labeling algorithm to segment anatomically meaningful tracts from diffusion weighted images. Our multi-atlas method incorporates information from multiple hand-labeled fiber tract atlases. In validations, we showed that the method outperformed the standard ROI-based labeling using a deformable, parcellated atlas. Finally, we show a high-throughput application of the method to genetic population studies. We use the sub-voxel diffusion information from fibers in the clustered tracts based on 105-gradient HARDI scans of 86 young normal twins. The whole workflow shows promise for larger population studies in the future.

AUTOMATIC SEGMENTATION AND STRUCTURAL CHARACTERIZATION OF LOW DENSITY FIBREBOARDS

In this paper, a new skeleton-based algorithm for the segmentation of individual fibres in 3D tomographic images is described. The proposed method is designed to deal with low density materials featuring fibres with varied sizes, shapes and tortuosities, like composite fibreboards used for buildings insulation. To this end the paths of the skeleton are first classified according to their connectivity, the connectivity of their adjacent nodes, their orientation, their average radius and the variation of the distance transform along each path. This allows for the identification of spurious paths and paths linking two fibres. Reconstruction of the path of the fibres is done thanks to an optimal pairing algorithm which joins paths that show the most similar orientation and radius at each node/link. The segmented skeleton is finally dilated by means of a growing algorithm ordonned by the average radius of the fibres in order to reconstruct each identified fibres. As an application, the algorithm is used to segment a 3D tomographic image of hemp/polymer fibreboard for buildings insulation. Information such as the number of contacts, tortuosity, length, average radius, orientation of fibres are finally measured on both the segmented skeleton and reconstructed image, which allow for a thorough characterization of the fibre network.

Software-Based Imaging and Segmentation of Corneal Nerve Fibres

Article Software-Based Imaging and Segmentation of Corneal Nerve Fibres was published on September 7, 2013 in the journal Biomedical Engineering / Biomedizinische Technik (volume 58, issue SI-1-Track-L).

Radiation therapy effects on white matter fiber tracts of the limbic circuit

To segment fiber tracts in the limbic circuit and to assess their sensitivity to radiation therapy (RT). Twelve patients with brain metastases who had received fractionated whole brain radiation therapy to 30 Gy or 37.5 Gy were included in the study. Diffusion weighted images were acquired pre-RT, at the end of RT, and 1-month post-RT. The fornix, corpus callosum, and cingulum were extracted from diffusion weighted images by combining fiber tracking and segmentation methods based upon characteristics of the fiber bundles. Cingulum was segmented by a seed-based tractography, fornix by a region of interests (ROI)-based tractography, and corpus callosum by a level-set segmentation algorithm. The radiation-induced longitudinal changes of diffusion indices of the structures were evaluated. Significant decreases were observed in the fractional anisotropy of the posterior part of the cingulum, fornix, and corpus callosum from pre-RT to end of RT by -14.0%, -12.5%, and -5.2%, respectively (p < 0.001), and from pre-RT to 1-month post-RT by -11.9%, -12.8%, and -6.4%, respectively (p < 0.001). Moreover, significant increases were observed in the mean diffusivity of the corpus callosum and the posterior part of the cingulum from pre-RT to end of RT by 6.8% and 6.5%, respectively, and from pre-RT to 1-month post-RT by 8.5% and 6.3%, respectively. The increase in the radial diffusivity primarily contributed to the significant decrease in the fractional anisotropy, indicating that demyelination is the predominant radiation effect on the white matter structures. Our findings indicate that the fornix and the posterior part of the cingulum are significantly susceptible to radiation damage. We have developed robust computer-aided semiautomatic segmentation and fiber tracking tools to facilitate the ROI delineation of critical structures, which is important for assessment of radiation damage in a longitudinal fashion.

WE‐A‐217A‐01: The Effects of Radiation Therapy on White Matter Fiber Tracts of the Limbic Circuit

Purpose: To assess radio‐sensitivity of white matter fiber tracts interconnected to hippocampus. Methods: Twelve patients who had brain metastases and received fractionated whole brain radiation therapy (WBRT) (30 or 37.5G) were included in the study. Diffusion weighted images were acquired pre RT, end of RT, and 1 ‐month post‐RT. The fornix, cingulum and corpus callosum were extracted from diffusion weighted images by combining fiber tracking and segmentation methods based upon characteristics of the fiber bundles. The ingulum was segmented by a seed‐ based tractography, the fornix by an ROI‐based tractography, and the corpus callosum by a level‐set segmentation algorithm. The radiation‐induced longitudinal changes in diffusion indices of the structures were evaluated. Results: Significant percentage decreases were observed in fractional anisotropy in the cingulum posterior part, fornix, and corpus callosum from pre RT to end of RT by −14.0%, −12.5%, and −5.2%, respectively (p&lt;0.001), and from pre RT to 1‐month post‐RT by −11.9%, −12.8%, and −6.4%, respectively (p&lt;0.001). Moreover, significant percentage increases were observed in mean diffusivity in the corpus callosum and cingulum posterior part from pre RT to end of RT by 6.8% and 6.5%, respectively, and from pre RT to 1‐month post‐RT by 8.5% and 6.3%, respectively. Furthermore, significant percentage increases were observed in radial diffusivity anisotropy in the cingulum posterior part, fornix, and corpus callosum from pre RT to end of RT by 11.4%, 7.1%, and 17.0%, respectively, and from pre RT to 1‐month post‐RT by 10.5%, 4.9%, and 21.8%, respectively (p&lt;0.001). The increase in radial diffusivity primarily contributed to the significant decrease in fractional anisotropy, indicating demyelination is the predominant radiation effect on the white matter structures. Conclusions: Our findings indicate the fornix and cingulum posterior part are susceptible to radiation damage, particularly to demyelination. Further investigation of the relationship between the structural damage and cognitive dysfunction is on‐going. Supported in part by National Institutes of Health grants RO1 NS064973

Information-Theoretic Approach for Automated White Matter Fiber Tracts Reconstruction

Fiber tracking is the most popular technique for creating white matter connectivity maps from diffusion tensor imaging (DTI). This approach requires a seeding process which is challenging because it is not clear how and where the seeds have to be placed. On the other hand, to enhance the interpretation of fiber maps, segmentation and clustering techniques are applied to organize fibers into anatomical structures. In this paper, we propose a new approach to automatically obtain bundles of fibers grouped into anatomical regions. This method applies an information-theoretic split-and-merge algorithm that considers fractional anisotropy and fiber orientation information to automatically segment white matter into volumes of interest (VOIs) of similar FA and eigenvector orientation. For each VOI, a number of planes and seeds is automatically placed in order to create the fiber bundles. The proposed approach avoids the need for the user to define seeding or selection regions. The whole process requires less than a minute and minimal user interaction. The agreement between the automated and manual approaches has been measured for 10 tracts in a DTI brain atlas and found to be almost perfect (kappa > 0.8) and substantial (kappa > 0.6). This method has also been evaluated on real DTI data considering 5tracts. Agreement was substantial (kappa > 0.6) in most of the cases.

A STOCHASTIC SHAPE AND ORIENTATION MODEL FOR FIBRES WITH AN APPLICATION TO CARBON NANOTUBES

Methods are introduced for analysing the shape and orientation of planar fibres from greyscale images of fibrous systems. The sequence of image processing techniques needed for segmentation of fibres is described. The identified fibres were interpreted as deformed line segments for which two shape and two orientation parameters are estimated by the maximum likelihood method. The methods introduced are shown to perform quite well for simulated systems of deformed line segments with known properties. They were applied to TEM images of carbon nanotubes embedded in polycarbonate.

Segmentation of nerve fibers using multi-level gradient watershed and fuzzy systems

This paper presents an algorithm based on multi-level watershed segmentation combined with three fuzzy systems to segment a large number of myelinated nerve fibers in microscope images. The method can estimate various geometrical parameters of myelinated nerve fibers in peripheral nerves. It is expected to be a promising tool for the quantitative assessment of myelinated nerve fibers in related research. A novel multi-level watershed scheme iteratively detects pre-candidate nerve fibers. At each immersion level, watershed segmentation extracts the initial axon locations and obtains meaningful myelinated nerve fiber features. Thereafter, according to a priori characteristics of the myelinated nerve fibers, fuzzy rules reject unlikely pre-candidates and collect a set of candidates. Initial candidate boundaries are then refined by a fuzzy active contour model, which flexibly deforms contours according to the observed features of each nerve fiber. A final scan with a different set of fuzzy rules based on the a priori properties of the myelinated nerve fibers removes false detections. A particle swarm optimization method is employed to efficiently train the large number of parameters in the proposed fuzzy systems. The proposed method can automatically segment the transverse cross-sections of nerve fibers obtained from optical microscope images. Although the microscope image is usually noisy with weak or variable levels of contrast, the proposed system can handle images with a large number of myelinated nerve fibers and achieve a high fiber detection ratio. As compared to manual segmentation by experts, the proposed system achieved an average accuracy of 91% across different data sets. We developed an image segmentation system that automatically handles myelinated nerve fibers in microscope images. Experimental results showed the efficacy of this system and its superiority to other nerve fiber segmentation approaches. Moreover, the proposed method can be extended to other applications of automatic segmentation of microscopic images.

Non-destructive characterisation of polymers and Al-alloys by polychromatic cone-beam phase contrast tomography

X-ray computed tomography (XCT) has become a very important tool for the non-destructive characterisation of materials. Continuous improvements in the quality and performance of X-ray tubes and detectors have led to cone-beam XCT systems that can now achieve spatial resolutions down to 1 {mu}m and even below. Since not only the amplitude but also the phase of an X-ray beam is altered while passing through an object, phase contrast effects can occur even for polychromatic sources when the spatial coherence due to a small focal spot size is high enough. This can lead to significant improvements over conventional attenuation-based X-ray computed tomography. Phase contrast can increase by edge enhancement the visibility of small structures and of features which are only slightly different in attenuation. We report on the possibilities of polychromatic cone-beam phase contrast tomography for non-destructive characterisation of materials. A carbon fibre-reinforced polymer and the Al-alloys AlMg5Si7 and AlSi18 were investigated with high resolution cone-beam X-ray computed tomography with a polychromatic tube source. Under certain conditions strong phase contrast resulting in an upward and downward overshooting of the grey values across edges was observed. The phase effects are much stronger for the polymer than for the Al-alloys. The influencemore » on the phase contrast of the parameters, including source-detector distance, focal spot size and tube acceleration voltage is presented. Maximum phase contrast was observed for a maximum distance between the source and the detector, for a low voltage and a minimum focal spot size at the X-ray source. The detectability of the different phases is improved by the edge enhancement and the resulting improvement of sharpness. Thus, a better segmentation of the carbon fibres in the fibre-reinforced polymer and of the Mg{sub 2}Si-phase in the AlMg5Si7-alloy is achieved. Primary and eutectic Si cannot be detected by attenuation-based X-ray computed tomography due to the small difference in X-ray attenuation between the Si and the Al-matrix in AlSi18. However, phase contrast effects lead to a significant detectability and the large primary Si particles become clearly visible in the tomograms of AlSi18. - Highlights: Black-Right-Pointing-Pointer Phase contrast increases with increasing distance object-detector and with decreasing U, X-ray spot. Black-Right-Pointing-Pointer The detectability of details is improved by the phase contrast. Black-Right-Pointing-Pointer For the carbon fibre-reinforced polymer the visibility of the carbon fibres is improved. Black-Right-Pointing-Pointer For AlMg5Si7 the detectability of the phases Fe-aluminides and Mg{sub 2}Si particles is improved. Black-Right-Pointing-Pointer For AlSi18 primary Si particles with a size > 50 {mu}m become detectable because of the phase contrast.« less

Subchronic administration of Catha edulis F. (khat) extract is marked by elevation of cardiac biomarkers and subendocardial necrosis besides blood pressure alteration in rats

ETHNPHARMACOLOGICAL RELEVANCE: Khat is a widely chewed herb for its stimulant effect, however, its effects on the cardiovascular system are a source of growing concern, as prevalence of chewing is increasing and susceptible individuals may experience cardiovascular episodes. This study attempted to evaluate cardiovascular substrates that predispose individuals to these episodes by using both biochemical and morphologic-pathologic studies. Rats were treated with either Tween 80 (2% in distilled water) (CON), or khat extract (100mg/kg, K100; 200mg/kg, K200; or 400mg/kg, K400 doses) orally for 6 weeks. Blood pressure (BP) in each group was measured before dosing and 1, 2 and 3h after-dose as well as weekly for 6 weeks using Tail cuff method. On day 45, blood was drawn for assessment of cardiac biomarkers and animals were sacrificed, and histological examination was undertaken for any overt damage on the myocardium. K400 was the only dose that significantly increased BP at 2 (p<0.05) and 3h (p<0.001) postdose compared to predose level. Likewise, the 3h postdose BP of each week was significantly greater (p<0.001) than baseline BP only at 400mg/kg. However, when the weekly values were compared among themselves, the difference was not statistically significant and a progressive change in postdose BP had not been observed. On the other hand, predose systolic BP of K400 rats tended to decline at week 3 and significantly decreased (p<0.05) beyond week 4 compared to baseline values, but the decline was not significant for the rest of the doses. Biomarker assessments revealed that whereas levels of total creatine kinase were found to be elevated significantly for K100 (p<0.05), K200 and K400 (p<0.001 in both cases); aspartate aminotransferease was increased in K200 (p<0.01) and K400 (p<0.001) compared to CON rats. By contrast, levels of cardiac troponin T was significantly increased (p<0.001) only in K400 rats. Heart tissues of CON and K100 rats were normal, while those from K200 showed signs of focal lesions but normal architecture of the myocardium was maintained. K400 rats, however, displayed fragmentation and segmentation of fibers, edema and mottled staining. These findings collectively indicate that the high dose of crude khat extract modulated most of the hemodynamic, biochemical and histopathological parameters in rats and hence chronic use of khat at higher dose and for longer sessions bear a significant risk for cardiovascular morbidities.

Microscopic image analysis for quantitative characterization of muscle fiber type composition

Skeletal muscles consist of muscle fibers that are responsible for contracting and generating force. Skeletal muscle fibers are categorized into distinct subtypes based on several characteristics such as contraction time, force production and resistance to fatigue. The composition of distinct muscle fibers in terms of their number and cross-sectional areas is characterized by a histological examination. However, manual delineation of individual muscle fibers from digitized muscle histology tissue sections is extremely time-consuming. In this study, we propose an automated image analysis system for quantitative characterization of muscle fiber type composition. The proposed system operates on digitized histological muscle tissue slides and consists of the following steps: segmentation of muscle fibers, registration of successive slides with distinct stains, and classification of muscle fibers into distinct subtypes. The performance of the proposed approach was tested on a dataset consisting of 25 image pairs of successive muscle histological cross-sections with different ATPase stain. Experimental results demonstrate a promising overall segmentation and classification accuracy of 89.1% in identifying muscle fibers of distinct subtypes.

Segmentation and tracking of cytoskeletal filaments using open active contours

We use open active contours to quantify cytoskeletal structures imaged by fluorescence microscopy in two and three dimensions. We developed an interactive software tool for segmentation, tracking, and visualization of individual fibers. Open active contours are parametric curves that deform to minimize the sum of an external energy derived from the image and an internal bending and stretching energy. The external energy generates (i) forces that attract the contour toward the central bright line of a filament in the image, and (ii) forces that stretch the active contour toward the ends of bright ridges. Images of simulated semiflexible polymers with known bending and torsional rigidity are analyzed to validate the method. We apply our methods to quantify the conformations and dynamics of actin in two examples: actin filaments imaged by TIRF microscopy in vitro, and actin cables in fission yeast imaged by spinning disk confocal microscopy.

Automatic Determination of Fiber-Length Distribution in Composite Material Using 3D CT Data

Determining fiber length distribution in fiber reinforced polymer components is a crucial step in quality assurance, since fiber length has a strong influence on overall strength, stiffness, and stability of the material. The approximate fiber length distribution is usually determined early in the development process, as conventional methods require a destruction of the sample component. In this paper, a novel, automatic, and nondestructive approach for the determination of fiber length distribution in fiber reinforced polymers is presented. For this purpose, high-resolution computed tomography is used as imaging method together with subsequent image analysis for evaluation. The image analysis consists of an iterative process where single fibers are detected automatically in each iteration step after having applied image enhancement algorithms. Subsequently, a model-based approach is used together with a priori information in order to guide a fiber tracing and segmentation process. Thereby, the length of the segmented fibers can be calculated and a length distribution can be deduced. The performance and the robustness of the segmentation method is demonstrated by applying it to artificially generated test data and selected real components.

UNSUPERVISED SEGMENTATION OF COLLAGEN FIBER DISTRIBUTION IN DIFFERENT STAGES OF OSF

The objective of this paper is to describe the comparative efficacy of three signal processing-based feature extraction methodologies for classifying normal oral mucosa, early and advanced stages of Oral Submucous Fibrosis (OSF) by unsupervised segmentation of sub-epithelial collagen fibers. Wavelet and discrete cosine transform (DCT) based energy features are extracted from Transmission Electron Micrographs (TEM) of collagen fibers of different stages of OSF in non overlapping blocks which in turn are classified by fuzzy c-means clustering. The overall efficacy of DCT-based features have been found better in comparison to its wavelet-based counterpart.

White matter fiber tract segmentation in DT-MRI using geometric flows

In this paper, we present a 3D geometric flow designed to segment the main core of fiber tracts in diffusion tensor magnetic resonance images. The fundamental assumption of our fiber segmentation technique is that adjacent voxels in a tract have similar properties of diffusion. The fiber segmentation is carried out with a front propagation algorithm constructed to fill the whole fiber tract. The front is a 3D surface that evolves with a propagation speed proportional to a measure indicating the similarity of diffusion between the tensors lying on the surface and their neighbors in the direction of propagation. We use a level set implementation to assure a stable and accurate evolution of the surface and to handle changes of topology of the surface during the evolution process. The fiber tract segmentation method does not need a regularized tensor field since the surface is automatically smoothed as it propagates. The smoothing is done by an intrinsic surface force, based on the minimal principal curvature. This segmentation can be used for obtaining quantitative measures of the diffusion in the fiber tracts and it can also be used for white matter registration and for surgical planning.

An experimental method for measuring the mean length of cerebellar parallel fibers: validation and derivation of a correction factor by computational simulation and probability analysis

The length of cerebellar parallel fibers is important for information integration by the Purkinje cells. Based on the Copernican principle for analyzing the length of stochastic events, we have recently devised a stochastic method to estimate the mean length of parallel fibers within a given cerebellar region. The purpose of the present report is to provide validation of this methodology via computational simulations. We create virtual parallel fibers with known lengths and program each step of our stochastic method for computational simulation. We then compare the observed mean length obtained from our computational simulation with the known mean length of the virtual parallel fibers. In particular, we investigate the effect of cutting parallel fibers into segments during histological sectioning. Our computational results reveal an over-estimation factor ranging from 1.0 (no correction is necessary) to 2.0 as the parallel fiber segmentation becomes increasingly severe. Based on probability theory considerations, we have confirmed the existence of this over-estimation. We have further determined the cause of this over-estimation to be an artificial consequence of one of the sampling steps in our stochastic method. These results provide validation of our methodology, as well as a correction factor, which can be derived directly from the experimentally measured parameters and used to obtain the true mean length of parallel fibers. Potential applications of the stochastic method include a comparative analysis of the length of parallel fibers as an approach to gain clues about cerebellar circuit principles and function. In addition, the stochastic method may also find promising applications in other functionally important axonal systems in the brain.

On composite-structure weaknesses: Part I. Simulation, properties, and numerical approach

Composite material samples were created by means of computer simulation to duplicate short-fiber-reinforced metal-matrix composites (MMCs). Each sample contains a fairly large number of Voronoi grains and ellipsoidal short fibers, which orient and distribute in a random manner, to mimic composite microstructures for investigating the coherent interconnections of composite-structure weaknesses (CSWs) with local microstructure. It is supposed that the samples are subjected to coupled boundary traction due to mechanical loading and thermal cycling. A Kroner-Kneer structure-based model and Waldvogel-Rodin algorithm were used for numerical computations of the mesoscopic stress distribution in constituent grains. The computations are based on the grain-volume average of local fields. Polycrystal elastic/thermal properties and effective elastic/thermal properties of simulated MMC samples were predicted, respectively, in terms of micromechanics models, in favor of incorporating the influences of macroscopic material properties on the formation of CSWs. An analytically-numerically-based approach is proposed for analyzing peak mesoscopic stress and strain distributions in short fibers. Three crucial aspects constitute a kernel of the approach, i.e., (1) segmentation of short fibers, (2) establishment of the geometric relations of a short fiber to the surrounding grains, and (3) the local nature of micromechanics. The analytically-numerically-based approach takes into account the grain orientation, fiber orientation, grain geometry, fiber geometry, and macroscopic properties of simulated MMC samples. The Numerical Assessment of Computer-Imitated Weaknesses-MMCs (NACIW-MMCs) software program has been developed for performing simulation of the microstructure of short-fiber-reinforced MMCs and executing all involved numerical computations.

Computational assessment of composite structure weaknesses in short-fiber reinforced MMCs

Short-fiber composite samples, which contain a fairly large number of Voronoi grains and ellipsoidal short fibers, are generated by computer simulation to mimic real ones for investigating coherent interrelations of composite structure weaknesses (CSWs) with local microstructures. Mesoscopic stress response in a composite sample is modeled to be composed of matrix stress field disturbed by individual short fibers. An analytically numerically based approach is developed on the basis of Eshelby's equivalent inclusion method. Three crucial aspects constitute a kernel of the approach, i.e., (1) segmentation of short fibers; (2) establishment of geometric relation of short fiber with surrounding grains and (3) local nature of micromechanics. The approach allows numerical computation of mesoscopic stress and strain distributions in simulated short-fiber composite samples, which incorporates not only orientation-induced anisotropies but also geometry of composite constituents. A strong concern focuses on correlating CSWs with local microstructures. Software NACIW–MMCs is developed for performing composite simulations and executing proposed approach. The proposed analytically numerically based procedure is employed for numerical computation of mesoscopic stress distributions in short fibers. It is found that the defined `synergetic interaction factor' and `relevance parameter' are two adequate physical parameters, which account for synergetic interactions due to orientation-induced local anisotropies. By using the two defined physical quantities, CSWs can be correlated with local microstructure of simulated composite samples. Computational experiments confirm that not only stiffness mismatch between fiber and matrix materials but also orientation-induced local anisotropies exert decisive influence on distribution of CSWs in short-fiber composites. The mesoscopic stress and strain distributions within a short fiber are critically dependent on local evolution of elastic anisotropy of surrounding grains. Whether a CSW may become a failure origin must be evaluated in conjunction with local damage mechanism. Six instructions are proposed for discerning CSWs, which might inspire one to establish a methodology for designing and tailoring MMC materials.

Automatic morphometry of nerve histological sections

A method for the automatic segmentation, recognition and measurement of neuronal myelinated fibers in nerve histological sections is presented. In this method, the fiber parameters i.e. perimeter, area, position of the fiber and myelin sheath thickness are automatically computed. Obliquity of the sections may be taken into account. First, the image is thresholded to provide a coarse classification between myelin and non-myelin pixels. Next, the resulting binary image is further simplified using connected morphological operators. By applying semantic rules to the zonal graph axon candidates are identified. Those are either isolated or still connected. Then, separation of connected fibers is performed by evaluating myelin sheath thickness around each candidate area with an Euclidean distance transformation. Finally, properties of each detected fiber are computed and false positives are removed. The accuracy of the method is assessed by evaluating missed detection, false positive ratio and comparing the results to the manual procedure with sampling. In the evaluated nerve surface, a 0.9% of false positives was found, along with 6.36% of missed detections. The resulting histograms show strong correlation with those obtained by manual measure. The noise introduced by this method is significantly lower than the intrinsic sampling variability. This automatic method constitutes an original tool for morphometrical analysis.