3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries

Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.Comment: 39 pages, 14 figures. High resolution figures and supplemental movies available upon reques

Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hopkinson, B. M., King, A. C., Owen, D. P., Johnson-Roberson, M., Long, M. H., & Bhandarkar, S. M. Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks. PLoS One, 15(3), (2020): e0230671, doi: 10.1371/journal.pone.0230671.Coral reefs are biologically diverse and structurally complex ecosystems, which have been severally affected by human actions. Consequently, there is a need for rapid ecological assessment of coral reefs, but current approaches require time consuming manual analysis, either during a dive survey or on images collected during a survey. Reef structural complexity is essential for ecological function but is challenging to measure and often relegated to simple metrics such as rugosity. Recent advances in computer vision and machine learning offer the potential to alleviate some of these limitations. We developed an approach to automatically classify 3D reconstructions of reef sections and assessed the accuracy of this approach. 3D reconstructions of reef sections were generated using commercial Structure-from-Motion software with images extracted from video surveys. To generate a 3D classified map, locations on the 3D reconstruction were mapped back into the original images to extract multiple views of the location. Several approaches were tested to merge information from multiple views of a point into a single classification, all of which used convolutional neural networks to classify or extract features from the images, but differ in the strategy employed for merging information. Approaches to merging information entailed voting, probability averaging, and a learned neural-network layer. All approaches performed similarly achieving overall classification accuracies of ~96% and >90% accuracy on most classes. With this high classification accuracy, these approaches are suitable for many ecological applications.This study was funded by grants from the Alfred P. Sloan Foundation (BMH, BR2014-049; https://sloan.org), and the National Science Foundation (MHL, OCE-1657727; https://www.nsf.gov). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript

Standardizing Single-Frame Phase Singularity Identification Algorithms and Parameters in Phase Mapping During Human Atrial Fibrillation

[EN] Purpose Recent investigations failed to reproduce the positive rotor-guided ablation outcomes shown by initial studies for treating persistent atrial fibrillation (persAF). Phase singularity (PS) is an important feature for AF driver detection, but algorithms for automated PS identification differ. We aim to investigate the performance of four different techniques for automated PS detection. Methods 2048-channel virtual electrogram (VEGM) and electrocardiogram signals were collected for 30 s from 10 patients undergoing persAF ablation. QRST-subtraction was performed and VEGMs were processed using sinusoidal wavelet reconstruction. The phase was obtained using Hilbert transform. PSs were detected using four algorithms: (1) 2D image processing based and neighbor-indexing algorithm; (2) 3D neighbor-indexing algorithm; (3) 2D kernel convolutional algorithm estimating topological charge; (4) topological charge estimation on 3D mesh. PS annotations were compared using the structural similarity index (SSIM) and Pearson's correlation coefficient (CORR). Optimized parameters to improve detection accuracy were found for all four algorithms usingF(beta)score and 10-fold cross-validation compared with manual annotation. Local clustering with density-based spatial clustering of applications with noise (DBSCAN) was proposed to improve algorithms 3 and 4. Results The PS density maps created by each algorithm with default parameters were poorly correlated. Phase gradient threshold and search radius (or kernels) were shown to affect PS detections. The processing times for the algorithms were significantly different (p< 0.0001). TheF(beta)scores for algorithms 1, 2, 3, 3 + DBSCAN, 4 and 4 + DBSCAN were 0.547, 0.645, 0.742, 0.828, 0.656, and 0.831. Algorithm 4 + DBSCAN achieved the best classification performance with acceptable processing time (2.0 +/- 0.3 s). Conclusion AF driver identification is dependent on the PS detection algorithms and their parameters, which could explain some of the inconsistencies in rotor-guided ablation outcomes in different studies. For 3D triangulated meshes, algorithm 4 + DBSCAN with optimal parameters was the best solution for real-time, automated PS detection due to accuracy and speed. Similarly, algorithm 3 + DBSCAN with optimal parameters is preferred for uniform 2D meshes. Such algorithms - and parameters - should be preferred in future clinical studies for identifying AF drivers and minimizing methodological heterogeneities. This would facilitate comparisons in rotor-guided ablation outcomes in future works.This work was supported by the NIHR Leicester Biomedical Research Centre, UK. XL received research grants from Medical Research Council UK (MRC DPFS Ref: MR/S037306/1). TA received research grants from the British Heart Foundation (BHF Project Grant No. PG/18/33/33780), BHF Research Accelerator Award funding and Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP, Brazil, Grant No. 2017/00319-8). MG research was funded by a research grant from the Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain: PI13-00903). GN received funding from the British Heart Foundation (BHF Programme Grant, RG/17/3/32774).Li, X.; Almeida, TP.; Dastagir, N.; Guillem Sánchez, MS.; Salinet, J.; Chu, GS.; Stafford, PJ.... (2020). Standardizing Single-Frame Phase Singularity Identification Algorithms and Parameters in Phase Mapping During Human Atrial Fibrillation. Frontiers in Physiology. 11:1-16. https://doi.org/10.3389/fphys.2020.00869S11611ALHUSSEINI, M., VIDMAR, D., MECKLER, G. L., KOWALEWSKI, C. A., SHENASA, F., WANG, P. J., … RAPPEL, W.-J. (2017). Two Independent Mapping Techniques Identify Rotational Activity Patterns at Sites of Local Termination During Persistent Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 28(6), 615-622. doi:10.1111/jce.13177Allessie, M. A., de Groot, N. M. S., Houben, R. P. M., Schotten, U., Boersma, E., Smeets, J. L., & Crijns, H. J. (2010). Electropathological Substrate of Long-Standing Persistent Atrial Fibrillation in Patients With Structural Heart Disease. Circulation: Arrhythmia and Electrophysiology, 3(6), 606-615. doi:10.1161/circep.109.910125Benharash, P., Buch, E., Frank, P., Share, M., Tung, R., Shivkumar, K., & Mandapati, R. (2015). Quantitative Analysis of Localized Sources Identified by Focal Impulse and Rotor Modulation Mapping in Atrial Fibrillation. Circulation: Arrhythmia and Electrophysiology, 8(3), 554-561. doi:10.1161/circep.115.002721BRAY, M.-A., LIN, S.-F., ALIEV, R. R., ROTH, B. J., & WIKSWO, J. P. (2001). Experimental and Theoretical Analysis of Phase Singularity Dynamics in Cardiac Tissue. Journal of Cardiovascular Electrophysiology, 12(6), 716-722. doi:10.1046/j.1540-8167.2001.00716.xBray, M.-A., & Wikswo, J. P. (2002). Use of topological charge to determine filament location and dynamics in a numerical model of scroll wave activity. IEEE Transactions on Biomedical Engineering, 49(10), 1086-1093. doi:10.1109/tbme.2002.803516Buch, E., Share, M., Tung, R., Benharash, P., Sharma, P., Koneru, J., … Shivkumar, K. (2016). Long-term clinical outcomes of focal impulse and rotor modulation for treatment of atrial fibrillation: A multicenter experience. Heart Rhythm, 13(3), 636-641. doi:10.1016/j.hrthm.2015.10.031Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679-698. doi:10.1109/tpami.1986.4767851Clayton, R. H., & Nash, M. P. (2015). Analysis of Cardiac Fibrillation Using Phase Mapping. Cardiac Electrophysiology Clinics, 7(1), 49-58. doi:10.1016/j.ccep.2014.11.011Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning - ICML ’06. doi:10.1145/1143844.1143874De Groot, N. M. S., Houben, R. P. M., Smeets, J. L., Boersma, E., Schotten, U., Schalij, M. J., … Allessie, M. A. (2010). Electropathological Substrate of Longstanding Persistent Atrial Fibrillation in Patients With Structural Heart Disease. Circulation, 122(17), 1674-1682. doi:10.1161/circulationaha.109.910901Earley, M. J., Abrams, D. J. R., Sporton, S. C., & Schilling, R. J. (2006). Validation of the Noncontact Mapping System in the Left Atrium During Permanent Atrial Fibrillation and Sinus Rhythm. Journal of the American College of Cardiology, 48(3), 485-491. doi:10.1016/j.jacc.2006.04.069Gianni, C., Mohanty, S., Di Biase, L., Metz, T., Trivedi, C., Gökoğlan, Y., … Natale, A. (2016). Acute and early outcomes of focal impulse and rotor modulation (FIRM)-guided rotors-only ablation in patients with nonparoxysmal atrial fibrillation. Heart Rhythm, 13(4), 830-835. doi:10.1016/j.hrthm.2015.12.028GOJRATY, S., LAVI, N., VALLES, E., KIM, S. J., MICHELE, J., & GERSTENFELD, E. P. (2009). Dominant Frequency Mapping of Atrial Fibrillation: Comparison of Contact and Noncontact Approaches. Journal of Cardiovascular Electrophysiology, 20(9), 997-1004. doi:10.1111/j.1540-8167.2009.01488.xGrandi, E., Pandit, S. V., Voigt, N., Workman, A. J., Dobrev, D., Jalife, J., & Bers, D. M. (2011). Human Atrial Action Potential and Ca 2+ Model. Circulation Research, 109(9), 1055-1066. doi:10.1161/circresaha.111.253955Gray, R. A., Pertsov, A. M., & Jalife, J. (1998). Spatial and temporal organization during cardiac fibrillation. Nature, 392(6671), 75-78. doi:10.1038/32164Guillem, M. S., Climent, A. M., Millet, J., Arenal, Á., Fernández-Avilés, F., Jalife, J., … Berenfeld, O. (2013). Noninvasive Localization of Maximal Frequency Sites of Atrial Fibrillation by Body Surface Potential Mapping. Circulation: Arrhythmia and Electrophysiology, 6(2), 294-301. doi:10.1161/circep.112.000167Guillem, M. S., Climent, A. M., Rodrigo, M., Fernández-Avilés, F., Atienza, F., & Berenfeld, O. (2016). Presence and stability of rotors in atrial fibrillation: evidence and therapeutic implications. Cardiovascular Research, 109(4), 480-492. doi:10.1093/cvr/cvw011Gurevich, D. R., & Grigoriev, R. O. (2019). Robust approach for rotor mapping in cardiac tissue. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(5), 053101. doi:10.1063/1.5086936HAISSAGUERRE, M., HOCINI, M., SHAH, A. J., DERVAL, N., SACHER, F., JAIS, P., & DUBOIS, R. (2013). Noninvasive Panoramic Mapping of Human Atrial Fibrillation Mechanisms: A Feasibility Report. Journal of Cardiovascular Electrophysiology, 24(6), 711-717. doi:10.1111/jce.12075Iyer, A. N., & Gray, R. A. (2001). An Experimentalist’s Approach to Accurate Localization of Phase Singularities during Reentry. Annals of Biomedical Engineering, 29(1), 47-59. doi:10.1114/1.1335538Jalife, J. (2002). Mother rotors and fibrillatory conduction: a mechanism of atrial fibrillation. Cardiovascular Research, 54(2), 204-216. doi:10.1016/s0008-6363(02)00223-7Jalife, J., Filgueiras Rama, D., & Berenfeld, O. (2015). Letter by Jalife et al Regarding Article, «Quantitative Analysis of Localized Sources Identified by Focal Impulse and Rotor Modulation Mapping in Atrial Fibrillation». Circulation: Arrhythmia and Electrophysiology, 8(5), 1296-1298. doi:10.1161/circep.115.003324Jarman, J. W. E., Wong, T., Kojodjojo, P., Spohr, H., Davies, J. E., Roughton, M., … Peters, N. S. (2012). Spatiotemporal Behavior of High Dominant Frequency During Paroxysmal and Persistent Atrial Fibrillation in the Human Left Atrium. Circulation: Arrhythmia and Electrophysiology, 5(4), 650-658. doi:10.1161/circep.111.967992Kuklik, P., Zeemering, S., Maesen, B., Maessen, J., Crijns, H. J., Verheule, S., … Schotten, U. (2015). Reconstruction of Instantaneous Phase of Unipolar Atrial Contact Electrogram Using a Concept of Sinusoidal Recomposition and Hilbert Transform. IEEE Transactions on Biomedical Engineering, 62(1), 296-302. doi:10.1109/tbme.2014.2350029Identification of Rotors during Human Atrial Fibrillation Using Contact Mapping and Phase Singularity Detection: Technical Considerations. (2017). IEEE Transactions on Biomedical Engineering, 64(2), 310-318. doi:10.1109/tbme.2016.2554660Lee, Y.-S., Song, J.-S., Hwang, M., Lim, B., Joung, B., & Pak, H.-N. (2016). A New Efficient Method for Detecting Phase Singularity in Cardiac Fibrillation. PLOS ONE, 11(12), e0167567. doi:10.1371/journal.pone.0167567Li, X., Chu, G. S., Almeida, T. P., Salinet, J. L., Dastagir, N., Mistry, A. R., … André Ng, G. (2017). 5Characteristics of ablated rotors in terminating persistent atrial fibrillation using non-contact mapping. EP Europace, 19(suppl_1), i3-i3. doi:10.1093/europace/eux283.145Li, X., Salinet, J. L., Almeida, T. P., Vanheusden, F. J., Chu, G. S., Ng, G. A., & Schlindwein, F. S. (2017). An interactive platform to guide catheter ablation in human persistent atrial fibrillation using dominant frequency, organization and phase mapping. Computer Methods and Programs in Biomedicine, 141, 83-92. doi:10.1016/j.cmpb.2017.01.011Mandapati, R., Skanes, A., Chen, J., Berenfeld, O., & Jalife, J. (2000). Stable Microreentrant Sources as a Mechanism of Atrial Fibrillation in the Isolated Sheep Heart. Circulation, 101(2), 194-199. doi:10.1161/01.cir.101.2.194Narayan, S. M., Baykaner, T., Clopton, P., Schricker, A., Lalani, G. G., Krummen, D. E., … Miller, J. M. (2014). Ablation of Rotor and Focal Sources Reduces Late Recurrence of Atrial Fibrillation Compared With Trigger Ablation Alone. Journal of the American College of Cardiology, 63(17), 1761-1768. doi:10.1016/j.jacc.2014.02.543NARAYAN, S. M., KRUMMEN, D. E., & RAPPEL, W.-J. (2012). Clinical Mapping Approach To Diagnose Electrical Rotors and Focal Impulse Sources for Human Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 23(5), 447-454. doi:10.1111/j.1540-8167.2012.02332.xNarayan, S. M., Krummen, D. E., Shivkumar, K., Clopton, P., Rappel, W.-J., & Miller, J. M. (2012). Treatment of Atrial Fibrillation by the Ablation of Localized Sources. Journal of the American College of Cardiology, 60(7), 628-636. doi:10.1016/j.jacc.2012.05.022Nattel, S. (2002). New ideas about atrial fibrillation 50 years on. Nature, 415(6868), 219-226. doi:10.1038/415219aNattel, S. (2003). Atrial Electrophysiology and Mechanisms of Atrial Fibrillation. Journal of Cardiovascular Pharmacology and Therapeutics, 8(1_suppl), S5-S11. doi:10.1177/107424840300800102Ortigosa, N., Fernández, C., Galbis, A., & Cano, Ó. (2015). Phase information of time-frequency transforms as a key feature for classification of atrial fibrillation episodes. Physiological Measurement, 36(3), 409-424. doi:10.1088/0967-3334/36/3/409Pandit, S. V., & Jalife, J. (2013). Rotors and the Dynamics of Cardiac Fibrillation. Circulation Research, 112(5), 849-862. doi:10.1161/circresaha.111.300158VII. Mathematical contributions to the theory of evolution.—III. Regression, heredity, and panmixia. (1896). Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 187, 253-318. doi:10.1098/rsta.1896.0007Pertsov, A. M., Davidenko, J. M., Salomonsz, R., Baxter, W. T., & Jalife, J. (1993). Spiral waves of excitation underlie reentrant activity in isolated cardiac muscle. Circulation Research, 72(3), 631-650. doi:10.1161/01.res.72.3.631Podziemski, P., Zeemering, S., Kuklik, P., van Hunnik, A., Maesen, B., Maessen, J., … Schotten, U. (2018). Rotors Detected by Phase Analysis of Filtered, Epicardial Atrial Fibrillation Electrograms Colocalize With Regions of Conduction Block. Circulation: Arrhythmia and Electrophysiology, 11(10). doi:10.1161/circep.117.005858Wieser, L., Stühlinger, M. C., Hintringer, F., Tilg, B., Fischer, G., & Rantner, L. J. (2007). Detection of Phase Singularities in Triangular Meshes. Methods of Information in Medicine, 46(06), 646-654. doi:10.3414/me0427Ríos-Muñoz, G. R., Arenal, Á., & Artés-Rodríguez, A. (2018). Real-Time Rotational Activity Detection in Atrial Fibrillation. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.00208Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms. Circulation: Arrhythmia and Electrophysiology, 10(9). doi:10.1161/circep.117.005008Rodrigo, M., Guillem, M. S., Climent, A. M., Pedrón-Torrecilla, J., Liberos, A., Millet, J., … Berenfeld, O. (2014). Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study. Heart Rhythm, 11(9), 1584-1591. doi:10.1016/j.hrthm.2014.05.013Roney, C. H., Cantwell, C. D., Bayer, J. D., Qureshi, N. A., Lim, P. B., Tweedy, J. H., … Ng, F. S. (2017). Spatial Resolution Requirements for Accurate Identification of Drivers of Atrial Fibrillation. Circulation: Arrhythmia and Electrophysiology, 10(5). doi:10.1161/circep.116.004899Salinet, J., Schlindwein, F. S., Stafford, P., Almeida, T. P., Li, X., Vanheusden, F. J., … Ng, G. A. (2017). Propagation of meandering rotors surrounded by areas of high dominant frequency in persistent atrial fibrillation. Heart Rhythm, 14(9), 1269-1278. doi:10.1016/j.hrthm.2017.04.031Salinet, J. L., Madeiro, J. P. V., Cortez, P. C., Stafford, P. J., André Ng, G., & Schlindwein, F. S. (2013). Analysis of QRS-T subtraction in unipolar atrial fibrillation electrograms. Medical & Biological Engineering & Computing, 51(12), 1381-1391. doi:10.1007/s11517-013-1071-4Salinet, J. L., Oliveira, G. N., Vanheusden, F. J., Comba, J. L. D., Ng, G. A., & Schlindwein, F. S. (2013). Visualizing intracardiac atrial fibrillation electrograms using spectral analysis. Computing in Science & Engineering, 15(2), 79-87. doi:10.1109/mcse.2013.37Schilling, R. J., Peters, N. S., & Davies, D. W. (1998). Simultaneous Endocardial Mapping in the Human Left Ventricle Using a Noncontact Catheter. Circulation, 98(9), 887-898. doi:10.1161/01.cir.98.9.887Schricker, A. A., Lalani, G. G., Krummen, D. E., & Narayan, S. M. (2014). Rotors as Drivers of Atrial Fibrillation and Targets for Ablation. Current Cardiology Reports, 16(8). doi:10.1007/s11886-014-0509-0Steinberg, J. S., Shah, Y., Bhatt, A., Sichrovsky, T., Arshad, A., Hansinger, E., & Musat, D. (2017). Focal impulse and rotor modulation: Acute procedural observations and extended clinical follow-up. Heart Rhythm, 14(2), 192-197. doi:10.1016/j.hrthm.2016.11.008THIAGALINGAM, A., WALLACE, E. M., BOYD, A. C., EIPPER, V. E., CAMPBELL, C. R., BYTH, K., … KOVOOR, P. (2004). Noncontact Mapping of the Left Ventricle:. Insights from Validation with Transmural Contact Mapping. Pacing and Clinical Electrophysiology, 27(5), 570-578. doi:10.1111/j.1540-8159.2004.00489.xUmapathy, K., Nair, K., Masse, S., Krishnan, S., Rogers, J., Nash, M. P., & Nanthakumar, K. (2010). Phase Mapping of Cardiac Fibrillation. Circulation: Arrhythmia and Electrophysiology, 3(1), 105-114. doi:10.1161/circep.110.853804WITTKAMPF, F. H. M., & NAKAGAWA, H. (2006). RF Catheter Ablation: Lessons on Lesions. Pacing and Clinical Electrophysiology, 29(11), 1285-1297. doi:10.1111/j.1540-8159.2006.00533.xWang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/tip.2003.81986

Algorithms and methods for discrete mesh repair

Computational analysis and design has become a fundamental part of product research, development, and manufacture in aerospace, automotive, and other industries. In general the success of the specific application depends heavily on the accuracy and consistency of the computational model used. The aim of this work is to reduce the time needed to prepare geometry for mesh generation. This will be accomplished by developing tools that semi-automatically repair discrete data. Providing a level of automation to the process of repairing large, complex problems in discrete data will significantly accelerate the grid generation process. The developed algorithms are meant to offer semi-automated solutions to complicated geometrical problems—specifically discrete mesh intersections and isolated boundaries. The intersection-repair strategy presented here focuses on repairing the intersection in-place as opposed to re-discretizing the intersecting geometries. Combining robust, efficient methods of detecting intersections and then repairing intersecting geometries in-place produces a significant improvement over techniques used in current literature. The result of this intersection process is a non-manifold, non-intersecting geometry that is free of duplicate and degenerate geometry. Results are presented showing the accuracy and consistency of the intersection repair tool. Isolated boundaries are a type of gap that current research does not address directly. They are defined by discrete boundary edges that are unable to be paired with nearby discrete boundary edges in order to fill the existing gap. In this research the method of repair seeks to fill the gap by extruding the isolated boundary along a defined vector so that it is topologically adjacent to a nearby surface. The outcome of the repair process is that the isolated boundaries no longer exist because the gap has been filled. Results are presented showing the precision of the edge projection and the advantage of edge splitting in the repair of isolated boundaries

Matching 3D face scans using interest points and local histogram descriptors

n/

CAD-Based Porous Scaffold Design of Intervertebral Discs in Tissue Engineering

With the development and maturity of three-dimensional (3D) printing technology over the past decade, 3D printing has been widely investigated and applied in the field of tissue engineering to repair damaged tissues or organs, such as muscles, skin, and bones, Although a number of automated fabrication methods have been developed to create superior bio-scaffolds with specific surface properties and porosity, the major challenges still focus on how to fabricate 3D natural biodegradable scaffolds that have tailor properties such as intricate architecture, porosity, and interconnectivity in order to provide the needed structural integrity, strength, transport, and ideal microenvironment for cell- and tissue-growth. In this dissertation, a robust pipeline of fabricating bio-functional porous scaffolds of intervertebral discs based on different innovative porous design methodologies is illustrated. Firstly, a triply periodic minimal surface (TPMS) based parameterization method, which has overcome the integrity problem of traditional TPMS method, is presented in Chapter 3. Then, an implicit surface modeling (ISM) approach using tetrahedral implicit surface (TIS) is demonstrated and compared with the TPMS method in Chapter 4. In Chapter 5, we present an advanced porous design method with higher flexibility using anisotropic radial basis function (ARBF) and volumetric meshes. Based on all these advanced porous design methods, the 3D model of a bio-functional porous intervertebral disc scaffold can be easily designed and its physical model can also be manufactured through 3D printing. However, due to the unique shape of each intervertebral disc and the intricate topological relationship between the intervertebral discs and the spine, the accurate localization and segmentation of dysfunctional discs are regarded as another obstacle to fabricating porous 3D disc models. To that end, we discuss in Chapter 6 a segmentation technique of intervertebral discs from CT-scanned medical images by using deep convolutional neural networks. Additionally, some examples of applying different porous designs on the segmented intervertebral disc models are demonstrated in Chapter 6

Review on Classification Methods used in Image based Sign Language Recognition System

Sign language is the way of communication among the Deaf-Dumb people by expressing signs. This paper is present review on Sign language Recognition system that aims to provide communication way for Deaf and Dumb pople. This paper describes review of Image based sign language recognition system. Signs are in the form of hand gestures and these gestures are identified from images as well as videos. Gestures are identified and classified according to features of Gesture image. Features are like shape, rotation, angle, pixels, hand movement etc. Features are finding by various Features Extraction methods and classified by various machine learning methods. Main pupose of this paper is to review on classification methods of similar systems used in Image based hand gesture recognition . This paper also describe comarison of various system on the base of classification methods and accuracy rate

Feature-sensitive and Adaptive Image Triangulation: A Super-pixel-based Scheme for Image Segmentation and Mesh Generation

With increasing utilization of various imaging techniques (such as CT, MRI and PET) in medical fields, it is often in great need to computationally extract the boundaries of objects of interest, a process commonly known as image segmentation. While numerous approaches have been proposed in literature on automatic/semi-automatic image segmentation, most of these approaches are based on image pixels. The number of pixels in an image can be huge, especially for 3D imaging volumes, which renders the pixel-based image segmentation process inevitably slow. On the other hand, 3D mesh generation from imaging data has become important not only for visualization and quantification but more critically for finite element based numerical simulation. Traditionally image-based mesh generation follows such a procedure as: (1) image boundary segmentation, (2) surface mesh generation from segmented boundaries, and (3) volumetric (e.g., tetrahedral) mesh generation from surface meshes. These three majors steps have been commonly treated as separate algorithms/steps and hence image information, once segmented, is not considered any more in mesh generation. In this thesis, we investigate a super-pixel based scheme that integrates both image segmentation and mesh generation into a single method, making mesh generation truly an image-incorporated approach. Our method, called image content-aware mesh generation, consists of several main steps. First, we generate a set of feature-sensitive, and adaptively distributed points from 2D grayscale images or 3D volumes. A novel image edge enhancement method via randomized shortest paths is introduced to be an optional choice to generate the features’ boundary map in mesh node generation step. Second, a Delaunay-triangulation generator (2D) or tetrahedral mesh generator (3D) is then utilized to generate a 2D triangulation or 3D tetrahedral mesh. The generated triangulation (or tetrahedralization) provides an adaptive partitioning of a given image (or volume). Each cluster of pixels within a triangle (or voxels within a tetrahedron) is called a super-pixel, which forms one of the nodes of a graph and adjacent super-pixels give an edge of the graph. A graph-cut method is then applied to the graph to define the boundary between two subsets of the graph, resulting in good boundary segmentations with high quality meshes. Thanks to the significantly reduced number of elements (super-pixels) as compared to that of pixels in an image, the super-pixel based segmentation method has tremendously improved the segmentation speed, making it feasible for real-time feature detection. In addition, the incorporation of image segmentation into mesh generation makes the generated mesh well adapted to image features, a desired property known as feature-preserving mesh generation