Academic literature on the topic 'Segmentation des pores'
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Journal articles on the topic "Segmentation des pores"
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Sintorn, Ida-Maria, Stina Svensson, Maria Axelsson, and Gunilla Borgefors. "Segmentation of individual pores in 3D paper images." Nordic Pulp & Paper Research Journal 20, no. 3 (August 1, 2005): 316–19. http://dx.doi.org/10.3183/npprj-2005-20-03-p316-319.
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Bauer, Benjamin, Xiaohao Cai, Stephan Peth, Katja Schladitz, and Gabriele Steidl. "Variational-based segmentation of bio-pores in tomographic images." Computers & Geosciences 98 (January 2017): 1–8. http://dx.doi.org/10.1016/j.cageo.2016.09.013.
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Liu, Lei, Qiaoling Han, Yue Zhao, and Yandong Zhao. "A Novel Method Combining U-Net with LSTM for Three-Dimensional Soil Pore Segmentation Based on Computed Tomography Images." Applied Sciences 14, no. 8 (April 16, 2024): 3352. http://dx.doi.org/10.3390/app14083352.
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The non-destructive study of soil micromorphology via computed tomography (CT) imaging has yielded significant insights into the three-dimensional configuration of soil pores. Precise pore analysis is contingent on the accurate transformation of CT images into binary image representations. Notably, segmentation of 2D CT images frequently harbors inaccuracies. This paper introduces a novel three-dimensional pore segmentation method, BDULSTM, which integrates U-Net with convolutional long short-term memory (CLSTM) networks to harness sequence data from CT images and enhance the precision of pore segmentation. The BDULSTM method employs an encoder–decoder framework to holistically extract image features, utilizing skip connections to further refine the segmentation accuracy of soil structure. Specifically, the CLSTM component, critical for analyzing sequential information in soil CT images, is strategically positioned at the juncture of the encoder and decoder within the U-shaped network architecture. The validation of our method confirms its efficacy in advancing the accuracy of soil pore segmentation beyond that of previous deep learning techniques, such as U-Net and CLSTM independently. Indeed, BDULSTM exhibits superior segmentation capabilities across a diverse array of soil conditions. In summary, BDULSTM represents a state-of-the-art artificial intelligence technology for the 3D segmentation of soil pores and offers a promising tool for analyzing pore structure and soil quality.
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Yosifov, Miroslav, Patrick Weinberger, Bernhard Plank, Bernhard Fröhler, Markus Hoeglinger, Johann Kastner, and Christoph Heinzl. "Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network." Acta Polytechnica CTU Proceedings 42 (October 12, 2023): 87–93. http://dx.doi.org/10.14311/app.2023.42.0087.
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This study demonstrates the utilization of deep learning techniques for binary semantic segmentation of pores in carbon fiber reinforced polymers (CFRP) using X-ray computed tomography (XCT) datasets. The proposed workflow is designed to generate efficient segmentation models with reasonable execution time, applicable even for users using consumer-grade GPU systems. First, U-Net, a convolutional neural network, is modified to handle the segmentation of XCT datasets. In the second step, suitable hyperparameters are determined through a parameter analysis (hyperparameter tuning), and the parameter set with the best result was used for the final training. In the final step, we report on our efforts of implementing the testing stage in open_iA, which allows users to segment datasets with the fully trained model within reasonable time. The model performs well on datasets with both high and low resolution, and even works reasonably for barely visible pores with different shapes and size. In our experiments, we could show that U-Net is suitable for pore segmentation. Despite being trained on a limited number of datasets, it exhibits a satisfactory level of prediction accuracy.
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Tomažinčič, Dejan, Žiga Virk, Peter Marijan Kink, Gregor Jerše, and Jernej Klemenc. "Predicting the Fatigue Life of an AlSi9Cu3 Porous Alloy Using a Vector-Segmentation Technique for a Geometric Parameterisation of the Macro Pores." Metals 11, no. 1 (December 31, 2020): 72. http://dx.doi.org/10.3390/met11010072.
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Most of the published research work related to the fatigue life of porous, high-pressure, die-cast structures is limited to a consideration of individual isolated pores. The focus of this article is on calculating the fatigue life of high-pressure, die-cast, AlSi9Cu3 parts with many clustered macro pores. The core of the presented methodology is a geometric parameterisation of the pores using a vector-segmentation technique. The input for the vector segmentation is a μ-CT scan of the porous material. After the pores are localised, they are parameterised as 3D ellipsoids with the corresponding orientations in the Euclidian space. The extracted ellipsoids together with the outer contour are then used to build a finite-element mesh of the porous structure. The stress–strain distribution is calculated using Abaqus and the fatigue life is predicted using SIMULIA fe-safe. The numerical results are compared to the experimentally determined fatigue lives to prove the applicability of the proposed approach. The outcome of this research is a usable tool for estimating the limiting quantity of a structure’s porosity that still allows for the functional performance and required durability of a product.
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Tong, Tong, Yan Cai, Da Wei Sun, and Peng Liu. "Automatic Segmentation of Pores in Weld Images Based on Transition Region Extraction." Applied Mechanics and Materials 217-219 (November 2012): 1964–67. http://dx.doi.org/10.4028/www.scientific.net/amm.217-219.1964.
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In allusion to the complex images of weld defects, weak contrast between the target and the background, a new segmentation method based on gray level difference transition region extraction is proposed. The paper analyzes the characteristic of weld defects, and then low-pass filtering and contrast enhanced are used to enhance the clarity. Finally, we extract the transition region and confirm a threshold for defects segmentation. The experimental results show that the method can extract the transition region more accurate, and segment the image much better in complex environment.
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Yoon, Huisu, Semin Kim, Jongha Lee, and Sangwook Yoo. "Deep-Learning-Based Morphological Feature Segmentation for Facial Skin Image Analysis." Diagnostics 13, no. 11 (May 29, 2023): 1894. http://dx.doi.org/10.3390/diagnostics13111894.
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Facial skin analysis has attracted considerable attention in the skin health domain. The results of facial skin analysis can be used to provide skin care and cosmetic recommendations in aesthetic dermatology. Because of the existence of several skin features, grouping similar features and processing them together can improve skin analysis. In this study, a deep-learning-based method of simultaneous segmentation of wrinkles and pores is proposed. Unlike color-based skin analysis, this method is based on the analysis of the morphological structures of the skin. Although multiclass segmentation is widely used in computer vision, this segmentation was first used in facial skin analysis. The architecture of the model is U-Net, which has an encoder–decoder structure. We added two types of attention schemes to the network to focus on important areas. Attention in deep learning refers to the process by which a neural network focuses on specific parts of its input to improve its performance. Second, a method to enhance the learning capability of positional information is added to the network based on the fact that the locations of wrinkles and pores are fixed. Finally, a novel ground truth generation scheme suitable for the resolution of each skin feature (wrinkle and pore) was proposed. The experimental results revealed that the proposed unified method achieved excellent localization of wrinkles and pores and outperformed both conventional image-processing-based approaches and one of the recent successful deep-learning-based approaches. The proposed method should be expanded to applications such as age estimation and the prediction of potential diseases.
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Song, Wenlong, Junyu Li, Kexin Li, Jingxu Chen, and Jianping Huang. "An Automatic Method for Stomatal Pore Detection and Measurement in Microscope Images of Plant Leaf Based on a Convolutional Neural Network Model." Forests 11, no. 9 (September 1, 2020): 954. http://dx.doi.org/10.3390/f11090954.
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Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The proposed method uses a type of convolutional neural network model (Mask R-CNN (region-based convolutional neural network)) to obtain the contour coordinates of the pore regions in microscope images of leaves. The anatomy parameters of pores are then obtained by ellipse fitting technology, and the quantitative analysis of pore parameters is implemented. Stomatal microscope image datasets for black poplar leaves were obtained using a large depth-of-field microscope observation system, the VHX-2000, from Keyence Corporation. The images used in the training, validation, and test sets were taken randomly from the datasets (562, 188, and 188 images, respectively). After 10-fold cross validation, the 188 test images were found to contain an average of 2278 pores (pore widths smaller than 0.34 μm (1.65 pixels) were considered to be closed stomata), and an average of 2201 pores were detected by our network with a detection accuracy of 96.6%, and the intersection of union (IoU) of the pores was 0.82. The segmentation results of 2201 stomatal pores of black poplar leaves showed that the average measurement accuracies of the (a) pore length, (b) pore width, (c) area, (d) eccentricity, and (e) degree of stomatal opening, with a ratio of width-to-maximum length of a stomatal pore, were (a) 94.66%, (b) 93.54%, (c) 90.73%, (d) 99.09%, and (e) 92.95%, respectively. The proposed stomatal pore detection and measurement method based on the Mask R-CNN can automatically measure the anatomy parameters of pores in plants, thus helping researchers to obtain accurate stomatal pore information for leaves in an efficient and simple way.
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Soboleva, N. N., and A. N. Mushnikov. "Determination of the volume fraction of primary carbides in the microstructure of composite coatings using semantic segmentation." Frontier materials & technologies, no. 3 (2023): 95–102. http://dx.doi.org/10.18323/2782-4039-2023-3-65-9.
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In the process of formation of composite coatings, partial dissolution of hardening particles (most often carbides) in the matrix is possible; therefore, in some cases, the material creation mode is chosen taking into account the volume fraction of primary carbides not dissolved during coating deposition. The methods currently widely used for calculating the volume fraction of carbides in the structure of composite coatings (manual point method and programs implementing classical computer vision methods) have limitations in terms of the possibility of automation. It is expected that performing semantic segmentation using convolutional neural networks will improve both the performance of the process and the accuracy of carbide detection. In the work, multiclass semantic segmentation was carried out including the classification on the image of pores and areas that are not a microstructure. The authors used two neural networks based on DeepLab-v3 trained with different loss functions (IoU Loss and Dice Loss). The initial data were images of various sizes from electron and optical microscopes, with spherical and angular carbides darker and lighter than the matrix, in some cases with pores and areas not related to the microstructure. The paper presents mask images consisting of four classes, created manually and by two trained neural networks. The study shows that the networks recognize pores, areas not related to the microstructure, and perfectly segment spherical carbides in images, regardless of their color relative to the matrix and the presence of pores in the structure. The authors compared the proportion of carbides in the microstructure of coatings determined by two neural networks and a manual point method.
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Wen, Hao, Chang Huang, and Shengmin Guo. "The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts." Materials 14, no. 10 (May 15, 2021): 2575. http://dx.doi.org/10.3390/ma14102575.
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Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.
Dissertations / Theses on the topic "Segmentation des pores"
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DELERUE, JEAN FRANCOIS. "Segmentation 3d, application a l'extraction de reseaux de pores et a la caracterisation hydrodynamique des sols." Paris 11, 2001. http://www.theses.fr/2001PA112141.
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Le sol et les materiaux poreux en general, peuvent etre vus comme l'union de deux parties : la partie solide, constituee de differents materiaux (argile, roche etc. ) et la partie vide (espace poral) par ou peuvent s'ecouler des fluides. Une connaissance precise de la structure 3d de la partie vide devrait permettre une meilleure comprehension des phenomenes d'ecoulement, voire meme une prevision des proprietes hydriques de ces materiaux. Les recents progres dans les domaines de l'acquisition d'image rendent de plus en plus abordable l'obtention d'images volumiques de sol, notamment grace a la tomographie a rayon x. Mon travail a consiste a adapter des algorithmes d'analyse d'image existants, et a en developper de nouveaux afin de decrire les structures des parties vides dans des images volumiques de sol. Pour mener a bien cette description, je propose differents algorithmes originaux : un algorithme de calcul de diagramme de voronoi sur espace discret, un algorithme de squelettisation par selection des points de frontieres de voronoi et un algorithme de segmentation par croissance de region utilisant des distances de type geodesique. Ces differents algorithmes forment une suite qui, appliquee a un objet quelconque, permet de le decomposer suivant des criteres de taille locale. Dans le cas d'une image de sol, la partie vide du sol est segmentee en regions correspondant a des pores (parties elementaires de l'espace poral d'ouverture homogene) et un reseau de pores est cree. A partir de ce reseau, il est possible par analogie avec les reseaux electriques de calculer la conductivite hydrique equivalente pour l'image etudiee. De facon generale, je propose un ensemble de procedures permettant entre autre, de simuler des processus d'intrusion et d'extrusion de fluide dans l'espace poral, de simuler la porosimetrie au mercure et de calculer des distributions d'ouvertures. Bien que concu pour l'etude des sols, ce travail d'imagerie 3d pourrait etre applique a d'autres domaines.
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Ding, Nan. "3D Modeling of the Lamina Cribrosa in OCT Data." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS148.
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La lame criblée (LC), située dans la tête du nerf optique, joue un rôle crucial dans le diagnostic et l'étude du glaucome, la deuxième cause de cécité. Il s'agit d'un maillage collagénique 3D formé de pores, par lesquels les fibres nerveuses passent pour atteindre le cerveau. L'observation 3D in vivo des pores de la LC est désormais possible grâce aux progrès de l'imagerie de tomographie de cohérence optique (OCT). Dans cette étude, nous visons à réaliser automatiquement la reconstruction 3D des pores à partir de volumes OCT, afin d'étudier le remodelage de la LC au cours du glaucome. La résolution limitée de l'OCT conventionnel ainsi que le faible rapport signal à bruit (SNR) posent des problèmes pour caractériser les chemins axonaux avec suffisamment de fiabilité et de précision, sachant qu'il est difficile, même pour des experts, d'identifier les pores dans une seule image en-face. Ainsi, notre première contribution est une méthode innovante de recalage et de fusion de deux volumes OCT 3D orthogonaux pour l'amélioration de la qualité d'image et le rehaussement des pores, ce qui, à notre connaissance, n'avait jamais été réalisé. Les résultats expérimentaux démontrent que notre algorithme est robuste et conduit à un alignement précis. Notre deuxième contribution est la conception d'un réseau de neurones profond, de type attention U-net, pour segmenter les pores de la LC dans les images 2D en-face. Il s'agit de la première tentative de résolution de ce problème par apprentissage profond, les défis posés relevant de l'incomplétude des annotations pour l'apprentissage, et du faible contraste et de la mauvaise résolution des pores. L'analyse comparative avec d'autres méthodes montre que notre approche conduit aux meilleurs résultats. La fusion des volumes OCT et la segmentation des pores dans les images en-face constituent les deux étapes préliminaires à la reconstruction 3D des trajets axonaux, notre troisième contribution. Nous proposons une méthode de suivi des pores fondée sur un algorithme de contour actif paramétrique appliqué localement. Notre modèle intègre les caractéristiques de faible intensité et de régularité des pores. Combiné aux cartes de segmentation 2D, il nous permet de reconstituer plan par plan les chemins axonaux en 3D. Ces résultats ouvrent la voie au calcul de biomarqueurs et facilitent l'interprétation médicaleThe lamina cribrosa (LC) is a 3D collagenous mesh in theoptic nerve head that plays a crucial role in themechanisms and diagnosis of glaucoma, the second leading cause of blindness in the world. The LC is composed of so-called “pores”, namely axonal paths within the collagenous mesh, through which the axons pass to reach the brain. In vivo 3D observation of the LC pores is now possible thanks to advances in Optical Coherence Tomography (OCT) technology. In this study, we aim to automatically perform the 3D reconstruction of pore paths from OCT volumes, in order to study the remodeling of the lamina cribrosa during glaucoma and better understand this disease.The limited axial resolution of conventional OCT as well as the low signal to noise ratio (SNR) poses challenges for the robust characterization of axonal paths with enough reliability, knowing that it is difficult even for experts to identify the pores in a single en-face image. To this end, our first contribution introduces an innovative method to register and fuse 2 orthogonal 3D OCT volumes in order to enhance the pores. This is, to our knowledge, the first time that orthogonal OCT volumes are jointly exploited to achieve better image quality. Experimental results demonstrate that our algorithm is robust and leads to accurate alignment.Our second contribution presents a context-aware attention U-Net method, a deep learning approach using partial points annotation for the accurate pore segmentation in every 2D en-face image. This work is also, to the best of our knowledge, the first attempt to look into the LC pore reconstruction problem using deep learning methods. Through a comparative analysis with other state-of-the-art methods, we demonstrate the superior performance of the proposed approach.Our robust and accurate pore registration and segmentation methods provide a solid foundation for 3D reconstruction of axonal pathways, our third contribution. We propose a pore tracking method based on a locally applied parametric active contour algorithm. Our model integrates the characteristics of low intensity and regularity of pores. Combined with the 2D segmentation maps, it enables us to reconstruct the axonal paths in 3D plane by plane. These results pave the way for the calculation of biomarkers characterizing the LC and facilitate medical interpretation
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Sekkal, Rafiq. "Techniques visuelles pour la détection et le suivi d'objets 2D." Phd thesis, INSA de Rennes, 2014. http://tel.archives-ouvertes.fr/tel-00981107.
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De nos jours, le traitement et l'analyse d'images trouvent leur application dans de nombreux domaines. Dans le cas de la navigation d'un robot mobile (fauteuil roulant) en milieu intérieur, l'extraction de repères visuels et leur suivi constituent une étape importante pour la réalisation de tâches robotiques (localisation, planification, etc.). En particulier, afin de réaliser une tâche de franchissement de portes, il est indispensable de détecter et suivre automatiquement toutes les portes qui existent dans l'environnement. La détection des portes n'est pas une tâche facile : la variation de l'état des portes (ouvertes ou fermées), leur apparence (de même couleur ou de couleur différentes des murs) et leur position par rapport à la caméra influe sur la robustesse du système. D'autre part, des tâches comme la détection des zones navigables ou l'évitement d'obstacles peuvent faire appel à des représentations enrichies par une sémantique adaptée afin d'interpréter le contenu de la scène. Pour cela, les techniques de segmentation permettent d'extraire des régions pseudo-sémantiques de l'image en fonction de plusieurs critères (couleur, gradient, texture...). En ajoutant la dimension temporelle, les régions sont alors suivies à travers des algorithmes de segmentation spatio-temporelle. Dans cette thèse, des contributions répondant aux besoins cités sont présentées. Tout d'abord, une technique de détection et de suivi de portes dans un environnement de type couloir est proposée : basée sur des descripteurs géométriques dédiés, la solution offre de bons résultats. Ensuite, une technique originale de segmentation multirésolution et hiérarchique permet d'extraire une représentation en régions pseudo-sémantique. Enfin, cette technique est étendue pour les séquences vidéo afin de permettre le suivi des régions à travers le suivi de leurs contours. La qualité des résultats est démontrée et s'applique notamment au cas de vidéos de couloir.
Book chapters on the topic "Segmentation des pores"
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Jiqun, Zhang, Hu Chungjin, Liu Xin, He Dongmei, and Li Hua. "An Algorithm for Rock Pore Image Segmentation." In Lecture Notes in Electrical Engineering, 243–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46578-3_28.
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Jiang, Hao. "Finding Human Poses in Videos Using Concurrent Matching and Segmentation." In Computer Vision – ACCV 2010, 228–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19315-6_18.
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Krüger, Nina, Jan Brüning, Leonid Goubergrits, Matthias Ivantsits, Lars Walczak, Volkmar Falk, Henryk Dreger, Titus Kühne, and Anja Hennemuth. "Deep Learning-Based Pulmonary Artery Surface Mesh Generation." In Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers, 140–51. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52448-6_14.
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AbstractProperties of the pulmonary artery play an essential role in the diagnosis and treatment planning of diseases such as pulmonary hypertension. Patient-specific simulation of hemodynamics can support the planning of interventions. However, the variable complex branching structure of the pulmonary artery poses a challenge for image-based generation of suitable geometries. State-of-the-art segmentation-based approaches require an interactive 3D surface reconstruction to prepare the simulation geometry. We propose a deep learning approach to generate a 3D surface mesh of the pulmonary artery from CT images suitable for simulation. The proposed method is based on the Voxel2Mesh algorithm and includes a voxel encoder and decoder as well as a mesh decoder to deform a prototype mesh. An additional centerline coverage loss facilitates the reconstruction of the branching structure. Furthermore, vertex classification allows for the definition of in- and outlets. Our model was trained with 48 human cases and tested on 10 human cases annotated by two observers. The differences in the anatomical parameters inferred from the automatic surface generation correspond to the differences between the observers’ annotations. The suitability of the generated mesh geometries for numerical flow simulations is demonstrated.
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Lu, Siwei, Xiaofang Zhao, Huazhu Liu, and Hongjie Liang. "Semiconductor Material Porosity Segmentation in Flame Retardant Materials SEM Images Using Data Augmentation and Transfer Learning." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240011.
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Non-halogenated flame retardants are becoming the trend in the development of polymer flame retardant materials due to their high flame retardant efficiency and low generation of toxic smoke gases. Non-halogenated flame retardants achieve flame retardancy by forming a dense char layer and generating non-combustible gases, with the micro-porous structure of the char residue being crucial for studying the flame retardant mechanism. This study focuses on the segmentation of pores in scanning electron microscopy (SEM) images of the combustion char layer of non-halogenated flame retardant materials, which are cropped and labeled to form a unified dataset. We investigate the SEM image pore segmentation using data augmentation and transfer learning, addressing the challenge of limited sample size. We explore the impact of different data augmentation techniques and transfer learning on model performance. Additionally, we compare convolutional neural network (CNN) segmentation algorithms with traditional segmentation methods. Experimental results demonstrate that CNN segmentation algorithms outperform traditional methods in terms of segmentation accuracy. Offline data augmentation enhances model stability compared to online data augmentation, and adopting transfer learning significantly improves model performance metrics. Specifically, when training with VGG backbone weights through transfer learning, the average pixel accuracy and average intersection over union reach 94.49% and 89.88%, respectively.
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Nandhitha, N. M., S. Emalda Roslin, Rekha Chakravarthi, and M. S. Sangeetha. "Feasibility of Infrared Thermography for Health Monitoring of Archeological Structures." In Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210021.
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Archeological assets of the nation are to be preserved and rejuvenated. Ageing of these sites poses a major challenge in assessing the health of these structures. Hence it necessitates a technique that is non contact non invasive and non hazardous. Passive InfraRed Thermography is one such technique that uses an IR camera to capture the temperature variations. Thermal variations are mapped as thermographs. Interpretation of thermographs provides information about the health of the archeological structures. As the paradigm has shifted to computer aided interpretation, segmentation techniques and line profiling are used for describing the hotspot. Of the various segmentation techniques, morphological image processing provides accurate segmentation of cold spot.
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Hiremath, Shilpa, and A. Shobha Rani. "Image Filtering Using Anisotropic Diffusion for Brain Tumor Detection." In Applications of Parallel Data Processing for Biomedical Imaging, 244–60. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2426-4.ch012.
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Brain tumor analysis is a critical aspect of medical applications, offering valuable structural and functional insights crucial for disease diagnosis. Early detection of tumors significantly enhances treatment outcomes and patient survival rates. However, the manual segmentation of numerous magnetic resonance images poses challenges due to the increased risk of human error. Therefore, there is a pressing need for computer-aided detection systems to ensure higher accuracy and faster tumor identification. In our work, we propose computer-aided techniques utilizing anisotropic diffusion filtering, Otsu threshold segmentation, and morphological procedures for noise reduction, segmentation, and tumor area detection in MR images. Our approach aims to streamline the process of tumor identification by automating key steps through advanced image processing methods. Notably, simulation results highlight the superiority of anisotropic diffusion and Otsu thresholding over other filtering and segmentation combinations, underscoring their effectiveness in enhancing tumor detection accuracy.
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Prabha V., Punya, and Sriraam N. "A Primitive Survey on Ultrasonic Imaging-Oriented Segmentation Techniques for Detection of Fetal Cardiac Chambers." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 1455–66. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch074.
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Recognition of presence of fetal cardiac chambers through ultrasonic Doppler imaging poses a huge challenge for the clinical community. The four-chamber view and outflow tracts are found to be a potential identity marker for presence of all heart chambers as well as current states of fetal heart. Given the cine loop ultrasonic imaging sequences, computer-aided diagnostic tools have been developed to detect and measures the chambers through automated mode. Segmentation and region of interest identification process contribute significantly towards the presence of heart chamber and presence of abnormality. This study provides a primitive survey towards the ultrasonic imaging-oriented segmentation techniques for detection/recognition of all four fetal cardiac chambers. The challenges for the biomedical community were also reported.
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Zhang, Yan. "A New Method for Improving the Accuracy of Word Segmentation in Modern Chinese Texts." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia231409.
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Chinese does not adopt the form of word segmentation in writing like English. The role of words in NLP is enormous and can have a significant impact on subsequent tasks. Presently, NLP research in China mainly focuses on modern Chinese, leaving a gap in studying the pre modern Chinese stage during the late Qing Dynasty to early Republic. Many texts from this time were in traditional paper books with varying preservation conditions, making it hard to obtain a representative dataset, limiting in-depth research. Most texts haven’t undergone digital processing, lacking segmentation or annotation. This poses great challenges for researchers. Researchers must design word segmentation algorithms from scratch, increasing difficulty and workload. By using N-gram to extract bigram and trigram fragments, and combining with 100 Year Chinese New Words and Phrases Dictionary and other books, this paper constructs a Chinese wordlist at the end of the Qing Dynasty to the beginning of the Republic of China. 10000 sentences were randomly selected from the corpus constructed in this article and manually segmented and annotated. The experimental results show that adding the wordlist constructed in this article has indeed improved the accuracy of word segmentation for most of these 10000 sentences. Specifically, the three software (CoreNLP, FudanNLP, and Jieba) show a certain degree of performance improvement when using the wordlist. For HanLP, the use of wordlist in this set of data did not bring significant performance improvement, or even a slight decline to some extent.
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Win, Htwe Pa Pa, Phyo Thu Thu Khine, and Khin Nwe Ni Tun. "Character Segmentation Scheme for OCR System." In Intelligent Computer Vision and Image Processing, 262–71. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3906-5.ch018.
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Automatic machine-printed Optical Characters or texts Recognizers (OCR) are highly desirable for a multitude of modern IT applications, including Digital Library software. However, the state of the art OCR systems cannot do for Myanmar scripts as the language poses many challenges for document understanding. Therefore, the authors design an Optical Character Recognition System for Myanmar Printed Document (OCRMPD), with several proposed techniques that can automatically recognize Myanmar printed text from document images. In order to get more accurate system, the authors propose the method for isolation of the character image by using not only the projection methods but also structural analysis for wrongly segmented characters. To reveal the effectiveness of the segmentation technique, the authors follow a new hybrid feature extraction method and choose the SVM classifier for recognition of the character image. The proposed algorithms have been tested on a variety of Myanmar printed documents and the results of the experiments indicate that the methods can increase the segmentation accuracy as well as recognition rates.
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Shillcock, Richard, Paul Cairns, Nick Chater, and Joe Levy. "Statistical and Connectionist Modelling of the Development of Speech Segmentation." In Models of Language Acquisition, 103–20. Oxford University PressOxford, 2000. http://dx.doi.org/10.1093/oso/9780198299899.003.0006.
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Abstract The speech signal is typically continuous; only a minority of word boundaries are marked by any recognizable acoustic cue such as a pause. The continuous nature of speech poses a problem for the adult speaker of the language, in that processing the signal requires a complete parse into words yet any string of more than a few segments is locally multiply ambiguous: given only a phonetic transcription, most words contain other words, in the way that curtain contains cur, or floor contains or. Segmentation strategies are available to the adult listener that are not given to the infant, as the former possesses both a lexicon containing the phonological specification of the words of the language, and a knowledge of the syntax and semantics of the language. For instance, the adult listener may recognize a word before its acoustic offset and hence may be able to predict the end of the current word and the start of the next; indeed, this strategy featured explicitly in one early model of word recognition (Cole and Jakimik 1980 ). The adult listener may be able to recruit syntactic knowledge to predict and identify closed-class words (the short grammatical, or function words) (Shillcock and Bard 1993), and hence identify their boundaries too. The infant, faced with the speech sounds of an unknown language, is unable to draw on such knowledge, yet over the first two years of life individual words are isolated in comprehension, stored and begin to be deployed in production. This chapter is concerned with the nature of the information that the infant might exploit to obtain a foothold on the segmentation problem.
Conference papers on the topic "Segmentation des pores"
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Joshi, R. M. "Self-Consistent Approximation for Porosity Segmentation." In Indonesian Petroleum Association - 46th Annual Convention & Exhibition 2022. Indonesian Petroleum Association, 2022. http://dx.doi.org/10.29118/ipa22-g-121.
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Carbonate reservoirs have been known to be a major source of hydrocarbons; it is well known that approximately 60% of the oil and 40% of the gas reserves in the world are found in carbonates, yet the understanding of the carbonate reservoir poses a significant challenge in exploration and exploitation. Presence of secondary porosity which differentiates it from clastic reservoirs brings its own set of complexity primarily owing to the poro-perm relationship. Carbonate fields in Bombay offshore (Western offshore of India) are no different. In order to understand the poro-perm complexity of one such field in Bombay offshore, the core samples obtained from wells went through Scanning Electron Microscopy (SEM). This study incorporates the results of experiments performed on core samples to determine the pore size distribution and the porosity of the samples at the given depth. The Self-Consistent Approximation (SCA) approach is applied on Scanning Electron Microscope (SEM) image data to determine the elastic properties of rocks and porosity partitioning of carbonate reservoir located in the western offshore region, India. The perquisites for this SCA modelling approach were sonic derived logs and SEM data extracted from core samples. The SEM images of cores from ten different depths and two different wells are analyzed by an algorithm to quantify the type of pores into cracks, inter-particle, and stiff defined by their aspect ratios. The sonic velocities were inverted using optimization technique for the entire depth range of the one well log. Machine learning algorithm was used to estimate the pore aspect ratio’s probability density. This study attempts to achieve porosity partitioning in carbonate reservoirs using SCA that will help in understanding the complex porosity system of these reservoirs and to develop a petro-physical and rock physics model to deduce the scalability of its different properties, not only in Bombay offshore but anywhere else.
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Wong, Vivian Wen Hui, Max Ferguson, Kincho H. Law, Yung-Tsun Tina Lee, and Paul Witherell. "Segmentation of Additive Manufacturing Defects Using U-Net." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-68885.
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Abstract Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a non-destructive inspection technique often used for AM parts. Although defects within AM specimens can be identified and segmented by manually thresholding the XCT images, the process can be tedious and inefficient, and the segmentation results can be ambiguous. The variation in the shapes and appearances of defects also poses difficulty in accurately segmenting defects. This paper describes an automatic defect segmentation method using U-Net based deep convolutional neural network (CNN) architectures. Several models of U-Net variants are trained and validated on an AM XCT image dataset containing pores and cracks, achieving a best mean intersection over union (IOU) value of 0.993. Performance of various U-Net models is compared and analyzed. Specific to AM porosity segmentation with XCT images, several techniques in data augmentation and model development are introduced. This work demonstrates that, using XCT images, U-Net can be effectively applied for automatic segmentation of AM porosity with high accuracy. The method can potentially help improve quality control of AM parts in an industry setting.
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Sugiarto, Bambang, Esa Prakasa, Ratih Damayanti, Gunawan, Riffa Haviani Laluma, and A. Andini Radisya Pratiwi. "Pores Segmentation Based on Active Contour Model for Automatic Wood Species Identification." In 2023 17th International Conference on Telecommunication Systems, Services, and Applications (TSSA). IEEE, 2023. http://dx.doi.org/10.1109/tssa59948.2023.10366953.
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Panaitescu, C. T., K. Wu, Y. Tanino, and A. Starkey. "AI Enabled Digital Rock Technology for Larger Scale Modelling of Complex Fractured Subsurface Rocks." In SPE Offshore Europe Conference & Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215499-ms.
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Abstract Quantifying and modelling fractured subsurface rocks, characterised by their complex geometric heterogeneity, is crucial to the geo-energy transition because it helps predict flow properties in fractured systems. Multiscale Digital Rock Technology (MDRT) offers a solution to analyse comprehensive fluid flow mechanisms from the pore scale to much larger scales. In addition, artificial intelligence (AI) techniques can add significant value to geoscience workflows, automating time-consuming tasks, some even prohibitively long if done manually (such as 3D image volume labelling), and obtaining new insight from combining highly diverse data sources. We propose a novel machine-learning algorithm for semantic segmentation of rock matrix, fractures, vugs, and secondary mineralogy. After implementing and examining deep and shallow-learning approaches, we concluded to use shallow machine-learning methods for increased computational efficiency and explainability while achieving comparable accuracy. By integrating our novel machine-learning algorithm into the multiscale Pore Network Model (PNM) code, we improve the modelling method of subsurface flow, particularly in complex fractured subsurface systems and carbonates. The resulting algorithm accurately discriminates between pores, fractures, and vugs. Therefore, it enhances the accuracy of pore-fracture-vug network extraction and simulation and provides an improved analysis of complex rock structures. Moreover, the segmentation results are integrated into a Fracture-Pore Network Model, validated against high-fidelity OpenFOAM simulation. This integration of fractures into the PNM code allows for larger scale fluid flow simulation in complex fractured subsurface systems. The current research produced a fast algorithm that accurately and automatically segments X-ray micro-computed tomography (micro-CT) samples having pores, fractures, and vugs. Our validation also showcases the potential of this algorithm to improve existing industrial core analysis practices.
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KALEEL, IBRAHIM, SOPHIE-MARIA RAUSCHER, and ARUN RAINA. "RECONSTRUCTION OF A SIC-SIC CMC MICROSTRUCTURE USING DEEP LEARNING AND ADVANCED IMAGE PROCESSING TECHNIQUE." In Proceedings for the American Society for Composites-Thirty Eighth Technical Conference. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/asc38/36681.
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The paper presents a workflow for the reconstruction of a SiC-SiC ceramic matrix composite (CMC) microstructure using advanced image processing techniques and deep learning. The objective of this research is to develop highly accurate physics-based computational models for CMCs by gaining a comprehensive understanding of the microstructural features and their impact on material properties. A workflow is presented to classify voxels into individual components and extract stochastic data for establishing microstructure-property correlations. X-ray computed tomography (CT) data of the SiC/SiC CMC with a plain weave architecture and 00/900 fiber orientation are reconstructed using advanced image processing techniques. The resulting CT data is successfully segmented into three material constituents: tows, matrix, and pores. Pore segmentation is accomplished using the Otsu segmentation algorithm, while a deep learning-based U-net model is employed for accurate segmentation between SiC tows and the SiC matrix. Furthermore, an anisotropic segmentation algorithm is utilized to classify tow voxels along different directions, capturing the intricate variations within the microstructure. Geometrical and morphological attributes are extracted from the segmented data for further analysis.
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Safonov, Ilia, Anton Kornilov, and Iryna Reimers. "Rendering Semisynthetic FIB-SEM Images of Rock Samples." In 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-3027-855-863.
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Digital rock analysis is a prospective approach to estimate properties of oil and gas reservoirs. This concept implies constructing a 3D digital twin of a rock sample. Focused Ion Beam - Scanning Electron Microscope (FIB-SEM) allows to obtain a 3D image of a sample at nanoscale. One of the main specific features of FIB-SEM images in case of porous media is pore-back (or shine-through) effect. Since pores are transparent, their back side is visible in the current slice, whereas, in fact, it locates in the following ones. A precise segmentation of pores is a challenging problem. Absence of annotated ground truth complicates fine-tuning the algorithms for processing of FIB-SEM data and prevents successful application of machine- learning-based methods, which require a huge training set. Recently, several synthetic FIB- SEM images based on stochastic structures were created. However, those images strongly differ from images of real samples. We propose fast approaches to render semisynthetic FIB- SEM images, which imply that intensities of voxels of mineral matrix in a milling plane, as well as geometry of pore space, are borrowed from an image of rock sample saturated by epoxy. Intensities of voxels in pores depend on the distance from milling plane to the given voxel along a ray directed at an angle equal to the angle between FIB and SEM columns. The proposed method allows to create very realistic FIB-SEM images of rock samples with precise ground truth. Also, it opens the door for numerical estimation of plenty of algorithms for processing FIB-SEM data.
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Cao, Jinxin, Yiqiang Li, Yaqian Zhang, Wenbin Gao, Yuling Zhang, Yifei Cai, Xuechen Tang, Qihang Li, and Zheyu Liu. "Identification of Polymer Flooding Flow Channels and Characterization of Oil Recovery Factor Based On U-Net." In SPE Conference at Oman Petroleum & Energy Show. SPE, 2024. http://dx.doi.org/10.2118/218767-ms.
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Abstract Image identification is a major means to achieve quantitative characterization of the microscopic oil displacement process. Traditional digital image processing techniques usually uses a series of pixel-based algorithms, which is difficult to achieve real-time processing of large-scale images. Deep learning methods have the characteristics of fast speed and high accuracy. This paper proposes a four-channel image segmentation method based on RGB color and rock particle mask. First, the micro model rock particle mask is divided together with the RGB component to form four-channel input data through image processing technology. Pixel-level training set labels are then created through traditional image processing techniques. Through the U-Net semantic segmentation network, the pixel-level oil and water identification and recovery factor calculation of the polymer microscopic oil displacement process were carried out. Combined with the pore distance transformation algorithm, the lower limit of pore utilization for different displacement media was clarified. The results show that U-Net can achieve accurate division of oil and water areas. Compared with conventional three-channel images, the improved four-channel image proposed in this paper has significantly improved the segmentation accuracy due to the addition of the constraints of the rock particle mask, and the global accuracy can be Up to 99%. Combining some post-processing methods, this paper found that polymer flooding increased the mobilization degree of small pores on the basis of water flooding and lowered the lower limit of pore mobilization from 25 μm to 16 μm. In microscopic experiments, the recovery factor was increased by 24.01%, finally achieving rapid and accurate quantitative characterization of the microscopic oil displacement process. The four-channel image method based on the U-Net semantic segmentation network and the improved rock particle mask proposed in this article has strong adaptability to the identification of flow channels in the microscopic oil displacement process. Quantitative characterization of the lower limit of pore movement and recovery degree during microscopic oil displacement provides a new method for microscopic image processing.
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Hage, Ilige S., and Ramsey F. Hamade. "Distribution of Porosity in Cortical (Bovine) Bone." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51703.
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Pores (namely lacunae, clusters of canaliculi, Haversian canals, and resorption cavities) are present throughout cortical bone. This paper characterizes the area fraction (AF, %)) of each type of these pores as function of distance from the bone’s geometric center while noting the region in which such pores are located: midcortical or periosteal. Optical slides (at 20X) are taken from 2 cortical bone biopsies named bone 1 and bone 2 and cut at mid-diaphysis femur from 2 different (about 2 year-old) bovine cows. The slides are collected from posterior (pericortical) and anterior (intracortical) locations. The area of each of these biopsies is about 2.5mm × 3mm located near the outer cortex of the bone. In polar coordinates from the bone’s center, the areas cover radial distance of about 3.3 mm (of radius, R) and encompass an arc of 10°. Automated segmentation is used to locate and identify all pores in the optical slides the shapes of which are best fitted into ellipses. Values of area fraction, AF (%) of said fitted ellipses are then automatically calculated in secondary osteons for both regions. Variations in values of area fraction AF (%) are related to actual areas of pores (based on their defining equations). Observations suggest that area fractions (%) of all pores (but to lesser degree for Haversian canals), to significantly decrease linearly and in a steep fashion with R (statistically significant, p < 0.01) in the anterior region where osteonal growth is expected to have continued to develop. However, in the posterior region where osteonal growth appears to have matured, area fraction (%) values seem to have reached a steady state resulting in fairly flat behavior versus R. All observations are equally applicable for biopsies collected from bone 1 and bone 2.
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Ding, Nan, Hélène Urien, Florence Rossant, Jérémie Sublime, and Michel Paques. "Context-aware Attention U-Net for the segmentation of pores in Lamina Cribrosa using partial points annotation." In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00088.
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Hage, Ilige S., Mu'tasem A. Shehadeh, and Ramsey F. Hamade. "Application of Homogenization Theory to Study the Mechanics of Cortical Bone." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-36427.
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Homogenization theory is utilized to study the effect on the axial stiffness of secondary osteons in cortical bone due to the presence of micro porous features (e.g., lacunae, canaliculi clusters, and Haversian canals). Specifically, 2 geometric characteristics were used to describe these features within the secondary osteons: volume fraction (% porosity) and shape (circular- or elliptical-shaped). Such information was determined for each individual porous feature from an image segmentation methodology developed earlier by Hage and Hamade. For each feature, aspect ratio vectors (or arrays of ratios for each individual porous feature) were used to classify each pore inhomogeneity as cylindrical, elliptical or irregular shape. Two prominent homogenization theories were used: the Mori-Tanaka (MT) and the generalized self-consistent method (GSCM). Using the results of image segmentation, it was possible to calculate the respective Eshelby tensors of each porous feature. To calculate the isotropic stiffness tensors for matrix (Cm) and pores (Cp) the Young’s modulus and Poisson’s ratio for the matrix (Em, νm) were assigned as obtained from literature and as those of blood (Ep=10MPa, νp= 0.3), respectively. The effective elastic stiffness tensors (C*) for the secondary osteons were obtained from which axial Young’s modulus was obtained as function of volume fraction (% porosity) of each pore type and their individual shapes. The normalized axial Young’s modulus was found to 1) significantly decrease with increasing volume fraction (%) of porosity and 2) for the same % porosity, to slightly decrease (increase) with increasing ratio of circular-shaped to elliptical-shaped (elliptical-shaped to circular-shaped) porous features. These findings were validated using experimental micro-indentation study performed on secondary osteons.