The well-documented relation between bone mineral density (BMD) and bone compression strength constitutes the basis for osteoporosis diagnostics and the assessment of fracture risk. Simultaneously, this relation demonstrates a considerable scatter of results as bones of identical mineral density may have significantly different properties. The experimentally confirmed theorem that two materials or tissues of identical microstructure have identical properties leads to the evaluation of various quantitative stereological parameters (also referred to in biomedicine as histomorphology). These parameters, obtained from analysis of 2D or 3D images, have been used in numerous attempts to explain changes in bone strength. Although numerous correlation dependencies, often with high correlation coefficients, were evaluated, we do not know which parameters are worth evaluating, and there is no physical interpretation of these relations. An extended statistical analysis was accomplished on the basis of analysis of 3D images from 23 lumbar (L3) vertebrae scanned with micro-CT and the results of subsequent compression tests. A new parameter called SDF (structure destruction factor) was proposed in order to characterise the quality of 3D trabecular structures, and its significance was demonstrated. The final correlation function, which uses only three stereological parameters, made it possible to predict compression strength with considerable precision. The estimated values correlated very well with the apparent values (correlation coefficient r=0.96). Finally, the stereological parameters most suitable for characterisation of bone compression strength were chosen and a mechanism responsible for the changes in mechanical properties was proposed. The results obtained defined the necessary improvements in diagnostic techniques that would allow for more efficient quantitative microstructure evaluation and guidelines on how to improve treatment of patients with weakened bones.
{"title":"ON THE ROLE OF HISTOMORPHOMETRIC (STEREOLOGICAL) MICROSTRUCTURE PARAMETERS IN THE PREDICTION OF VERTEBRAE COMPRESSION STRENGTH","authors":"L. Wojnar, A. Gądek-Moszczak, J. Pietraszek","doi":"10.5566/IAS.2028","DOIUrl":"https://doi.org/10.5566/IAS.2028","url":null,"abstract":"The well-documented relation between bone mineral density (BMD) and bone compression strength constitutes the basis for osteoporosis diagnostics and the assessment of fracture risk. Simultaneously, this relation demonstrates a considerable scatter of results as bones of identical mineral density may have significantly different properties. The experimentally confirmed theorem that two materials or tissues of identical microstructure have identical properties leads to the evaluation of various quantitative stereological parameters (also referred to in biomedicine as histomorphology). These parameters, obtained from analysis of 2D or 3D images, have been used in numerous attempts to explain changes in bone strength. Although numerous correlation dependencies, often with high correlation coefficients, were evaluated, we do not know which parameters are worth evaluating, and there is no physical interpretation of these relations. An extended statistical analysis was accomplished on the basis of analysis of 3D images from 23 lumbar (L3) vertebrae scanned with micro-CT and the results of subsequent compression tests. A new parameter called SDF (structure destruction factor) was proposed in order to characterise the quality of 3D trabecular structures, and its significance was demonstrated. The final correlation function, which uses only three stereological parameters, made it possible to predict compression strength with considerable precision. The estimated values correlated very well with the apparent values (correlation coefficient r=0.96). Finally, the stereological parameters most suitable for characterisation of bone compression strength were chosen and a mechanism responsible for the changes in mechanical properties was proposed. The results obtained defined the necessary improvements in diagnostic techniques that would allow for more efficient quantitative microstructure evaluation and guidelines on how to improve treatment of patients with weakened bones.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"32 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81565517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Microstructure of a material stores the genesis of the material and shows various properties of the material. To efficiently analyse the microstructure of a material, the segmentation of different phases or constituents is an important step. However, in general, due to the microstructure’s complexity, most of segmentation is manually done by human experts. It is challenging to automatically segment the material phases and the microstructure. In this work, we propose a method which combines the the dilation operator, GLCM (gray-level co-occurrence matrix), Hough transform and DBSCAN (density-based spatial clustering of applications with noise) for phases segmentation in the examples of certain material of eutectic HfB2-B4C ceramics. In the segmented regions, the further analysis for the microstructural elements is done with DBSCAN. The experimental results show that the proposed method achieves 95.75% segmentation accuracy for segmenting phases and 86.64% correct classification rate for the microstructure in the segmented phases. These experimental results show that our method is effective for the difficult task of the both segmentation and classification of the microstructural characteristics.
{"title":"SEGMENTATION AND ANALYSIS METHOD FOR TWO-PHASE CERAMIC (HfB2-B4C) BASED ON THE DETECTION OF VIRTUAL BOUNDARIES","authors":"Yuexing Han, Chuanbin Lai, Bing Wang, Tian-Yi Hu, Dong-Li Hu, Hui Gu","doi":"10.5566/IAS.1992","DOIUrl":"https://doi.org/10.5566/IAS.1992","url":null,"abstract":"Microstructure of a material stores the genesis of the material and shows various properties of the material. To efficiently analyse the microstructure of a material, the segmentation of different phases or constituents is an important step. However, in general, due to the microstructure’s complexity, most of segmentation is manually done by human experts. It is challenging to automatically segment the material phases and the microstructure. In this work, we propose a method which combines the the dilation operator, GLCM (gray-level co-occurrence matrix), Hough transform and DBSCAN (density-based spatial clustering of applications with noise) for phases segmentation in the examples of certain material of eutectic HfB2-B4C ceramics. In the segmented regions, the further analysis for the microstructural elements is done with DBSCAN. The experimental results show that the proposed method achieves 95.75% segmentation accuracy for segmenting phases and 86.64% correct classification rate for the microstructure in the segmented phases. These experimental results show that our method is effective for the difficult task of the both segmentation and classification of the microstructural characteristics.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"15 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75256003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Dobrovolskij, Johannes Persch, K. Schladitz, G. Steidl
A frequently applied indicator of tubular structures is based on the eigenvalues of the Hessian matrix of the original image convolved with a Gaussian, whose standard derivation depends on the size of the tubes. Hence the tube size must either be known in advance or a whole scale of standard deviations has to be tested resulting in higher computational costs – a serious obstacle for data with varying tube thickness.In this paper, we propose to modify the structure indicator by replacing the derivatives of the Gaussian smoothed function by the Riesz transform. We show by various numerical examples that the resulting structure indicator is scale independent. Smoothing with a Gaussian is just necessary to cope with the noise in the image, but is not related to the size of the tubular structures. We apply the novel structure indicator for the fiber orientation analysis of fibrous materials and for the segmentation of leather. The latter one was a special challenging application since all scales are present in the microstructure of leather.
{"title":"STRUCTURE DETECTION WITH SECOND ORDER RIESZ TRANSFORMS","authors":"D. Dobrovolskij, Johannes Persch, K. Schladitz, G. Steidl","doi":"10.5566/IAS.1964","DOIUrl":"https://doi.org/10.5566/IAS.1964","url":null,"abstract":"A frequently applied indicator of tubular structures is based on the eigenvalues of the Hessian matrix of the original image convolved with a Gaussian, whose standard derivation depends on the size of the tubes. Hence the tube size must either be known in advance or a whole scale of standard deviations has to be tested resulting in higher computational costs – a serious obstacle for data with varying tube thickness.In this paper, we propose to modify the structure indicator by replacing the derivatives of the Gaussian smoothed function by the Riesz transform. We show by various numerical examples that the resulting structure indicator is scale independent. Smoothing with a Gaussian is just necessary to cope with the noise in the image, but is not related to the size of the tubular structures. We apply the novel structure indicator for the fiber orientation analysis of fibrous materials and for the segmentation of leather. The latter one was a special challenging application since all scales are present in the microstructure of leather.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"17 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90407996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The intact grains of the dead leaves model enables us to generate random media with non overlapping grains. Using the time non homogeneous sequential model with convex grains, theoretically very dense packings can be generated, up to a full covering of space. For these models, the theoretical volume fraction, the size distribution of grains, and the pair correlation function of centers of grains are given.
{"title":"SOME DENSE RANDOM PACKINGS GENERATED BY THE DEAD LEAVES MODEL","authors":"D. Jeulin","doi":"10.5566/IAS.2081","DOIUrl":"https://doi.org/10.5566/IAS.2081","url":null,"abstract":"The intact grains of the dead leaves model enables us to generate random media with non overlapping grains. Using the time non homogeneous sequential model with convex grains, theoretically very dense packings can be generated, up to a full covering of space. For these models, the theoretical volume fraction, the size distribution of grains, and the pair correlation function of centers of grains are given.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"87 5 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84028300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The isotropic Cavalieri design is based on a isotropically oriented set of parallel systematic sections a constant distance apart. Its advantage over the ordinary Cavalieri design is twofold - first, besides volume it allows the unbiased estimation of surface area, and second, the error variance predictor for the volume estimator is much simpler, involving only the surface area of the object, and the distance between sections. In an earlier paper, the two hemispheres of a rat brain were arranged perpendicular to each other before sectioning, aiming at reducing the error variance with respect to other arrangements (such as the aligned one) by exploiting an intuitively plausible antithetic effect. Because the total surface area of the objects is unchanged under any arrangements, however, the error variance predictor for the volume estimator does not depend on object shape, which looks intriguing. Using reconstructions of the mentioned hemispheres, we dilucidate the aparent paradox by means of automatic Monte Carlo replications of the relevant volume estimates under the antithetic and the aligned arrangements.
{"title":"ON THE PRECISION OF THE ISOTROPIC CAVALIERI DESIGN","authors":"Javier González-Villa, M. Cruz, L. Cruz-Orive","doi":"10.5566/IAS.1947","DOIUrl":"https://doi.org/10.5566/IAS.1947","url":null,"abstract":"The isotropic Cavalieri design is based on a isotropically oriented set of parallel systematic sections a constant distance apart. Its advantage over the ordinary Cavalieri design is twofold - first, besides volume it allows the unbiased estimation of surface area, and second, the error variance predictor for the volume estimator is much simpler, involving only the surface area of the object, and the distance between sections. In an earlier paper, the two hemispheres of a rat brain were arranged perpendicular to each other before sectioning, aiming at reducing the error variance with respect to other arrangements (such as the aligned one) by exploiting an intuitively plausible antithetic effect. Because the total surface area of the objects is unchanged under any arrangements, however, the error variance predictor for the volume estimator does not depend on object shape, which looks intriguing. Using reconstructions of the mentioned hemispheres, we dilucidate the aparent paradox by means of automatic Monte Carlo replications of the relevant volume estimates under the antithetic and the aligned arrangements.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72765149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mathieu de Langlard, F. Lamadie, S. Charton, J. Debayle
In this paper a new approach to geometrically model and characterize 2D silhouette images of two-phase flows is proposed. The method consists of a 3D modeling of the particles population based on some morphological and interaction assumptions. It includes the following steps. First, the main analytical properties of the proposed model – which is an adaptation of the Matérn type II model – are assessed, namely the effect of the thinning procedures on the population’s fundamental properties. Then, orthogonal projections of the model realizations are made to obtain 2D modeled images. The inference technique we propose and implement to determine the model parameters is a two-step numerical procedure: after a first guess of the parameters is defined, an optimization procedure is achieved to find the local minimum closest to the constructed initial solution. The method was validated on synthetic images, which has highlighted the efficiency of the proposed calibration procedure. Finally, the model was used to analyze real, i.e., experimentally acquired, silhouette images of calibrated polymethyl methacrylate (PMMA) particles. The population properties are correctly evaluated, even when suspensions of concentrated monodispersed and bidispersed particles are considered, hence highlighting the method’s relevance to describe the typical configurations encountered in bubbly flows and emulsions.
{"title":"A 3D STOCHASTIC MODEL FOR GEOMETRICAL CHARACTERIZATION OF PARTICLES IN TWO-PHASE FLOW APPLICATIONS","authors":"Mathieu de Langlard, F. Lamadie, S. Charton, J. Debayle","doi":"10.5566/IAS.1942","DOIUrl":"https://doi.org/10.5566/IAS.1942","url":null,"abstract":"In this paper a new approach to geometrically model and characterize 2D silhouette images of two-phase flows is proposed. The method consists of a 3D modeling of the particles population based on some morphological and interaction assumptions. It includes the following steps. First, the main analytical properties of the proposed model – which is an adaptation of the Matérn type II model – are assessed, namely the effect of the thinning procedures on the population’s fundamental properties. Then, orthogonal projections of the model realizations are made to obtain 2D modeled images. The inference technique we propose and implement to determine the model parameters is a two-step numerical procedure: after a first guess of the parameters is defined, an optimization procedure is achieved to find the local minimum closest to the constructed initial solution. The method was validated on synthetic images, which has highlighted the efficiency of the proposed calibration procedure. Finally, the model was used to analyze real, i.e., experimentally acquired, silhouette images of calibrated polymethyl methacrylate (PMMA) particles. The population properties are correctly evaluated, even when suspensions of concentrated monodispersed and bidispersed particles are considered, hence highlighting the method’s relevance to describe the typical configurations encountered in bubbly flows and emulsions.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"127 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80048900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For perfect diagnosis of brain tumour, it is necessary to identify tumour affected regions in the brain in MRI (Magnetic Resonance Imaging) images effectively and compression of these images for transmission over a communication channel at high speed with better visual quality to the experts. An attempt has been made in this paper for identifying tumour regions with optimal thresholds which are optimized with the proposed Hybrid Bacteria Foraging Optimization Algorithm (BFOA) and Particle Swarm Optimization (PSO) named (HBFOA-PSO) by maximizing the Renyi’s entropy and Kapur’s entropy. BFOA may be trapped into local optimal problem and delay in execution time (convergence time) because of random chemo taxis steps in the procedure of algorithm and to get global solution, a theory of swarming is commenced in the structure of HBFOA-PSO. Effectiveness of this HBFOA-PSO is evaluated on six different MRI images of brain with tumours and proved to be better in Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Fitness Function.
{"title":"NEW BACTERIA FORAGING AND PARTICLE SWARM HYBRID ALGORITHM FOR MEDICAL IMAGE COMPRESSION","authors":"G. Kumari, G. Rao, B. Rao","doi":"10.5566/IAS.1865","DOIUrl":"https://doi.org/10.5566/IAS.1865","url":null,"abstract":"For perfect diagnosis of brain tumour, it is necessary to identify tumour affected regions in the brain in MRI (Magnetic Resonance Imaging) images effectively and compression of these images for transmission over a communication channel at high speed with better visual quality to the experts. An attempt has been made in this paper for identifying tumour regions with optimal thresholds which are optimized with the proposed Hybrid Bacteria Foraging Optimization Algorithm (BFOA) and Particle Swarm Optimization (PSO) named (HBFOA-PSO) by maximizing the Renyi’s entropy and Kapur’s entropy. BFOA may be trapped into local optimal problem and delay in execution time (convergence time) because of random chemo taxis steps in the procedure of algorithm and to get global solution, a theory of swarming is commenced in the structure of HBFOA-PSO. Effectiveness of this HBFOA-PSO is evaluated on six different MRI images of brain with tumours and proved to be better in Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Fitness Function.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"25 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87079210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blind image deconvolution/deblurring (BID) is a challenging task due to lack of prior information about the blurring process and image. Noise and ringing artefacts resulted during the restoration process further deter fine restoration of the pristine image. These artefacts mainly arise from using a poorly estimated point spread function (PSF) combined with an ineffective restoration filter. This paper presents a BID scheme based on the steepest descent in kurtosis maximization. Assuming uniform blur, the PSF can be modelled by a parametric form. The scheme tries to estimate the blur parameters by maximizing kurtosis of the deblurred image. The scheme is devised to handle any type of blur that can be framed into a parametric form such as Gaussian, motion and out-of-focus. Gradients for the blur parameters are computed and optimized in the direction of increasing kurtosis value using a steepest descent scheme. The algorithms for several common blurs are derived and the effectiveness has been corroborated through a set of experiments. Validation has also been carried out on various real examples. It is shown that the scheme optimizes on the parameters in a close vicinity of the true parameters. Results of both benchmark and real images are presented. Both full-reference and non-reference image quality measures have been used in quantifying the deblurring performance. The results show that the proposed method offers marked improvements over the existing methods.
{"title":"PARAMETRIC BLIND IMAGE DEBLURRING WITH GRADIENT BASED SPECTRAL KURTOSIS MAXIMIZATION","authors":"Aftab Khan, Hujun Yin","doi":"10.5566/IAS.1887","DOIUrl":"https://doi.org/10.5566/IAS.1887","url":null,"abstract":"Blind image deconvolution/deblurring (BID) is a challenging task due to lack of prior information about the blurring process and image. Noise and ringing artefacts resulted during the restoration process further deter fine restoration of the pristine image. These artefacts mainly arise from using a poorly estimated point spread function (PSF) combined with an ineffective restoration filter. This paper presents a BID scheme based on the steepest descent in kurtosis maximization. Assuming uniform blur, the PSF can be modelled by a parametric form. The scheme tries to estimate the blur parameters by maximizing kurtosis of the deblurred image. The scheme is devised to handle any type of blur that can be framed into a parametric form such as Gaussian, motion and out-of-focus. Gradients for the blur parameters are computed and optimized in the direction of increasing kurtosis value using a steepest descent scheme. The algorithms for several common blurs are derived and the effectiveness has been corroborated through a set of experiments. Validation has also been carried out on various real examples. It is shown that the scheme optimizes on the parameters in a close vicinity of the true parameters. Results of both benchmark and real images are presented. Both full-reference and non-reference image quality measures have been used in quantifying the deblurring performance. The results show that the proposed method offers marked improvements over the existing methods.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"11 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84764859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fluorescence microscopy imaging has become one of the essential tools used by biologists to visualize and study intracellular particles within a cell. Studying these particles is a long-term research effort in the field of microscopy image analysis, consisting of discovering the relationship between the dynamics of particles and their functions. However, biologists are faced with challenges such as the counting and tracking of these intracellular particles. To overcome the issues faced by biologists, tools which can extract the location and motion of these particles are essential. One of the most important steps in these analyses is to accurately detect particle positions in an image, termed spot detection. The detection of spots in microscopy imaging is seen as a critical step for further quantitative analysis. However, the evaluation of these microscopic images is mainly conducted manually, with automated methods becoming popular. This work presents some advances in fluorescence microscopy image analysis, focusing on the detection methods needed for quantifying the location of these spots. We review several existing detection methods in microscopy imaging, along with existing synthetic benchmark datasets and evaluation metrics.
{"title":"SPOT DETECTION METHODS IN FLUORESCENCE MICROSCOPY IMAGING: A REVIEW","authors":"Matsilele Mabaso, D. Withey, Bhekisipho Twala","doi":"10.5566/IAS.1690","DOIUrl":"https://doi.org/10.5566/IAS.1690","url":null,"abstract":"Fluorescence microscopy imaging has become one of the essential tools used by biologists to visualize and study intracellular particles within a cell. Studying these particles is a long-term research effort in the field of microscopy image analysis, consisting of discovering the relationship between the dynamics of particles and their functions. However, biologists are faced with challenges such as the counting and tracking of these intracellular particles. To overcome the issues faced by biologists, tools which can extract the location and motion of these particles are essential. One of the most important steps in these analyses is to accurately detect particle positions in an image, termed spot detection. The detection of spots in microscopy imaging is seen as a critical step for further quantitative analysis. However, the evaluation of these microscopic images is mainly conducted manually, with automated methods becoming popular. This work presents some advances in fluorescence microscopy image analysis, focusing on the detection methods needed for quantifying the location of these spots. We review several existing detection methods in microscopy imaging, along with existing synthetic benchmark datasets and evaluation metrics.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"468 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76356468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Dvořák, J. Švihlík, J. Kybic, B. Radochová, J. Janáček, J. Kukal, Jiri Borovec, D. Habart
The present paper deals with the problem of volume estimation of individual objects from a single 2D view. Our main application is volume estimation of pancreatic (Langerhans) islets and the single 2D view constraint comes from the time and equipment limitations of the standard clinical procedure.Two main approaches are followed in this paper. First, two regression-based methods are proposed, using a set of simple shape descriptors of the segmented image of the islet. Second, two example-based methods are proposed, based on a database of islets with known volume. For training and evaluation, islet volumes were determined by OPT microscopy and a semi-automatical stereological volume estimation using the so-called Fakir probes.The performance of the single image volume estimation methods is studied on a set of 99 islets from human donors. Further experiments were also performed on a stone dataset and on synthetic 3D shapes, generated using a flexible stochastic particle model. The proposed methods are fast and the experimental results show that in most situations the proposed methods perform significantly better than the methods currently used in clinical practice, which are based on simple spherical or ellipsoidal models.
{"title":"VOLUME ESTIMATION FROM SINGLE IMAGES: AN APPLICATION TO PANCREATIC ISLETS","authors":"J. Dvořák, J. Švihlík, J. Kybic, B. Radochová, J. Janáček, J. Kukal, Jiri Borovec, D. Habart","doi":"10.5566/IAS.1869","DOIUrl":"https://doi.org/10.5566/IAS.1869","url":null,"abstract":"The present paper deals with the problem of volume estimation of individual objects from a single 2D view. Our main application is volume estimation of pancreatic (Langerhans) islets and the single 2D view constraint comes from the time and equipment limitations of the standard clinical procedure.Two main approaches are followed in this paper. First, two regression-based methods are proposed, using a set of simple shape descriptors of the segmented image of the islet. Second, two example-based methods are proposed, based on a database of islets with known volume. For training and evaluation, islet volumes were determined by OPT microscopy and a semi-automatical stereological volume estimation using the so-called Fakir probes.The performance of the single image volume estimation methods is studied on a set of 99 islets from human donors. Further experiments were also performed on a stone dataset and on synthetic 3D shapes, generated using a flexible stochastic particle model. The proposed methods are fast and the experimental results show that in most situations the proposed methods perform significantly better than the methods currently used in clinical practice, which are based on simple spherical or ellipsoidal models.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"47 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86631521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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