Image Analysis & Stereology最新文献_第3页

On the Properties of Some Adaptive Morphological Filters for Salt and Pepper Noise Removal 几种用于椒盐噪声去除的自适应形态学滤波器的特性研究

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2021-03-29 DOI: 10.5566/IAS.2418

Marisol Mares-Javier, C. Guillén-Galván, R. Lemuz-López, J. Debayle

引用次数: 3

The Wavelet-Based Denoising Of Images in Fiji, With Example Applications in Structured Illumination Microscopy 斐济基于小波的图像去噪，在结构照明显微镜中的应用实例

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2021-03-29 DOI: 10.5566/IAS.2432

M. Capek, M. Blažíková, Ivan Novotny, H. Chmelova, D. Svoboda, B. Radochová, J. Janáček, Ondrej Horvath

{"title":"The Wavelet-Based Denoising Of Images in Fiji, With Example Applications in Structured Illumination Microscopy","authors":"M. Capek, M. Blažíková, Ivan Novotny, H. Chmelova, D. Svoboda, B. Radochová, J. Janáček, Ondrej Horvath","doi":"10.5566/IAS.2432","DOIUrl":"https://doi.org/10.5566/IAS.2432","url":null,"abstract":"Filtration of super-resolved microscopic images brings often troubles with removing undesired image parts like, e.g., noise, inhomogenous background and reconstruction artifacts. Standard filtration techniques, e.g., convolution- or Fourier transform-based methods are not always appropriate, since they may lower image resolution that was acquired by hi-tech and expensive microscopy systems. Thus, in this article it is proposed to filter such images using discrete wavelet transform (DWT). Newly developed Wavelet_Denoise plugin for free available Fiji software package demonstrates important possibilities of applying DWT to images: Decomposition of a filtered picture using various wavelet filters and levels of details with showing decomposed images and visualization of effects of back transformation of the picture with chosen level of suppression or denoising of wavelet coefficients. The Fiji framework allows, for example, using a plethora of various microscopic image formats for data opening, users can easily install the plugin through a menu command and the plugin supports processing 3D images in Z-stacks. The application of the plugin for removal of reconstruction artifacts and undesirable background in images acquired by super-resolved structured illumination microscopy is demonstrated as well.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"38 1","pages":"3-16"},"PeriodicalIF":0.9,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90277044","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}

引用次数: 2

Smoothing and Clustering Guided Image Decolorization 平滑和聚类引导图像脱色

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2021-03-28 DOI: 10.5566/IAS.2348

Fang Li, Yuanming Zhu

引用次数: 3

Obituary for Hans Jørgen Gottlieb Gundersen

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2021-03-26 DOI: 10.5566/IAS.2540

J. Nyengaard

引用次数: 0

Quantification of Segregation in Portland Cement Concrete Based on Spatial Distribution of Aggregate Size Fractions 基于骨料粒度空间分布的硅酸盐水泥混凝土离析定量研究

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2020-11-25 DOI: 10.5566/ias.2318

M. Ozen, M. Guler

{"title":"Quantification of Segregation in Portland Cement Concrete Based on Spatial Distribution of Aggregate Size Fractions","authors":"M. Ozen, M. Guler","doi":"10.5566/ias.2318","DOIUrl":"https://doi.org/10.5566/ias.2318","url":null,"abstract":"Segregation is one of the quality standards that must be monitored during the fabrication and placement of Portland cement concrete. Segregation refers to separation of coarse aggregate from the cement paste, resulting in inhomogeneous mixture. This study introduces a digital imaging based technique to quantify the segregation of Portland cement concrete from 2D digital images of cut sections. In the previous studies, segregation was evaluated based on the existence of coarse aggregate fraction at different geometrical regions of a sample cross section without considering its distribution characteristics. However, it is shown that almost all particle fractions can form clusters and increase the degree of segregation, thus deteriorating the structural performance of concrete. In the proposed methodology, a segregation index is developed by based on the spatial distribution of different size fractions of coarse aggregate within a sample cross section. It is shown that degradation in mixture’s homogeneity is controlled by the combined effect of particle distribution and their relative proportions in the mixture. Hence, a segregation index characterizing the mixture inhomogeneity is developed by considering not only spatial distribution of aggregate particles, but also their size fractions in the mixture. The proposed methodology can be successfully used as a quality control tool for monitoring the segregation level in hardened concrete samples.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"175 1","pages":"147-159"},"PeriodicalIF":0.9,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79762782","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}

引用次数: 6

Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture 基于局部二值卷积-反卷积结构的皮肤病灶分割

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2020-11-25 DOI: 10.5566/ias.2397

Omran Salih, Serestina Viriri

{"title":"Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture","authors":"Omran Salih, Serestina Viriri","doi":"10.5566/ias.2397","DOIUrl":"https://doi.org/10.5566/ias.2397","url":null,"abstract":"Deep learning techniques such as Deep Convolutional Networks have achieved great success in skin lesion segmentation towards melanoma detection. The performance is however restrained by distinctive and challenging features of skin lesions such as irregular and fuzzy border, noise and artefacts presence and low contrast between lesions. The methods are also restricted with scarcity of annotated lesion images training dataset and limited computing resources. Recent research in convolutional neural network (CNN) has provided a variety of new architectures for deep learning. One interesting new architecture is the local binary convolutional neural network (LBCNN), which can reduce the workload of CNNs and improve the classification accuracy. The proposed framework employs the local binary convolution on U-net architecture instead of the standard convolution in order to reduced-size deep convolutional encoder-decoder network that adopts loss function for robust segmentation. The proposed framework replaced the encoder part in U-net by LBCNN layers. The approach automatically learns and segments complex features of skin lesion images. The encoder stage learns the contextual information by extracting discriminative features while the decoder stage captures the lesion boundaries of the skin images. This addresses the issues with encoder-decoder network producing coarse segmented output with challenging skin lesions appearances such as low contrast between healthy and unhealthy tissues and fine grained variability. It also addresses issues with multi-size, multi-scale and multi-resolution skin lesion images. The deep convolutional network also adopts a reduced-size network with just five levels of encoding-decoding network. This reduces greatly the consumption of computational processing resources. The system was evaluated on publicly available dataset of ISIC and PH2. The proposed system outperforms most of the existing state-of-art.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"136 1","pages":"169-185"},"PeriodicalIF":0.9,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77449758","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}

引用次数: 10

RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning RIM-ONE DL:使用深度学习评估青光眼的统一视网膜图像数据库

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2020-11-25 DOI: 10.5566/ias.2346

Francisco José Fumero Batista, T. Díaz-Alemán, J. Sigut, S. Alayón, R. Arnay, D. Ángel-Pereira

{"title":"RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning","authors":"Francisco José Fumero Batista, T. Díaz-Alemán, J. Sigut, S. Alayón, R. Arnay, D. Ángel-Pereira","doi":"10.5566/ias.2346","DOIUrl":"https://doi.org/10.5566/ias.2346","url":null,"abstract":"The first version of the Retinal IMage database for Optic Nerve Evaluation (RIM-ONE) was published in 2011. This was followed by two more, turning it into one of the most cited public retinography databases for evaluating glaucoma. Although it was initially intended to be a database with reference images for segmenting the optic disc, in recent years we have observed that its use has been more oriented toward training and testing deep learning models. The recent REFUGE challenge laid out some criteria that a set of images of these characteristics must satisfy to be used as a standard reference for validating deep learning methods that rely on the use of these data. This, combined with the certain confusion and even improper use observed in some cases of the three versions published, led us to consider revising and combining them into a new, publicly available version called RIM-ONE DL (RIM-ONE for Deep Learning). This paper describes this set of images, consisting of 313 retinographies from normal subjects and 172 retinographies from patients with glaucoma. All of these images have been assessed by two experts and include a manual segmentation of the disc and cup. It also describes an evaluation benchmark with different models of well-known convolutional neural networks.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"81 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83870078","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}

引用次数: 40

Stereology with cylinder probes 圆柱体探头立体学

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2020-11-25 DOI: 10.5566/ias.2433

L. Cruz-Orive, X. Gual-Arnau

引用次数: 0

Automated Evaluation of Contact Angles in a Three-Phase System of Selective Agglomeration in Liquids 液体中选择性团聚三相系统接触角的自动评价

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2020-11-12 DOI: 10.5566/IAS.2403

J. Schreier, O. Furat, M. Cankaya, V. Schmidt, U. Bröckel

{"title":"Automated Evaluation of Contact Angles in a Three-Phase System of Selective Agglomeration in Liquids","authors":"J. Schreier, O. Furat, M. Cankaya, V. Schmidt, U. Bröckel","doi":"10.5566/IAS.2403","DOIUrl":"https://doi.org/10.5566/IAS.2403","url":null,"abstract":"This study aims to an automated evaluation of contact angles in a three-phase system of selective agglomeration in liquids. Wetting properties, quantified by contact angles, are essential in many industries and their processes. Selective agglomeration as a three-phase system consists of a suspension liquid, a heterogeneous solid phase and an immiscible binding liquid. It offers the chance of establishing more efficient separation processes because of the shape-dependent wetting properties of fine particles (size ≤ 10 µm). In the present paper, an experimental setup for contact angle measurements of fine particles based on the Sessile Drop Method is described. Moreover, a new algorithm is discussed, which can be used to automatically compute contact angles from image data captured by a high-speed camera. The algorithm uses a marker-based watershed transform to segment the image data into regions representing the droplet, the carrier plate coated by fine particles, and the background. The main idea is a parametric modelling approach for the time-dependent droplet’s contour by an ellipse. The results show that the development of the dynamic contact angles towards a static contact angle can be efficiently determined based on this novel technique. These findings are useful for a detailed discrimination of wetting properties of spherical and irregularly shaped particles as well as their wetting kinetics. Also, a better understanding of selective agglomeration processes will be promoted by this user-friendly method.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85649000","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}

引用次数: 2

Improving the Cavalieri estimator under non-equidistant sampling and dropouts 改进非等距采样和dropouts下的Cavalieri估计量

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2020-11-04 DOI: 10.5566/IAS.2422

Mads Stehr, M. Kiderlen

{"title":"Improving the Cavalieri estimator under non-equidistant sampling and dropouts","authors":"Mads Stehr, M. Kiderlen","doi":"10.5566/IAS.2422","DOIUrl":"https://doi.org/10.5566/IAS.2422","url":null,"abstract":"Motivated by the stereological problem of volume estimation from parallel section profiles, the so-called Newton-Cotes integral estimators based on random sampling nodes are analyzed. These estimators generalize the classical Cavalieri estimator and its variant for non-equidistant sampling nodes, the generalized Cavalieri estimator, and have typically a substantially smaller variance than the latter. The present paper focuses on the following points in relation to Newton-Cotes estimators: the treatment of dropouts, the construction of variance estimators, and, finally, their application in volume estimation of convex bodies. Dropouts are eliminated points in the initial stationary point process of sampling nodes, modeled by independent thinning. Among other things, exact representations of the variance are given in terms of the thinning probability and increments of the initial points under two practically relevant sampling models. The paper presents a general estimation procedure for the variance of Newton-Cotes estimators based on the sampling nodes in a bounded interval. Finally, the findings are illustrated in an application of volume estimation for three-dimensional convex bodies with sufficiently smooth boundaries.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"17 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88283314","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}

引用次数: 1