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Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method

IET COMPUTER VISION 2018年第7期12卷 989-995页

作者： Vivona, Letizia Cascio, Donato Taormina, Vincenzo Raso, Giuseppe Univ Palermo Dept Chem & Phys Viale Sci Ed 18 Palermo Italy

Autoimmune diseases (ADs) are a collection of many complex disorders of unknown aetiology resulting in immune responses to self-antigens and are thought to result from interactions between genetic and environmental factors. ADs collectively are amongst the most prevalent diseases in the U.S., affecting at least 7% of the population. The diagnosis of ADs is very complex, the standard screening methods provides seeking and recognizing of Antinuclear Antibodies (ANA) by Indirect ImmunoFluorescence (IIF) based on HEp-2 cells. In this paper an automatic system able to identify and classify the Centromere pattern is presented. The method is based on the grouping of centromeres present on the cells through a clustering K-means algorithm. The performances were obtained on two public database of IIF images (A.I.D.A. and MIVIA). Our results showed a sensitivity for image of (90 +/- 5)% and a Accuracy equal to (98.0 +/- 0.5)%. Results demonstrate that the system is able to identify and classify Centromere pattern with accuracy better or comparable with some representative state of the art works. Moreover, it should be noted that for the classification phase the works used for the comparison used an expert-manual segmentation while, in the present work, the segmentation was obtained automatically.

关键词： feature extraction medical disorders diseases blood image classification cellular biophysics biomedical optical imaging fluorescence image segmentation medical image processing image texture standard screening methods antinuclear antibodies ANAs HEp-2 cells fluorescence intensity staining patterns automatic system centromere pattern clustering K-means algorithm databases IIF images public database MIVIA representative state-of-the-art works state-of-the-art techniques classification phase automated approach indirect immunofluorescence images classification unsupervised clustering method autoimmune diseases complex disorders unknown aetiology immune responses self-antigens genetic factors environmental factors prevalent diseases centromere pattern classification

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An unsupervised clustering method for nuclear magnetic resonance transverse relaxation spectrums based on the Gaussian mixture model and its application

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Petroleum Exploration and Development 2022年第2期49卷 339-348页

作者： GE Xinmin XUE Zong’an ZHOU Jun HU Falong LI Jiangtao ZHANG Hengrong WANG Shuolong NIU Shenyuan ZHAO Ji’er School of Geosciences China University of PetroleumQingdao 266580China CNPC Key Well Logging Laboratory Xi’an 710077China Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and TechnologyQingdao 266071China Oil and Gas Survey Center of China Geological Survey Beijing 100083China China Petroleum Logging Co.Ltd. Xi’an 710077China PetroChina Research Institute of Petroleum Exploration&Development Beijing 100083China PetroChina Qinghai Oilfield Company Dunhuang 736202China Zhanjiang Branch of CNOOC Ltd. Zhanjiang 524057China Sinopec Research Institute of Petroleum Engineering Beijing 100101China

To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the Gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.

关键词： NMR T2 spectrum Gaussian mixture model expectation-maximization algorithm Akaike information criterion unsupervised clustering method quantitative pore structure evaluation

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An unsupervised person re-identification approach based on cross-view distribution alignment

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IET IMAGE PROCESSING 2021年第11期15卷 2693-2704页

作者： Jia, Xibin Wang, Xing Mi, Qing Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Municipal Key Lab Multimedia & Intelligen Beijing Peoples R China

unsupervised clustering is a kind of popular solution for unsupervised person re-identification (re-ID). However, due to the influence of cross-view differences, the results of clustering labels are not accurate. To solve this problem, an unsupervised re ID method based on cross-view distributed alignment (CV-DA) to reduce the influence of unsupervised cross-view is proposed. Specifically, based on a popular unsupervised clustering method, density clustering DBSCAN is used to obtain pseudo labels. By calculating the similarity scores of images in the target domain and the source domain, the similarity distribution of different camera views is obtained and is aligned with the distribution with the consistency constraint of pseudo labels. The cross-view distribution alignment constraint is used to guide the clustering process to obtain a more reliable pseudo label. The comprehensive comparative experiments are done in two public datasets, i.e. Market-1501 and DukeMTMC-reID. The comparative results show that the proposed method outperforms several state-of-the-art approaches with mAP reaching 52.6% and rank1 71.1%. In order to prove the effectiveness of the proposed CV-DA, the proposed constraint is added into two advanced re-ID methods. The experimental results demonstrate that the mAP and rank increase by ?0.5-2% after using the cross-view distribution alignment constraint comparing with that of the associated original methods without using CV-DA.

关键词： cross-view distributed alignment pattern clustering clustering labels unsupervised person re-identification approach clustering process pseudolabels cross-view distribution alignment constraint unsupervised re ID method similarity distribution unsupervised cross-view cross-view differences unsupervised clustering method image processing unsupervised learning clustering DBSCAN

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Automatic machine embroidery image color analysis system. Part I: Using Gustafson-Kessel clustering algorithm in embroidery fabric color separation

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TEXTILE RESEARCH JOURNAL 2012年第6期82卷 571-583页

作者： Kuo, Chung-Feng Jeffrey Jian, Bo-Lin Wu, Han-Cheng Peng, Kai-Ching Natl Taiwan Univ Sci & Technol Grad Inst Automat & Control Taipei 106 Taiwan Natl Taiwan Univ Sci & Technol Dept Mat Sci & Engn Taipei 106 Taiwan

This series of studies aims to propose the automatic machine embroidery image color analysis system to solve the problem of lack of manpower for machine embroidery fabric drafting and to shorten drafting time. The studies included three parts: (1) machine embroidery image color separation, (2) search of repeated pattern images, (3) machine embroidery color analysis system integration. This study aimed to find the optimal clustering algorithm and cluster validity indices for the automatic color separation process of machine embroidery fabric drafting in order to shorten drafting time. To improve image quality for computer analysis, this study used the color hybrid median filter to filter noise and the color bilateral filter to smoothen fabric and embroidery texture for subsequent color separation. By extracting the color a* component and b* component of the machine embroidery image in CIE L*a*b* color system, this study used the Gustafson-Kessel clustering algorithm for color separation. The Gustafson-Kessel clustering algorithm in machine embroidery image color separation can improve color separation accuracy, and its result is compared with that of the clustering algorithms commonly used in the color separation of color images. This study implemented the chromatography of the color separation results, and used the cluster validity indices to prove that the application of Gustafson-Kessel clustering algorithm in the machine embroidery image color separation system has better results than K-means, K-medoid, fuzzy C-means (FCM), and self-organizing map (SOM) clustering algorithm. The results meet the classifications as expected by human eyes.

关键词： Gustafson-Kessel unsupervised clustering method color hybrid median filter color bilateral filter K-means K-medoid FCM SOM cluster validity

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Profiling of genes associated with transcriptional responses in mouse hippocampus after transient forebrain ischemia using high-density oligonucleotide DNA array

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MOLECULAR BRAIN RESEARCH 2004年第1-2期121卷 1-11页

作者： Nagata, T Takahashi, Y Sugahara, M Murata, A Nishida, Y Ishikawa, K Asai, S Nihon Univ Sch Med Dept Adv Med Itabashi Ku Tokyo 1738610 Japan Nihon Univ Sch Med Dept Pharmacol Itabashi Ku Tokyo 1738610 Japan

Several cascades of changes in gene expression have been shown to be involved in the neuronal injury after transient cerebral ischemia;however, little is known about the profile of genes showing alteration of expression in a mouse model of transient forebrain ischemia. We analyzed the gene expression profile in the mouse hippocampus during 24 h of reperfusion, after 20 min of transient forebrain ischemia, using a high-density oligonucleotide DNA array. Using statistical filtration (Welch's ANOVA and Welch's t-test), we identified 25 genes with a more than 3.0-fold higher or lower level of expression on average, with statistical significance set at p < 0.05, in at least one ischemia-reperfusion group than in the sham, group. Using unsupervised clustering methods (hierarchical clustering and k-means clustering algorithms), we identified four types of gene expression pattern that may be associated with the response of cell populations in the hippocampus to an ischemic insult in this mouse model. Functional classification of the 25 genes demonstrated alterations of expression of several kinds of biological pathways, regulating transcription (Bhlhb2, Jun, c-fos, Egr1, Egr2, Fosb, Junb, Ifrd1, Neurod6), the cell cycle (c-fos, Fosb, Jun, Junb, Dusp1), stress response (Dusp1, Dnajb1, Dnaja4), chaperone activity (Dnajb1, Dnaja4) and cell death (Ptgs2, Gadd45g, Tdag51), in the mouse hippocampus by 24 h of reperfusion. Using hierarchical clustering analysis, we also found that the same 25 genes clearly discriminated between the sham group and the ischemia-reperfusion groups. The alteration of expression of 25 genes identified in this study suggests the involvement of these genes in the transcriptional response of cell populations in the mouse hippocampus after transient forebrain ischemia. (C) 2003 Elsevier B.V. All rights reserved.

关键词： high-density oligonucleotide DNA array gene expression profile unsupervised clustering method forebrain ischemia hippocampus mouse

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An Analysis of Communities in Different Types of Online Forums

An Analysis of Communities in Different Types of Online Foru...

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International Conference on Advances in Social Network Analysis and Mining (ASONAM)

作者： Morzy, Mikolaj Poznan Univ Tech Inst Comp Sci Piotrowo 2 PL-60965 Poznan Poland

ISBN: (纸本)9780769541389

The most important feature of Internet forums is their social aspect. Many forums are active for a long period of time and attract a group of dedicated users, who build a tight social community around a forum. With great abundance of forums devoted to every possible aspect of human activity, such as politics, religion, sports, technology, entertainment, economy, fashion, and many more, users are able to find a forum that perfectly suits their needs and interests. In this paper we introduce a micro-community-based model for descriptive characterization of Internet forums. We show how a simple concept of a micro-community can be used to quantitatively assess the openness and durability of an Internet forum. We also show that our model is capable of producing a taxonomy of Internet forums using unsupervised clustering method. We present the micro-community model, the set of basic statistics, and we apply the model to several real-world online forums to experimentally verify the correctness and robustness of the model.

关键词： Internet descriptive characterization internet forum microcommunity based model online community pattern clustering social aspect social networking (online) social sciences computing statistical analysis unsupervised clustering method unsupervised learning

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Experimental Studies with a Hybrid Model of unsupervised Neural Networks

Experimental Studies with a Hybrid Model of Unsupervised Neu...

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International Joint Conference on Neural Networks (IJCNN)

作者： Sato, Kazuhito Madokoro, Hirokazu Otani, Toshimitsu Kadowaki, Sakura Akita Prefectural Univ Dept Machine Intelligence & Syst Engn Akita Japan Smart Design Corp Akita Japan

ISBN: (纸本)9781424496365

This paper presents an unsupervised clustering method to classify the optimal number of clusters from a given dataset based solely on the image characteristics. The proposed method contains a feature based on the hybridization of two unsupervised neural networks, Self-Organizing Maps (SOMs) and Fuzzy Adaptive Resonance Theory (ART), which has a seamless mapping procedure comprising the following two steps. First, based on the similarity of the spatial topological structure of images, we will form a local neighborhood region holding the order of topological changes. Then the region is mapped to one-dimensional space equivalent to more than the optimal number of clusters. Furthermore, by additional learning in accordance with the order of the one-dimensional maps formed in the neighborhood region, we must generate suitable labels that match the optimal number of clusters. We use it as a target problem for which the number of categories or clusters is unknown. We emphasize the effectiveness of the proposed method for resolving the target problem for which the number of categories and clusters is unknown, and we anticipate its use for the categorization of facial expression patterns for time-series datasets and for the segmentation of brain tissues shown in Magnetic Resonance (MR) images.

关键词： ART ART neural nets Feature extraction Image coding Image segmentation Neurons SOM Stress Subspace constraints Training biomedical MRI brain tissue segmentation emotion recognition face recognition facial expression pattern categorization fuzzy adaptive resonance theory fuzzy set theory image characteristics image classification image classification image segmentation image spatial topological structure local neighborhood region magnetic resonance images medical image processing pattern clustering self-organising feature maps self-organizing maps time-series datasets unsupervised clustering method unsupervised learning unsupervised neural networks

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