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3 results on '"Huttunen H"'

1. Effects of latanoprost in iris bioidentification.

Author

Lamminen H, Voipio V, Manninen T, and Huttunen H

Subjects
Humans, Latanoprost, Photography, Algorithms, Eye Color drug effects, Pigmentation drug effects, Prostaglandins F, Synthetic adverse effects, Security Measures
Abstract

Purpose: Bioidentification is becoming increasingly important in everyday life. One of the most widespread methods of bioidentification is based on the structure of the iris. Iris photography has several advantages as an identification method: it is relatively simple and effective; it is non-invasive, and it is comparatively inexpensive. However, some medical conditions may change the appearance of the iris. This paper discusses the effects of latanoprost-induced pigmentation changes in iris bioidentification., Methods: The study is based on four extreme cases of latanoprost-induced pigmentation changes. Iris photographs in these patients during treatment are compared with pretreatment photographs. The comparison is carried out with iris recognition software developed by our research group based on the principles of Daugman's well-known IrisCode. The system was evaluated with 595 iris comparisons., Results: Iris photographs showing latanoprost-induced pigmentation changes were correctly matched with pretreatment photographs of the same irises with an error probability similar to that for matching equivalent pairs of photographs in intact eyes., Conclusions: Our results indicate that the pigmentation changes studied do not seem to have a significant effect on the standard identification algorithm.

Published
2009
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2. Computational framework for simulating fluorescence microscope images with cell populations.

Author

Lehmussola A, Ruusuvuori P, Selinummi J, Huttunen H, and Yli-Harja O

Subjects
Computer Simulation, Reproducibility of Results, Sensitivity and Specificity, Software, Algorithms, Cells, Cultured cytology, Image Enhancement methods, Image Interpretation, Computer-Assisted methods, Microscopy, Fluorescence methods, Models, Biological, Software Validation
Abstract

Fluorescence microscopy combined with digital imaging constructs a basic platform for numerous biomedical studies in the field of cellular imaging. As the studies relying on analysis of digital images have become popular, the validation of image processing methods used in automated image cytometry has become an important topic. Especially, the need for efficient validation has arisen from emerging high-throughput microscopy systems where manual validation is impractical. We present a simulation platform for generating synthetic images of fluorescence-stained cell populations with realistic properties. Moreover, we show that the synthetic images enable the validation of analysis methods for automated image cytometry and comparison of their performance. Finally, we suggest additional usage scenarios for the simulator. The presented simulation framework, with several user-controllable parameters, forms a versatile tool for many kinds of validation tasks, and is freely available at http://www.cs.tut.fi/sgn/csb/simcep.

Published
2007
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3. Critical assessment of automated flow cytometry data analysis techniques

Author

Aghaeepour, N, Finak, G, Flowcap, Consortium, Dougall, D, Khodabakhshi, Ah, Mah, P, Obermoser, G, Spidlen, J, Taylor, I, Wuensch, Sa, Bramson, J, Eaves, C, Weng, Ap, Iii, Es, Ho, K, Kollmann, T, Rogers, W, De Rosa, S, Dalal, B, Azad, A, Pothen, A, Brandes, A, Bretschneider, H, Bruggner, R, Finck, R, Jia, R, Zimmerman, N, Linderman, M, Dill, D, Nolan, G, Chan, C, Khettabi, Fe, O'Neill, K, Chikina, M, Ge, Y, Sealfon, S, Sugár, I, Gupta, A, Shooshtari, P, Zare, H, De Jager PL, Jiang, M, Keilwagen, J, Maisog, Jm, Luta, G, Barbo, Aa, Májek, P, Vilček, J, Manninen, T, Huttunen, H, Ruusuvuori, P, Nykter, M, Mclachlan, Gj, Wang, K, Naim, I, Sharma, G, Nikolic, R, Pyne, S, Qian, Y, Qiu, P, Quinn, J, Roth, A, Dream, Consortium, Meyer, P, Stolovitzky, G, Saez Rodriguez, J, Norel, R, Bhattacharjee, M, Biehl, M, Bucher, P, Bunte, K, DI CAMILLO, Barbara, Sambo, Francesco, Sanavia, Tiziana, Trifoglio, Emanuele, Toffolo, GIANNA MARIA, Dimitrieva, S, Dreos, R, Ambrosini, G, Grau, J, Grosse, I, Posch, S, Guex, N, Kursa, M, Rudnicki, W, Liu, B, Maienschein Cline, M, Schneider, P, Seifert, M, Strickert, M, Vilar, Jm, Hoos, H, Mosmann, Tr, Brinkman, R, Gottardo, R, Scheuermann, Rh, Dialogue for Reverse Engineering Assessment and Methods (DREAM) Consortium, Guex, Nicolas, and Abrosini, Giovanna

Subjects
Computer science, Population, Immunology, Graft vs Host Disease, Image processing, Gating, computer.software_genre, Bioinformatics, Biochemistry, Sensitivity and Specificity, Biotechnology, Cell Biology, Molecular Biology, Article, Flow cytometry, 03 medical and health sciences, 0302 clinical medicine, Software, Computational biology and bioinformatics, Immunology, Cancer, Flow cytometry, medicine, Image Processing, Computer-Assisted, Animals, Cluster Analysis, Humans, education, 030304 developmental biology, Cancer, 0303 health sciences, Data processing, education.field_of_study, medicine.diagnostic_test, business.industry, Computational Biology, Reproducibility of Results, Computational biology and bioinformatics, Identification (information), Data Interpretation, Statistical, Data analysis, Leukocytes, Mononuclear, Data mining, Lymphoma, Large B-Cell, Diffuse, business, computer, Algorithms, West Nile Fever, 030215 immunology
Abstract

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

Published
2013

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