Classification of iPSC colony images using hierarchical strategies with support vector machines

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Classification of iPSC colony images using hierarchical strategies with support vector machines. / Joutsijoki, Henry; Rasku, Jyrki; Haponen, Markus; Baldin, Ivan; Gizatdinova, Yulia; Paci, Michelangelo; Saarikoski, Jyri; Varpa, Kirsi; Siirtola, Harri; Ávalos-Salguero, Jorge; Iltanen, Kati; Laurikkala, Jorma; Penttinen, Kirsi; Hyttinen, Jari; Aalto-Setälä, Katriina; Juhola, Martti.

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings. The Institute of Electrical and Electronics Engineers, Inc., 2015. p. 86-92 7008152.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Harvard

Joutsijoki, H, Rasku, J, Haponen, M, Baldin, I, Gizatdinova, Y, Paci, M, Saarikoski, J, Varpa, K, Siirtola, H, Ávalos-Salguero, J, Iltanen, K, Laurikkala, J, Penttinen, K, Hyttinen, J, Aalto-Setälä, K & Juhola, M 2015, Classification of iPSC colony images using hierarchical strategies with support vector machines. in IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings., 7008152, The Institute of Electrical and Electronics Engineers, Inc., pp. 86-92, IEEE Symposium on Computational Intelligence and Data Mining, 1/01/00. https://doi.org/10.1109/CIDM.2014.7008152

APA

Joutsijoki, H., Rasku, J., Haponen, M., Baldin, I., Gizatdinova, Y., Paci, M., ... Juhola, M. (2015). Classification of iPSC colony images using hierarchical strategies with support vector machines. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings (pp. 86-92). [7008152] The Institute of Electrical and Electronics Engineers, Inc.. https://doi.org/10.1109/CIDM.2014.7008152

Vancouver

Joutsijoki H, Rasku J, Haponen M, Baldin I, Gizatdinova Y, Paci M et al. Classification of iPSC colony images using hierarchical strategies with support vector machines. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings. The Institute of Electrical and Electronics Engineers, Inc. 2015. p. 86-92. 7008152 https://doi.org/10.1109/CIDM.2014.7008152

Author

Joutsijoki, Henry ; Rasku, Jyrki ; Haponen, Markus ; Baldin, Ivan ; Gizatdinova, Yulia ; Paci, Michelangelo ; Saarikoski, Jyri ; Varpa, Kirsi ; Siirtola, Harri ; Ávalos-Salguero, Jorge ; Iltanen, Kati ; Laurikkala, Jorma ; Penttinen, Kirsi ; Hyttinen, Jari ; Aalto-Setälä, Katriina ; Juhola, Martti. / Classification of iPSC colony images using hierarchical strategies with support vector machines. IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings. The Institute of Electrical and Electronics Engineers, Inc., 2015. pp. 86-92

Bibtex - Download

@inproceedings{d23a628d669b49eabce37248c9cfb644,

title = "Classification of iPSC colony images using hierarchical strategies with support vector machines",

abstract = "In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for safe and patient specific drug therapy without any ethical problems. However, growing of iPSCs is a sensitive process and abnormalities may occur during the growing process. These abnormalities need to be recognized and the problem returns to image classification. We have a collection of 80 iPSC colony images where each one of the images is prelabeled by an expert to class bad, good or semigood. We use intensity histograms as features for classification and we evaluate histograms from the whole image and the colony area only having two datasets. We perform two feature reduction procedures for both datasets. In classification we examine how different hierarchical constructions effect the classification. We perform thorough evaluation and the best accuracy was around 54{\%} obtained with the linear kernel function. Between different hierarchical structures, in many cases there are no significant changes in results. As a result, intensity histograms are a good baseline for the classification of iPSC colony images but more sophisticated feature extraction and reduction methods together with other classification methods need to be researched in future.",

author = "Henry Joutsijoki and Jyrki Rasku and Markus Haponen and Ivan Baldin and Yulia Gizatdinova and Michelangelo Paci and Jyri Saarikoski and Kirsi Varpa and Harri Siirtola and Jorge {\'A}valos-Salguero and Kati Iltanen and Jorma Laurikkala and Kirsi Penttinen and Jari Hyttinen and Katriina Aalto-Set{\"a}l{\"a} and Martti Juhola",

year = "2015",

month = "1",

day = "13",

doi = "10.1109/CIDM.2014.7008152",

language = "English",

isbn = "9781479945191",

pages = "86--92",

booktitle = "IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings",

publisher = "The Institute of Electrical and Electronics Engineers, Inc.",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Classification of iPSC colony images using hierarchical strategies with support vector machines

AU - Joutsijoki, Henry

AU - Rasku, Jyrki

AU - Haponen, Markus

AU - Baldin, Ivan

AU - Gizatdinova, Yulia

AU - Paci, Michelangelo

AU - Saarikoski, Jyri

AU - Varpa, Kirsi

AU - Siirtola, Harri

AU - Ávalos-Salguero, Jorge

AU - Iltanen, Kati

AU - Laurikkala, Jorma

AU - Penttinen, Kirsi

AU - Hyttinen, Jari

AU - Aalto-Setälä, Katriina

AU - Juhola, Martti

PY - 2015/1/13

Y1 - 2015/1/13

N2 - In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for safe and patient specific drug therapy without any ethical problems. However, growing of iPSCs is a sensitive process and abnormalities may occur during the growing process. These abnormalities need to be recognized and the problem returns to image classification. We have a collection of 80 iPSC colony images where each one of the images is prelabeled by an expert to class bad, good or semigood. We use intensity histograms as features for classification and we evaluate histograms from the whole image and the colony area only having two datasets. We perform two feature reduction procedures for both datasets. In classification we examine how different hierarchical constructions effect the classification. We perform thorough evaluation and the best accuracy was around 54% obtained with the linear kernel function. Between different hierarchical structures, in many cases there are no significant changes in results. As a result, intensity histograms are a good baseline for the classification of iPSC colony images but more sophisticated feature extraction and reduction methods together with other classification methods need to be researched in future.

AB - In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for safe and patient specific drug therapy without any ethical problems. However, growing of iPSCs is a sensitive process and abnormalities may occur during the growing process. These abnormalities need to be recognized and the problem returns to image classification. We have a collection of 80 iPSC colony images where each one of the images is prelabeled by an expert to class bad, good or semigood. We use intensity histograms as features for classification and we evaluate histograms from the whole image and the colony area only having two datasets. We perform two feature reduction procedures for both datasets. In classification we examine how different hierarchical constructions effect the classification. We perform thorough evaluation and the best accuracy was around 54% obtained with the linear kernel function. Between different hierarchical structures, in many cases there are no significant changes in results. As a result, intensity histograms are a good baseline for the classification of iPSC colony images but more sophisticated feature extraction and reduction methods together with other classification methods need to be researched in future.

U2 - 10.1109/CIDM.2014.7008152

DO - 10.1109/CIDM.2014.7008152

M3 - Conference contribution

SN - 9781479945191

SP - 86

EP - 92

BT - IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings

PB - The Institute of Electrical and Electronics Engineers, Inc.

ER -

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