Detection of tumor tissue based on the multispectral imaging

Adam Świtoński; Marcin Michalak; Henryk Josiński; Konrad Wojciechowski

doi:10.1007/978-3-642-15907-7_40

Detection of tumor tissue based on the multispectral imaging

Polish-Japanese Academy of Information Technology
Central Mining Institute

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

6 Citations (Scopus)

Abstract

We have prepared multispectral image database of skin tumor diagnosis. All images have been labeled with two classes - tumor and healthy tissues. We have extracted pixel signatures with their spectral data and class assigning, thus obtained train dataset. Next we have used and evaluated the supervised learning techniques for the purpose of automatic tumor detection. We have tested Naive Bayes, KNN, Multilayer Perceptron, LibSVM, LibLinear, RBFNetwork, ConjuctiveRule, DecisionTable and PART classifiers. We have obtained results on the level of 99% classifier efficiency. We have visualized classification for example images by coloring class regions and verified if they overlap with labeled regions.

Original language	English
Title of host publication	Computer Vision and Graphics - International Conference, ICCVG 2010, Proceedings
Pages	325-333
Number of pages	9
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-15907-7_40
Publication status	Published - 2010
Event	International Conference on Computer Vision and Graphics, ICCVG 2010 - Warsaw, Poland Duration: 20 Sept 2010 → 22 Sept 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6375 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Computer Vision and Graphics, ICCVG 2010
Country/Territory	Poland
City	Warsaw
Period	20/09/10 → 22/09/10

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-15907-7_40

Cite this

Świtoński, A., Michalak, M., Josiński, H., & Wojciechowski, K. (2010). Detection of tumor tissue based on the multispectral imaging. In Computer Vision and Graphics - International Conference, ICCVG 2010, Proceedings (PART 2 ed., pp. 325-333). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6375 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-15907-7_40

Świtoński, Adam ; Michalak, Marcin ; Josiński, Henryk et al. / Detection of tumor tissue based on the multispectral imaging. Computer Vision and Graphics - International Conference, ICCVG 2010, Proceedings. PART 2. ed. 2010. pp. 325-333 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{21912ffa6a6b4bf68cc50fd704f61647,

title = "Detection of tumor tissue based on the multispectral imaging",

abstract = "We have prepared multispectral image database of skin tumor diagnosis. All images have been labeled with two classes - tumor and healthy tissues. We have extracted pixel signatures with their spectral data and class assigning, thus obtained train dataset. Next we have used and evaluated the supervised learning techniques for the purpose of automatic tumor detection. We have tested Naive Bayes, KNN, Multilayer Perceptron, LibSVM, LibLinear, RBFNetwork, ConjuctiveRule, DecisionTable and PART classifiers. We have obtained results on the level of 99\% classifier efficiency. We have visualized classification for example images by coloring class regions and verified if they overlap with labeled regions.",

author = "Adam {\'S}wito{\'n}ski and Marcin Michalak and Henryk Josi{\'n}ski and Konrad Wojciechowski",

year = "2010",

doi = "10.1007/978-3-642-15907-7\_40",

language = "English",

isbn = "3642159060",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "325--333",

booktitle = "Computer Vision and Graphics - International Conference, ICCVG 2010, Proceedings",

edition = "PART 2",

note = "International Conference on Computer Vision and Graphics, ICCVG 2010 ; Conference date: 20-09-2010 Through 22-09-2010",

}

Świtoński, A , Michalak, M , Josiński, H & Wojciechowski, K 2010, Detection of tumor tissue based on the multispectral imaging. in Computer Vision and Graphics - International Conference, ICCVG 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6375 LNCS, pp. 325-333, International Conference on Computer Vision and Graphics, ICCVG 2010, Warsaw, Poland, 20/09/10. https://doi.org/10.1007/978-3-642-15907-7_40

Detection of tumor tissue based on the multispectral imaging. / Świtoński, Adam ; Michalak, Marcin ; Josiński, Henryk et al.
Computer Vision and Graphics - International Conference, ICCVG 2010, Proceedings. PART 2. ed. 2010. p. 325-333 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6375 LNCS, No. PART 2).

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

TY - GEN

T1 - Detection of tumor tissue based on the multispectral imaging

AU - Świtoński, Adam

AU - Michalak, Marcin

AU - Josiński, Henryk

AU - Wojciechowski, Konrad

PY - 2010

Y1 - 2010

N2 - We have prepared multispectral image database of skin tumor diagnosis. All images have been labeled with two classes - tumor and healthy tissues. We have extracted pixel signatures with their spectral data and class assigning, thus obtained train dataset. Next we have used and evaluated the supervised learning techniques for the purpose of automatic tumor detection. We have tested Naive Bayes, KNN, Multilayer Perceptron, LibSVM, LibLinear, RBFNetwork, ConjuctiveRule, DecisionTable and PART classifiers. We have obtained results on the level of 99% classifier efficiency. We have visualized classification for example images by coloring class regions and verified if they overlap with labeled regions.

AB - We have prepared multispectral image database of skin tumor diagnosis. All images have been labeled with two classes - tumor and healthy tissues. We have extracted pixel signatures with their spectral data and class assigning, thus obtained train dataset. Next we have used and evaluated the supervised learning techniques for the purpose of automatic tumor detection. We have tested Naive Bayes, KNN, Multilayer Perceptron, LibSVM, LibLinear, RBFNetwork, ConjuctiveRule, DecisionTable and PART classifiers. We have obtained results on the level of 99% classifier efficiency. We have visualized classification for example images by coloring class regions and verified if they overlap with labeled regions.

UR - https://www.scopus.com/pages/publications/78049319385

U2 - 10.1007/978-3-642-15907-7_40

DO - 10.1007/978-3-642-15907-7_40

M3 - Conference contribution

AN - SCOPUS:78049319385

SN - 3642159060

SN - 9783642159060

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 325

EP - 333

BT - Computer Vision and Graphics - International Conference, ICCVG 2010, Proceedings

T2 - International Conference on Computer Vision and Graphics, ICCVG 2010

Y2 - 20 September 2010 through 22 September 2010

ER -

Świtoński A , Michalak M , Josiński H, Wojciechowski K. Detection of tumor tissue based on the multispectral imaging. In Computer Vision and Graphics - International Conference, ICCVG 2010, Proceedings. PART 2 ed. 2010. p. 325-333. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-15907-7_40

Detection of tumor tissue based on the multispectral imaging

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