Automatic malignancy classification of colon tumor by feature analysis of biopsy images

Takatoshi Karino; Toshiyuki Tanaka

Automatic malignancy classification of colon tumor by feature analysis of biopsy images

Department of Applied Physics and Physico-Informatics

Research output: Contribution to conference › Paper › peer-review

Abstract

We intend to construct a system to automatically diagnose malignant potential of colon biopsies. First, we classified biopsies components (nuclei, cytoplasm and interstitium) by discriminant analysis method with Mahalanobis's generalized distance and Otsu method twice. Furthermore, only ductal nuclei were sorted out from the other nuclei by using differences of areas and densities of edges, that is high in lymphoid follicles. We proposed a method for quantifying pseudostratified nuclei by a measure of thickness of ductal nuclei and methods for quantifying structural atypia by fractal dimension analysis with Box-counting method and HLAC features. Moreover, poorly and moderately differentiated carcinoma in Group5 was able to be discriminated with high accuracy by density of labeled ranges. With these features, biopsies' images were classified into 4 Groups by support vector machine.

Original language	English
Pages	1820-1824
Number of pages	5
Publication status	Published - 2013 Jan 1
Event	2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan Duration: 2013 Sept 14 → 2013 Sept 17

Other

Other	2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013
Country/Territory	Japan
City	Nagoya
Period	13/9/14 → 13/9/17

Keywords

Biopsy test
Cancer
Colon
Image processing

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "Automatic malignancy classification of colon tumor by feature analysis of biopsy images",

abstract = "We intend to construct a system to automatically diagnose malignant potential of colon biopsies. First, we classified biopsies components (nuclei, cytoplasm and interstitium) by discriminant analysis method with Mahalanobis's generalized distance and Otsu method twice. Furthermore, only ductal nuclei were sorted out from the other nuclei by using differences of areas and densities of edges, that is high in lymphoid follicles. We proposed a method for quantifying pseudostratified nuclei by a measure of thickness of ductal nuclei and methods for quantifying structural atypia by fractal dimension analysis with Box-counting method and HLAC features. Moreover, poorly and moderately differentiated carcinoma in Group5 was able to be discriminated with high accuracy by density of labeled ranges. With these features, biopsies' images were classified into 4 Groups by support vector machine.",

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T1 - Automatic malignancy classification of colon tumor by feature analysis of biopsy images

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AU - Tanaka, Toshiyuki

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N2 - We intend to construct a system to automatically diagnose malignant potential of colon biopsies. First, we classified biopsies components (nuclei, cytoplasm and interstitium) by discriminant analysis method with Mahalanobis's generalized distance and Otsu method twice. Furthermore, only ductal nuclei were sorted out from the other nuclei by using differences of areas and densities of edges, that is high in lymphoid follicles. We proposed a method for quantifying pseudostratified nuclei by a measure of thickness of ductal nuclei and methods for quantifying structural atypia by fractal dimension analysis with Box-counting method and HLAC features. Moreover, poorly and moderately differentiated carcinoma in Group5 was able to be discriminated with high accuracy by density of labeled ranges. With these features, biopsies' images were classified into 4 Groups by support vector machine.

AB - We intend to construct a system to automatically diagnose malignant potential of colon biopsies. First, we classified biopsies components (nuclei, cytoplasm and interstitium) by discriminant analysis method with Mahalanobis's generalized distance and Otsu method twice. Furthermore, only ductal nuclei were sorted out from the other nuclei by using differences of areas and densities of edges, that is high in lymphoid follicles. We proposed a method for quantifying pseudostratified nuclei by a measure of thickness of ductal nuclei and methods for quantifying structural atypia by fractal dimension analysis with Box-counting method and HLAC features. Moreover, poorly and moderately differentiated carcinoma in Group5 was able to be discriminated with high accuracy by density of labeled ranges. With these features, biopsies' images were classified into 4 Groups by support vector machine.

KW - Biopsy test

KW - Cancer

KW - Colon

KW - Image processing

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Y2 - 14 September 2013 through 17 September 2013

ER -

Automatic malignancy classification of colon tumor by feature analysis of biopsy images

Abstract

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Keywords

ASJC Scopus subject areas

UN SDGs

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