Study of feature extraction for diagnosing prostate cancer

Yoshiteru Toki; Toshiyuki Tanaka

Study of feature extraction for diagnosing prostate cancer

Yoshiteru Toki, Toshiyuki Tanaka

Department of Applied Physics and Physico-Informatics

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

1 Citation (Scopus)

Abstract

As the number of patient with prostate cancer increases, the computer aided diagnosis support for prostate cancer become important. Traditional computer-based method checks the detailed texture of the image, but the proposed method focuses on area and relation of position from a broader field of view approximating the way of diagnosis is made by pathologist. As a result, average of the glands' inner area seems significant for classifying Gleason Grade.

Original language	English
Title of host publication	Proceedings of SICE Annual Conference 2010, SICE 2010 - Final Program and Papers
Publisher	Society of Instrument and Control Engineers (SICE)
Pages	1039-1041
Number of pages	3
ISBN (Print)	9784907764364
Publication status	Published - 2010 Jan 1

Publication series

Name	Proceedings of the SICE Annual Conference

Keywords

Digital pathology
Feature extraction
Image processing
Prostate cancer

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

Study of feature extraction for diagnosing prostate cancer. / Toki, Yoshiteru; Tanaka, Toshiyuki.

Proceedings of SICE Annual Conference 2010, SICE 2010 - Final Program and Papers. Society of Instrument and Control Engineers (SICE), 2010. p. 1039-1041 5602884 (Proceedings of the SICE Annual Conference).

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

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AB - As the number of patient with prostate cancer increases, the computer aided diagnosis support for prostate cancer become important. Traditional computer-based method checks the detailed texture of the image, but the proposed method focuses on area and relation of position from a broader field of view approximating the way of diagnosis is made by pathologist. As a result, average of the glands' inner area seems significant for classifying Gleason Grade.

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ER -

Study of feature extraction for diagnosing prostate cancer

Abstract

Publication series

Keywords

ASJC Scopus subject areas

UN SDGs

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Cite this