Automated extraction and diagnosis of lung emphysema from lung CT images using artificial neural network

Tan Kok Liang, Toshiyuki Tanaka, Hidetoshi Nakamura, Akitoshi Ishizaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Emphysema is characterized by loss of elasticity of the lung tissue; destruction of structures supporting the alveoli; the destruction of capillaries feeding the alveoli [1]. The result is that the small airways collapse during expiration, leading to an obstructive form of lung disease (air is trapped in the lungs in obstructive lung diseases). The scientific definition of emphysema is: "Permanent destructive enlargement of the airspaces distal to the terminal bronchioles without obvious fibrosis". Hence, the definite diagnosis is made by a pathologist [1]. At present, diagnosis of emphysema is done by using spirometry, X-rays, spiral chest CT-scan, bronchoscopy, blood tests, pulse oximetry and arterial blood gas sampling. Although emphysema is an irreversible degenerative condition, early prognosis and treatment are very important for optimizing the patients' quality of life. This paper proposes an automated computed-aided diagnosis algorithm for extracting enlarged airways from lung CT image automatically using an image matching method, and consequently classifying the lung condition artificial neural network (ANN) by supplying 30 network inputs obtained from texture analysis of the lung CT image and calculations of the feature properties of extracted enlarged airways to the trained ANN. Our research aims to produce an automated system which has higher objectivity in the diagnosis of lung emphysema.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Pages2306-2311
Number of pages6
DOIs
Publication statusPublished - 2006
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: 2006 Oct 182006 Oct 21

Other

Other2006 SICE-ICASE International Joint Conference
CountryKorea, Republic of
CityBusan
Period06/10/1806/10/21

Fingerprint

Neural networks
Pulmonary diseases
Blood
Image matching
Computerized tomography
Elasticity
Textures
Tissue
Sampling
X rays
Air
Gases

Keywords

  • Artificial neural network
  • Automated extraction and diagnosis
  • Image matching method
  • Lung CT images

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Liang, T. K., Tanaka, T., Nakamura, H., & Ishizaka, A. (2006). Automated extraction and diagnosis of lung emphysema from lung CT images using artificial neural network. In 2006 SICE-ICASE International Joint Conference (pp. 2306-2311). [4109074] https://doi.org/10.1109/SICE.2006.315359

Automated extraction and diagnosis of lung emphysema from lung CT images using artificial neural network. / Liang, Tan Kok; Tanaka, Toshiyuki; Nakamura, Hidetoshi; Ishizaka, Akitoshi.

2006 SICE-ICASE International Joint Conference. 2006. p. 2306-2311 4109074.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liang, TK, Tanaka, T, Nakamura, H & Ishizaka, A 2006, Automated extraction and diagnosis of lung emphysema from lung CT images using artificial neural network. in 2006 SICE-ICASE International Joint Conference., 4109074, pp. 2306-2311, 2006 SICE-ICASE International Joint Conference, Busan, Korea, Republic of, 06/10/18. https://doi.org/10.1109/SICE.2006.315359
Liang TK, Tanaka T, Nakamura H, Ishizaka A. Automated extraction and diagnosis of lung emphysema from lung CT images using artificial neural network. In 2006 SICE-ICASE International Joint Conference. 2006. p. 2306-2311. 4109074 https://doi.org/10.1109/SICE.2006.315359
Liang, Tan Kok ; Tanaka, Toshiyuki ; Nakamura, Hidetoshi ; Ishizaka, Akitoshi. / Automated extraction and diagnosis of lung emphysema from lung CT images using artificial neural network. 2006 SICE-ICASE International Joint Conference. 2006. pp. 2306-2311
@inproceedings{6191ad4667af4bcb96c108df595f3523,
title = "Automated extraction and diagnosis of lung emphysema from lung CT images using artificial neural network",
abstract = "Emphysema is characterized by loss of elasticity of the lung tissue; destruction of structures supporting the alveoli; the destruction of capillaries feeding the alveoli [1]. The result is that the small airways collapse during expiration, leading to an obstructive form of lung disease (air is trapped in the lungs in obstructive lung diseases). The scientific definition of emphysema is: {"}Permanent destructive enlargement of the airspaces distal to the terminal bronchioles without obvious fibrosis{"}. Hence, the definite diagnosis is made by a pathologist [1]. At present, diagnosis of emphysema is done by using spirometry, X-rays, spiral chest CT-scan, bronchoscopy, blood tests, pulse oximetry and arterial blood gas sampling. Although emphysema is an irreversible degenerative condition, early prognosis and treatment are very important for optimizing the patients' quality of life. This paper proposes an automated computed-aided diagnosis algorithm for extracting enlarged airways from lung CT image automatically using an image matching method, and consequently classifying the lung condition artificial neural network (ANN) by supplying 30 network inputs obtained from texture analysis of the lung CT image and calculations of the feature properties of extracted enlarged airways to the trained ANN. Our research aims to produce an automated system which has higher objectivity in the diagnosis of lung emphysema.",
keywords = "Artificial neural network, Automated extraction and diagnosis, Image matching method, Lung CT images",
author = "Liang, {Tan Kok} and Toshiyuki Tanaka and Hidetoshi Nakamura and Akitoshi Ishizaka",
year = "2006",
doi = "10.1109/SICE.2006.315359",
language = "English",
isbn = "8995003855",
pages = "2306--2311",
booktitle = "2006 SICE-ICASE International Joint Conference",

}

TY - GEN

T1 - Automated extraction and diagnosis of lung emphysema from lung CT images using artificial neural network

AU - Liang, Tan Kok

AU - Tanaka, Toshiyuki

AU - Nakamura, Hidetoshi

AU - Ishizaka, Akitoshi

PY - 2006

Y1 - 2006

N2 - Emphysema is characterized by loss of elasticity of the lung tissue; destruction of structures supporting the alveoli; the destruction of capillaries feeding the alveoli [1]. The result is that the small airways collapse during expiration, leading to an obstructive form of lung disease (air is trapped in the lungs in obstructive lung diseases). The scientific definition of emphysema is: "Permanent destructive enlargement of the airspaces distal to the terminal bronchioles without obvious fibrosis". Hence, the definite diagnosis is made by a pathologist [1]. At present, diagnosis of emphysema is done by using spirometry, X-rays, spiral chest CT-scan, bronchoscopy, blood tests, pulse oximetry and arterial blood gas sampling. Although emphysema is an irreversible degenerative condition, early prognosis and treatment are very important for optimizing the patients' quality of life. This paper proposes an automated computed-aided diagnosis algorithm for extracting enlarged airways from lung CT image automatically using an image matching method, and consequently classifying the lung condition artificial neural network (ANN) by supplying 30 network inputs obtained from texture analysis of the lung CT image and calculations of the feature properties of extracted enlarged airways to the trained ANN. Our research aims to produce an automated system which has higher objectivity in the diagnosis of lung emphysema.

AB - Emphysema is characterized by loss of elasticity of the lung tissue; destruction of structures supporting the alveoli; the destruction of capillaries feeding the alveoli [1]. The result is that the small airways collapse during expiration, leading to an obstructive form of lung disease (air is trapped in the lungs in obstructive lung diseases). The scientific definition of emphysema is: "Permanent destructive enlargement of the airspaces distal to the terminal bronchioles without obvious fibrosis". Hence, the definite diagnosis is made by a pathologist [1]. At present, diagnosis of emphysema is done by using spirometry, X-rays, spiral chest CT-scan, bronchoscopy, blood tests, pulse oximetry and arterial blood gas sampling. Although emphysema is an irreversible degenerative condition, early prognosis and treatment are very important for optimizing the patients' quality of life. This paper proposes an automated computed-aided diagnosis algorithm for extracting enlarged airways from lung CT image automatically using an image matching method, and consequently classifying the lung condition artificial neural network (ANN) by supplying 30 network inputs obtained from texture analysis of the lung CT image and calculations of the feature properties of extracted enlarged airways to the trained ANN. Our research aims to produce an automated system which has higher objectivity in the diagnosis of lung emphysema.

KW - Artificial neural network

KW - Automated extraction and diagnosis

KW - Image matching method

KW - Lung CT images

UR - http://www.scopus.com/inward/record.url?scp=34250785032&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34250785032&partnerID=8YFLogxK

U2 - 10.1109/SICE.2006.315359

DO - 10.1109/SICE.2006.315359

M3 - Conference contribution

SN - 8995003855

SN - 9788995003855

SP - 2306

EP - 2311

BT - 2006 SICE-ICASE International Joint Conference

ER -