Feature Extraction for Classification of Breast Tumor Images Using Artificial Organisms

Hironori OKII; Takashi UOZUMI; Koichi ONO; Yasunori FUJISAWA

Feature Extraction for Classification of Breast Tumor Images Using Artificial Organisms

Hironori OKII, Takashi UOZUMI, Koichi ONO, Yasunori FUJISAWA

Full Text Views

0

Cite this

Summary :

This paper describes a method for classification of hematoxylin and eosin (HE)-stained breast tumor images into benign or malignant using the adaptive searching ability of artificial organisms. Each artificial organism has some attributes, such as, age, internal energy and coordinates. In addition, the artificial organism has a differentiation function for evaluating "malignant" or "benign" tumors and the adaptive behaviors of each artificial organism are evaluated using five kinds of texture features. The texture feature of nuclei regions in normal mammary glands and that of carcinoma regions in malignant tumors are treated as "self" and "non-self," respectively. This model consists of two stages of operations for detecting tumor regions, the learning and searching stages. At the learning stage, the nuclei regions are roughly detected and classified into benign or malignant tumors. At the searching stage, the similarity of each organism's environment is investigated before and after the movement for detecting breast tumor regions precisely. The method developed was applied to 21 cases of test images and the distinction between malignant and benign tumors by the artificial organisms was successful in all cases. The proposed method has the following advantages: the texture feature values for the evaluation of tumor regions at the searching stage are decided automatically during the learning stage in every input image. Evaluation of the environment, whether the target pixel is a malignant tumor or not, is performed based on the angular difference in each texture feature. Therefore, this model can successfully detect tumor regions and classify the type of tumors correctly without affecting a wide variety of breast tumor images, which depends on the tissue condition and the degree of malignancy in each breast tumor case.

Publication: IEICE TRANSACTIONS on Information Vol.E84-D No.3 pp.403-414

Publication Date: 2001/03/01

Publicized

Online ISSN

DOI

Type of Manuscript: PAPER

Category: Medical Engineering

Cite this

Copy

Hironori OKII, Takashi UOZUMI, Koichi ONO, Yasunori FUJISAWA, "Feature Extraction for Classification of Breast Tumor Images Using Artificial Organisms" in IEICE TRANSACTIONS on Information, vol. E84-D, no. 3, pp. 403-414, March 2001, doi: .
Abstract: This paper describes a method for classification of hematoxylin and eosin (HE)-stained breast tumor images into benign or malignant using the adaptive searching ability of artificial organisms. Each artificial organism has some attributes, such as, age, internal energy and coordinates. In addition, the artificial organism has a differentiation function for evaluating "malignant" or "benign" tumors and the adaptive behaviors of each artificial organism are evaluated using five kinds of texture features. The texture feature of nuclei regions in normal mammary glands and that of carcinoma regions in malignant tumors are treated as "self" and "non-self," respectively. This model consists of two stages of operations for detecting tumor regions, the learning and searching stages. At the learning stage, the nuclei regions are roughly detected and classified into benign or malignant tumors. At the searching stage, the similarity of each organism's environment is investigated before and after the movement for detecting breast tumor regions precisely. The method developed was applied to 21 cases of test images and the distinction between malignant and benign tumors by the artificial organisms was successful in all cases. The proposed method has the following advantages: the texture feature values for the evaluation of tumor regions at the searching stage are decided automatically during the learning stage in every input image. Evaluation of the environment, whether the target pixel is a malignant tumor or not, is performed based on the angular difference in each texture feature. Therefore, this model can successfully detect tumor regions and classify the type of tumors correctly without affecting a wide variety of breast tumor images, which depends on the tissue condition and the degree of malignancy in each breast tumor case.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_3_403/_p

Copy

@ARTICLE{e84-d_3_403,
author={Hironori OKII, Takashi UOZUMI, Koichi ONO, Yasunori FUJISAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Feature Extraction for Classification of Breast Tumor Images Using Artificial Organisms},
year={2001},
volume={E84-D},
number={3},
pages={403-414},
abstract={This paper describes a method for classification of hematoxylin and eosin (HE)-stained breast tumor images into benign or malignant using the adaptive searching ability of artificial organisms. Each artificial organism has some attributes, such as, age, internal energy and coordinates. In addition, the artificial organism has a differentiation function for evaluating "malignant" or "benign" tumors and the adaptive behaviors of each artificial organism are evaluated using five kinds of texture features. The texture feature of nuclei regions in normal mammary glands and that of carcinoma regions in malignant tumors are treated as "self" and "non-self," respectively. This model consists of two stages of operations for detecting tumor regions, the learning and searching stages. At the learning stage, the nuclei regions are roughly detected and classified into benign or malignant tumors. At the searching stage, the similarity of each organism's environment is investigated before and after the movement for detecting breast tumor regions precisely. The method developed was applied to 21 cases of test images and the distinction between malignant and benign tumors by the artificial organisms was successful in all cases. The proposed method has the following advantages: the texture feature values for the evaluation of tumor regions at the searching stage are decided automatically during the learning stage in every input image. Evaluation of the environment, whether the target pixel is a malignant tumor or not, is performed based on the angular difference in each texture feature. Therefore, this model can successfully detect tumor regions and classify the type of tumors correctly without affecting a wide variety of breast tumor images, which depends on the tissue condition and the degree of malignancy in each breast tumor case.},
keywords={},
doi={},
ISSN={},
month={March},}

Copy

TY - JOUR
TI - Feature Extraction for Classification of Breast Tumor Images Using Artificial Organisms
T2 - IEICE TRANSACTIONS on Information
SP - 403
EP - 414
AU - Hironori OKII
AU - Takashi UOZUMI
AU - Koichi ONO
AU - Yasunori FUJISAWA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2001
AB - This paper describes a method for classification of hematoxylin and eosin (HE)-stained breast tumor images into benign or malignant using the adaptive searching ability of artificial organisms. Each artificial organism has some attributes, such as, age, internal energy and coordinates. In addition, the artificial organism has a differentiation function for evaluating "malignant" or "benign" tumors and the adaptive behaviors of each artificial organism are evaluated using five kinds of texture features. The texture feature of nuclei regions in normal mammary glands and that of carcinoma regions in malignant tumors are treated as "self" and "non-self," respectively. This model consists of two stages of operations for detecting tumor regions, the learning and searching stages. At the learning stage, the nuclei regions are roughly detected and classified into benign or malignant tumors. At the searching stage, the similarity of each organism's environment is investigated before and after the movement for detecting breast tumor regions precisely. The method developed was applied to 21 cases of test images and the distinction between malignant and benign tumors by the artificial organisms was successful in all cases. The proposed method has the following advantages: the texture feature values for the evaluation of tumor regions at the searching stage are decided automatically during the learning stage in every input image. Evaluation of the environment, whether the target pixel is a malignant tumor or not, is performed based on the angular difference in each texture feature. Therefore, this model can successfully detect tumor regions and classify the type of tumors correctly without affecting a wide variety of breast tumor images, which depends on the tissue condition and the degree of malignancy in each breast tumor case.
ER -