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Computer Aided Diagnosis System for Pathological Images Using Artificial Organisms of Immuroid

Research Project

Project/Area Number 11680832
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeSingle-year Grants
Section一般
Research Field Biomedical engineering/Biological material science
Research InstitutionMuroran Institute of Technology

Principal Investigator

OKII Hironori  Muroran Institute of Technology Technology Associate Professor, 工学部, 助教授 (80224129)

Co-Investigator(Kenkyū-buntansha) ONO Koichi  Muroran Institute of Technology Technology Professor, 工学部, 教授 (60194586)
Project Period (FY) 1999 – 2000
Project Status Completed (Fiscal Year 2000)
Budget Amount *help
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2000: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1999: ¥2,500,000 (Direct Cost: ¥2,500,000)
Keywordsbreast cancer / medical image diagnosis / histological image / adaptive learning / artificial life
Research Abstract

SUMMARY A new Computer-Aided Diagnosis method, which can classify hematoxylin and eosin (HE)-stained breast tumor images into benign or malignant using the adaptive searching ability of artificial organisms, is developed. 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.

Report

(3 results)
  • 2000 Annual Research Report   Final Research Report Summary
  • 1999 Annual Research Report
  • Research Products

    (3 results)

All Other

All Publications (3 results)

  • [Publications] H.Oku,T.Uozumi,K.Ono and Y.Fujisawa: "Feature Extraction for Classification of Breast Tumor Images Using Artificial Organisms"IEICe Trans.Inf.& Syst.. Vol.E84-D No.3. 403-414 (2001)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      2000 Final Research Report Summary
  • [Publications] H.OKII, T.UOZUMI, K.ONO and Y.FUJISAWA: "Feature Extraction for Classification of Breast Tumor Images Using Artificial Organisms"IEICE TRANS.INF.& SYST. Vol.E84-D, No.3, March. 403-414 (2001)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      2000 Final Research Report Summary
  • [Publications] H.Ohii,T.Uozumi,K.Ono and Y.Fujisawa: "Feature Extraction for Classification of Breast Tumor Images Using Artificial Organisms"IEICE Trans.Int.&Syst.. Vol.E84-D No.3. 403-414 (2001)

    • Related Report
      2000 Annual Research Report

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Published: 1999-04-01   Modified: 2016-04-21  

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