Computer Aided Diagnosis System for Pathological Images Using Artificial Organisms of Immuroid
| Project/Area Number |
11680832
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Biomedical engineering/Biological material science
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| Research Institution | Muroran Institute of Technology |
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Principal Investigator |
OKII Hironori Muroran Institute of Technology Technology Associate Professor, 工学部, 助教授 (80224129)
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| Co-Investigator(Kenkyū-buntansha) |
ONO Koichi Muroran Institute of Technology Technology Professor, 工学部, 教授 (60194586)
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| Project Period (FY) |
1999 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| 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)
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| Keywords | breast cancer / medical image diagnosis / histological image / adaptive learning / artificial life |
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| 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.
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Report
(3 results)
Research Products
(3 results)
