Automatic Detection of Nuclei Regions from HE-Stained Breast Tumor Images Using Artificial Organisms

Hironori OKII  Takashi UOZUMI  Koichi ONO  Yasunori FUJISAWA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E81-D   No.4   pp.401-410
Publication Date: 1998/04/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Medical Electronics and Medical Information
Keyword: 
breast carcinoma,  medical image diagnosis,  histological diagnosis,  stained image,  artificial life,  genetic algorithm,  

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Summary: 
This paper describes an automatic region segmentation method which is detectable nuclei regions from hematoxylin and eosin (HE)-stained breast tumor images using artificial organisms. In this model, the stained images are treated as virtual environments which consist of nuclei, interstitial tissue and background regions. The movement characteristics of each organism are controlled by the gene and the adaptive behavior of each organism is evaluated by calculating the similarities of the texture features before and after the movement. In the nuclei regions, the artificial organisms can survive, obtain energy and produce offspring. Organisms in other regions lose energy by the movement and die during searching. As a result, nuclei regions are detected by the collective behavior of artificial organisms. The method developed was applied to 9 cases of breast tumor images and detection of nuclei regions by the artificial organisms was successful in all cases. The proposed method has the following advantages: (1) the criteria of each organism's texture feature values (supervised values) for the evaluation of nuclei regions are decided automatically at the learning stage in every input image; (2) the proposed algorithm requires only the similarity ratio as the threshold value when each organism evaluates the environment; (3) this model can successfully detect the nuclei regions without affecting the variance of color tones in stained images which depends on the tissue condition and the degree of malignancy in each breast tumor case.