| Medical image segmentation based on cellular neural network |
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| 作者姓名： | 姚力 刘佳敏 谢咏圭 |
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| 作者单位： | YAO Li，LIU Jiamin XIE Yonggui& PEI LiuqingDepartment of Electronics，Beijing Normal University，Beijing 100875，China |
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| 基金项目： | the National Natural Science Foundation of China (Grant No. 69772004)，the National Basic Research Program (G1999054000) of China. |
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| 摘 要： | The application of cellular neural network (CNN) has made great progress in image processing. When the selected objects extraction (SOE) CNN is applied to gray scale images, its effects depend on the choice of initial points. In this paper, we take medical images as an example to analyze this limitation. Then an improved algorithm is proposed in which we can segment any gray level objects regardless of the limitation stated above. We also use the gradient information and contour detection CNN to determine the contour and ensure the veracity of segmentation effectively. Finally, we apply the improved algorithm to tumor segmentation of the human brain MR image. The experimental results show that the algorithm is practical and effective.
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| 收稿时间： | 15 August 2000 |
Medical image segmentation based on cellular neural network |
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| YAO Li,LIU Jiamin,Xie Yonggui,PEI Liuqing.Medical image segmentation based on cellular neural network[J].Science in China(Information Sciences),2001,44(1):68-72. |
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| Authors: | YAO Li LIU Jiamin Xie Yonggui PEI Liuqing |
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| Affiliation: | Department of Electronics, Beijing Normal University, |
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| Abstract: | The application of cellular neural network (CNN) has made great progress in image processing. When the selected objects extraction (SOE) CNN is applied to gray scale images, its effects depend on the choice of initial points. In this paper, we take medical images as an example to analyze this limitation. Then an improved algorithm is proposed in which we can segment any gray level objects regardless of the limitation stated above. We also use the gradient information and contour detection CNN to determine the contour and ensure the veracity of segmentation effectively. Finally, we apply the improved algorithm to tumor segmentation of the human brain MR image. The experimental results show that the algorithm is practical and effective. |
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| Keywords: | cellular neural network (CNN) CNN gene template gradient image MRI |
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