激光杂志
激光雜誌
격광잡지
LASER JOURNAL
2015年
1期
53-56
,共4页
医学图像%分割算法%自组映射网络%小波包分解
醫學圖像%分割算法%自組映射網絡%小波包分解
의학도상%분할산법%자조영사망락%소파포분해
medical image%segmentation algorithm%self-organizing feature maps%wavelet packet decomposition
为了提高医学图像分割精度，提出一种改进自组织特征网络的医学图像分割算法。首先采用小波包分解提取医学图像的特征，然后改进自组织特征网络建立医学图像分类器，实现医学图像分割，最后采用仿真实验测试算法的性能。仿真结果表明，本文算法不仅解决了传统医学图像分割算法存在的缺陷，提高医学图像分割的精度，具有较好的实际应用价值。
為瞭提高醫學圖像分割精度，提齣一種改進自組織特徵網絡的醫學圖像分割算法。首先採用小波包分解提取醫學圖像的特徵，然後改進自組織特徵網絡建立醫學圖像分類器，實現醫學圖像分割，最後採用倣真實驗測試算法的性能。倣真結果錶明，本文算法不僅解決瞭傳統醫學圖像分割算法存在的缺陷，提高醫學圖像分割的精度，具有較好的實際應用價值。
위료제고의학도상분할정도，제출일충개진자조직특정망락적의학도상분할산법。수선채용소파포분해제취의학도상적특정，연후개진자조직특정망락건립의학도상분류기，실현의학도상분할，최후채용방진실험측시산법적성능。방진결과표명，본문산법불부해결료전통의학도상분할산법존재적결함，제고의학도상분할적정도，구유교호적실제응용개치。
In order to improve the segmentation accuracy of medical image segmentation algorithm, a medical im-age segmentation algorithm is proposed based on improved self organizing feature mapping network for medical image. Firstly, wavelet packet decomposition is used to extract features of medical images, and then the improved self organi-zing feature network algorithm is used to establish medical image classifier to segment medical image, finally the simu-lation experiment is carried out to test the performance of algorithm. The simulation results show that the proposed algo-rithm not only solves the defects existing in traditional segmentation algorithms for medical image, improve the segmen-tation accuracy of medical image, and has good practical value.
