摘要
为了提高藻种显微图像识别率,提出了一种基于自适应形态学的快速藻种顶刺分割和特征提取方法。该方法引入像素宽度的概念,以藻种目标像素宽度直方图和面积分布图为判别依据,自动判定最佳结构元尺寸,以联合滤波、区域归并等形态学操作分割顶刺区域;最后构造了两类局部生物形态学特征提取参数。实验证明,该算法针对不同甲藻细胞目标可自动计算最佳结构元素大小,具有精度高、速度快的优点,且两类特征提取参数具有视觉不变性。
A method for spine segmentation based on adaptive mathematical morphology was proposed in order to in- crease the correct rate of Pyrrophyta recognition. At first, the pixel-width was introduced, and the optimal structure ele- ment was computed by pixel-width histogram and area distribution automatically, and then the spine was extracted by mixed mathematical morphology operations. Finally, two kinds of local biological morphology feature parameters were constructed and their visual invariance was proved. The experiments results show that the optimal structure element can be computed by different Pyrrophyta cell and this method is of high precision and fast speed.
出处
《计算机科学》
CSCD
北大核心
2012年第7期262-266,共5页
Computer Science
基金
国家自然科学基金(11171191)
山东省高等学校科研计划项目(J11LG13)资助
关键词
甲藻
顶刺分割
自适应结构元素
Pyrrophyta, Spine segmentation, Adaptive structure element
