舌下赘生物轮廓提取方法初步研究

研究了一种从彩色舌下图像中提取舌下赘生物的方法。根据舌下图像的特点,方法中首先对舌面反光区域检测方法进行改进,提出了针对提取舌下赘生物的反光区域强检测方法,并运用反距离权重插值法对检测出的反光区域进行填充;然后应用自适应直方图均衡化,结合多阈值大津法进行粗分割,得到候选赘生物区域;进而,采用几何测度对不符合舌下赘生物特点的区域进行初步筛选;最后,通过观察赘生物隆起会在其临近区域产生阴影的舌质特征划定最终轮廓。实验结果表明,上述方法能有效地分离目标区域,较为准确地刻画舌下赘生物的轮廓边界。

Defect image segmentation using multilevel thresholding based on firefly algorithm with opposition-learning

To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.