基于形态学及区域合并的分水岭图像分割算法

针对传统分水岭算法在图像分割过程中的过分割问题，提出了基于形态学标记及MSRM的分水岭改进算法。利用形态学混合开闭运算实现图像重建，采用Laplace锐化强化图像边界，其次标记目标物和背景，修正幅值图像，经分水岭变换实现图像的粗分割，然后利用MSRM区域合并法实现目标物和背景的有效分割。对比聚类分割和基于H-minima标记的分水岭算法和Otsu类间最大间距算法，改进后的算法在分割效果、算法运行时间和分割精确度和召回率上具有明显优势，与上述其他算法在平均分割时间上提升3.86 ms，2.88 ms，5.64 ms。分割交并比IOU平均值96.42%，比其他算法平均值高出12.65%，2.77%和3.07%。

Watershed Image Segmentation Algorithm Based on Morphology and Region Merging

Aiming at the problem of over-segmentation of traditional watershed algorithm in image segmentation process,a watershed improvement algorithm based on morphological markers and region merging is proposed.Firstly,the color image is converted into gray image,the image reconstruction is realized by morphological hybrid opening and closing operation,the image boundary is strengthened by Laplace sharpening,the target object and background are marked,the amplitude image is corrected,and the image is coarsely segmented by watershed transformation.Then the MRSM region merging method is used to achieve effective segmentation of the target and background.Compared with clustering segmentation and H-minima-based watershed algorithm and Otsu class maximum spacing algorithm,the improved algorithm has obvious advantages in segmentation effect,algorithm running time,segmentation accuracy and recall rate,and average with other algorithms mentioned above.The split time is increased by 3.86 ms,2.88 ms and 5.64 ms.The split intersection ratio is 96.42%higher than the IOU average,which is 12.65%,2.77%and 3.07%higher than the average of other algorithms.