关键词: 图像分割, 分水岭算法, 自适应遗传算法, 各向异性扩散, 小波自适应增强

Abstract: An improved morphological watershed algorithm is presented to solve the
over-segmentation of watershed and keep the image detail. First, the image is decomposed by
multi-scale wavelet into high-frequency and low-frequency coefficients. The low frequency
coefficient of the image is filtered by Anisotropic diffusion filter algorithm based on Perona-Malik
diffusion model. The high frequency coefficient of the image is enhanced and denoised by using
the improved adaptive Genetic Algorithm, which is gotten by introducing the sigmoid function in
neural network to modify the generation of the mutation and crossover probability, and by
replacing the worst individual of the children with the best individual of the father generation to
protect the best individual from being destroyed, and to overcome the local optimal phenomenon
of genetic algorithm. Then, the gradient image is sharpened to highlight the edge, the watershed
algorithm is applied after conducting the morphological operation and the H-minima label on it,
the improved algorithm is realized. Experiment shows that the improved algorithm can
significantly restrain the noise disturbance and reduce the over-segmentation, and its segmentation
accuracy is also improved.

Key words: image segmentation, watershed algorithm, adaptive genetic algorithm;
anisotropic diffusion, wavelet adaptive enhancement