A method for optimal complex rank-order morphological filters with hybrid genetic algorithm is presented in this paper. It combined simulated annealing genetic algorithm (SAGA) with adaptive genetic algorithm (AGA) to achieve optimal filtering parameters in a global searching. Experimental results show that this method is practical, easy to extend, and improves the performances of the complex rank-order morphological filters. By means of adaptive optimizing training the percentile and the structuring elements, morphological filters possess the shape and structural system characteristics of image targets. Complex rank-order morphological filters formed in this way become intelligent and can provide good filtering results and robust adaptability to image targets with clutter background.

A new, simple method of counting and segmenting cell image is suggested in this paper. It is based on the feature of cell's logical and morphological iafornmation. By using of mathematics, n, morphological, logical operation and laplacian fiter, the method is realized with the MATLAH 5.10. The segment effect of this algorithm is tested with blood cell image in this article and the result is desirable, Particularly this method can count the number of blood cells. However, this counting is not very accurate, but it is enough for biological research of cytologlcal count. At the same time this method can segment special blood cell from its neighhorhood, which is very important for the development of image scgment technology, because traditional image segment method is inadequate to achieve such segment and count of calls.

Median filter can inhibit the impulse noise in the image, butit always erodes or dilates the edges of images. H.S.Ranganath mentioned that impulse noise could beremoved through modifying the intensity of those contaminated pixels step by step using PCNN. Obviously this method consumes much more time in computation. Combining the PCNN model with the median filter, this paper presents an impulse noise filter based on a simplified PCNN model which has less parameters. Not only can it remove the impulse noise effectively, but also it keeps the details of images as can as possible. It can be verified through experiments and theory analysis that this kind of filter is superior to the normal median filter and the filter mentioned by H.S.Ranganath, no matter in the aspect of noise removal or in the aspect of keeping details.

90年代发展形成的脉冲耦合神经网络（PCNN）模型特别适合于图像分割，边缘提取等方面的应用研究，但众所周知，PCNN模型图像分割效果不但取决于PCNN模型中各个参数的合理选择，而且同时还取决于循环迭代次数的确定选择准则，通常循环迭代次数N的选择通过人工交互方式来确。定正因如此选择合适的准则来确定N是PCNN图像分割的关键，但目前还没有文献提出一个合适的准则来解决这个问题。本文结合图像统计特性和PCNN参数模型提出了熵值最大准则。该准则实现了PCNN神经网络的自动图像分割。对于PCNN的理论研究和实际应用具有非常重要的现实意义。

Traditional image segmentation algorithms exhibit weak performance for plant cells which have complex structure. On the other hand, pulse-coupled neural network (PCNN) based on Eckhorn's model of the cat visual cortex should be suitable to the segmentation of plant cell image. But the present theories cannot explain the relationship be. tween the parameters of PCNN mathematical model and the effect of segmentation. Satisfactory results usually require time.consuming selection of experimental parameters. Mean-while, in a proper, selected parametric model, the number of iteration determines the segmented effect evaluated by visual judgment, which decreases the efficiency of image segmentation. To avoid these flaws, this note proposes a new PCNN algorithm for automatically segmenting plant embryonic cell image based on the maximum entropy principle. The algo-rithm produces a desirable result. In addition, a model with proper parameters can automatically determine the number of iteration, avoid visual judgment, enhance the speed of' segmentation and will be utilized subsequently by accurate quantitative analysis of micro.molecules of plant cell. So this algorithm is ainable for theoretical investigation and application of PCNN.