植物胚性细胞定量分析研究需要首先将其切片图像分割处理，然后进行大分子量化分析但植物细胞切片图像上表现出来的植物细胞特有的复杂属性，使得一般图像分割分析方法很难奏效20世纪90年代中期发展起来的脉冲耦合神经网络PCNN直接来自于动物视觉特性研究，应当适合这类植物细胞图像的分割处理但因目前理论很难解释PCNN数学模型参数与图像分割效果之间的关系，一般较好图像分割效果的获得需多次实验选择这些模型参数同时在模型参数选定的情况下，其循环迭代次数直接关系到分割结果的好坏，而分割好坏的判定需人眼观察分析，这样便引入了人为干预为此提出一种建立在分割图像熵值最大原则上的PCNN植物细胞图像自动分割新算法。

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.

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.

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.

阐述了小波变换、遗传算法、模糊数学、神经网络、数学形态学等生物细胞图像分割算法以及边缘检测、区域分割等传统图像分割算法为主的生物细胞图像分割技术的发展现状，指明了生物细胞图像本身具有的复杂性、多样性、各自差异性等属性是实现生物细胞图像全自动分割的难点，只有彻底结合生物视觉特性数学模型算法的研究和应用，才能使生物细胞图像全自动分割成为可能。