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。

Pulse Coupled Neural Network (PCNN) has gained widely research as a new artificial neural network. It derives directly from the studies of the small mammal's visual cortex. PCNN is a model with multiple parameters, and finding the proper values of these parameters is an onerous task. So a simplified PCNN is put forward and its performance in removing Gaussian noise of image is discussed in this article. The algorithm of PCNN combined with median filter and the step-by-step modifying algorithm, which is also based on PCNN, are proposed, and the experiment results of the two algorithms are analyzed and compared with that of median filter and wiener filter.

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.

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.

The extremum and median filter can not only preserve the details of image as much as possible but aslo remove the noise when the grayscale changed gently. But, it's not very effective when processing those images including extremum areas. A new algorithm is presented in this paper and it solves the problem is presented in this paper and it solves the problem existing in formet filter. The experimental results show that the method is quite effective on most images and has better performance.