本研究之目的是發展兩套以演化型禁制搜尋法為基礎的系統，一是正常與肝癌組織切片影像之分類系統，另一為幼鼠之聽覺神經的自動辨識與分類系統。肝臟組織切片影像之分類系統是一個改良型的機率神經網路分類器。相較於傳統的機率神經網路分類器，此分類器具有較小的網路結構與較佳的分類性能。此系統之發展主要分成兩個部分。第一部分是擷取肝臟組織切片影像的各種碎形維度特徵，並找出最佳配對的碎形維度特徵。第二部分是在最佳配對之碎形維度特徵的基礎上，使用一個本研究所發展之新的向量量化技術(簡稱ETSA-I)來建構一個改良型的機率神經網路分類器。在實驗中，ETSA-I與常見之向量量化技術作比較具有較佳之量化失真值與強健性。在發展自動辨識幼鼠之受激勵的聽覺神經細胞中的主要工作包括細胞的特徵擷取，以及依據所擷取的特徵使用本研究所發展的自動群聚演算法(ETSA-II)來探討這些受激勵之聽覺神經細胞的可能分類。其中，ETSA-II已在實驗中被證實可以有效率地決定出最佳之群聚個數及同時建構出一個具良好正確性的群聚結搆。最後，本研究利用20張正常的肝臟組織切片影像與20張肝癌組織切片影像來測試所建構之改良型的機率神經網路分類器。對於訓練組，此分類器的準確度至少可達96.0%。對於測試組， 其準確度至少88.5%。另一方面，ETSA-II對於自動辨識系統所找出之238 個幼鼠的受激勵的聽覺神經細胞分成3類。

　The purposes of this dissertation are to develop two evolutionary tabu search based systems for the classification of liver biopsy images and the identification of the excited auditory neurons of rat’s brain tissue image, respectively. The liver biopsy image classification system is an im-proved probabilistic neural network (PNN) classifier that has a smaller network size and better classification performance than those of traditional probabilistic neural networks. This proposed system contains two main parts. One is to extract the varied fractal dimension features in the liver biopsy images and then determine the best pair of fractal dimension features. Based on the selected pair of features, the second part is to construct an improved PNN classifier using a new vector quantization technique called the ETSA-I. The ETSA-I is better than some other proposed algorithms in terms of distortion and robustness measures. In the excited auditory neuron identi-fication system, the first task is to extract the features of excited auditory neurons. Then, in the feature space, the optimal number of classes for the excited auditory neurons is investigated us-ing the proposed automatic clustering algorithm called the ETSA-II. The ETSA-II can effec-tively determine the optimal number of clusters and at the same time construct a clustering structure with good validity. In this study, 20 normal and 20 cancerous liver biopsy images are used to test the liver biopsy image classification system. For the training pattern set, the accuracy of the classification system is 96.0% at least. For the testing pattern set, the accuracy is 88.5% at least. On the other hand, for the identified 238 auditory neurons the cluster analysis using the ETSA-II shows that the optimal number of classes of the excited auditory neurons is three.

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