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Abstract：
Automated diagnosis and identification of diseases and conditions such as parasites from microscopic images have been mainly carried out by utilizing the object morphological characteristics. The extraction of morphometric features needs the use of highly complex techniques that require computational power. Therefore, in order to reduce this complexity, this paper presents an automated identification based on analyzing three groups of pixel-based feature sets: column features (CF), row features (RF), and the third one (CRF) obtained by merging CF and RF together. For the classification task, K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANN) have been applied. The classification results have been evaluated by adapting a 5-fold cross validation. Additionally, a robust sub-set of the features has been selected by Relieff feature selection method to prevent overfitting, which in turn has improved the final results. Two microscopic image slide databases of a type of protozoan parasites genus called Eimeria in fowls and rabbits have been examined in order to assess the robustness of the proposed methods. The highest accuracy rates obtained when the entire features were used are 85.55% (±0.39%) and 96.6% (±0.82%) from grey-scale level and color images, respectively. These results have been increased by 5% when the feature size is reduced by two thirds when Relieff was utilized. The feature sets have yielded highly accurate results and are expected to make the automatic identification simpler than the analysis of morphological features.
摘要：
从显微图像中自动诊断和识别诸如寄生虫的疾病和状况主要通过利用对象的形态特征来进行。形态特征的提取需要使用需要计算能力的高度复杂的技术。因此,为了减少这种复杂性，本文提出了一种基于分析三组基于像素的特征集的自动识别:列特征(CF)、行特征(RF)，以及通过将CF和RF合并在一起获得的第三个(CRF)。对于分类任务，已经应用了K最近邻(KNN)和人工神经网络(ANN)。通过采用5倍交叉验证来评估分类结果。此外,通过Relieff特征选择方法选择了一个鲁棒的特征子集，以防止过拟合，这反过来又改善了最终结果。已经检查了家禽和兔子中一种称为艾美球虫的原生动物寄生虫属的两个显微图像幻灯片数据库，以评估所提出方法的鲁棒性。使用整个特征时获得的最高准确率为85.55%（±0.39%）和96.6%（±0.82%），来自灰度和彩色图像。分别。当使用Relieff时,当特征尺寸减小三分之二时,这些结果增加了5%。特征集已产生高度准确的结果，并有望使自动识别比形态特征分析更简单。
