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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Recognition of Protozoan Parasites from Microscopic Images: Eimeria species in Chickens and Rabbits as a case study
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Recognition of Protozoan Parasites from Microscopic Images: Eimeria species in Chickens and Rabbits as a case study

机译：从微生物寄生虫识别来自微生物图像：鸡和兔子的Eimeria物种作为一个案例研究

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摘要

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.
机译：通过利用对象形态特征来进行自动诊断和诸如寄生虫的寄生虫等寄生虫的鉴定。不同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，为了降低这种复杂性，本文基于分析三组基于像素的特征集：列特征（CF），行特征（RF）和通过合并CF而获得的第三个（CRF）的自动识别。和rf在一起。对于分类任务，已应用K-Collest邻居（KNN）和人工神经网络（ANN）。通过调整5倍交叉验证来评估分类结果。另外，通过Relieff特征选择方法选择了一种特征的强大子集，以防止过度装备，这反过来又改善了最终结果。已经检查了两种原生动物寄生虫属的微生物图像幻灯片数据库，称为禽类和兔子的Eimeria，以评估所提出的方法的稳健性。当使用整个特征时获得的最高精度率为85.55％（±0.39％），分别从灰度水平和彩色图像分别为96.6％（±0.82％）。当特征尺寸减少三分之二时，这些结果增加了5％，当使用重质量时。该特征集已产生高精度的结果，并且预计将使自动识别更简单，而不是形态学特征的分析。

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