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首页> 外文会议>Conference on Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI; Aug 5-6, 2003; San Diego, California, USA >Automatic screening and multifocus fusion methods for diatom identification
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Automatic screening and multifocus fusion methods for diatom identification

机译：用于硅藻鉴定的自动筛选和多焦点融合方法

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摘要

The first part of this paper presents a new method for the classification and screening of diatoms in images taken from water samples. The technique can be split into three main stages: segmentation, object feature extraction and classification. The segmentation part consists of two modified thresholding and contour tracing techniques in order to detect the majority of objects present at the sample. From the segmented objects, several features have been extracted and analyzed. For the classification, a diatom training set was considered and the centroids, means and variances of four different classes were found. For the identification process diatoms were classified according with their Mahalanobis distance. The results show the method ability to select at least 80% of usable diatoms from images contaminated with debris. Secondly, full automation of the diatom classification is achieved when multi-focal microscopy is utilized for water sample acquisition. In this case, a necessary preprocessing step is image fusion. A novel wavelet-based fusion method proposed here returns a sharp image that can be directly used for segmentation. For a better understanding of the diatom shape, a 2.5D reconstruction is given.
机译：本文的第一部分提出了一种新方法，用于对水样品中的硅藻进行分类和筛选。该技术可以分为三个主要阶段：分割，对象特征提取和分类。分割部分包括两种修改的阈值和轮廓跟踪技术，以便检测样本中存在的大多数对象。从分割的对象中，提取并分析了几个特征。对于分类，考虑了硅藻训练集，并找到了四个不同类别的质心，均值和方差。为了进行鉴定，根据硅藻的马氏距离对硅藻进行了分类。结果表明，该方法能够从被碎片污染的图像中选择至少80％的可用硅藻。其次，当多焦点显微镜用于水样采集时，实现了硅藻分类的完全自动化。在这种情况下，必要的预处理步骤是图像融合。这里提出的一种新颖的基于小波的融合方法返回了可以直接用于分割的清晰图像。为了更好地理解硅藻的形状，给出了2.5D重建。

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