Automatic screening and multifocus fusion methods for diatom identification

The first part of this paper presents a new method for the classification and screening of diatoms in images taken\ud from water samples. The technique can be split into three main stages: segmentation, object feature extraction\ud and classification. The segmentation part consists of two modified thresholding and contour tracing techniques\ud in order to detect the majority of objects present at the sample. From the segmented objects, several features\ud have been extracted and analyzed. For the classification, a diatom training set was considered and the centroids,\ud means and variances of four different classes were found. For the identification process diatoms were classified\ud according with their Mahalanobis distance. The results show the method ability to select at least 80% of usable\ud diatoms from images contaminated with debris. Secondly, full automation of the diatom classification is achieved\ud when multi-focal microscopy is utilized for water sample acquisition. In this case, a necessary preprocessing step\ud is image fusion. A novel wavelet-based fusion method proposed here returns a sharp image that can be directly\ud used for segmentation. For a better understanding of the diatom shape, a 2.5D reconstruction is given.\u