## 8 Conclusions  
The main contributions of this work are: (a) big database collection: a big database of diatoms composed of 80 diatom types with an average of 100 distinct diatom valves per type manually cropped has been collected; this database is being extended to 100 types; and (b) the study of the main handcrafted features to classify diatoms, proposing an efficient method for classification.  
Thus, the state of the art in morphological, statistical and texture descriptors has been exhaustively tested for classifying 80 diatom types. Some of them, like texture based on LBP and log Gabor descriptors, had not been evaluated before in this field. The combination of these features provided an improvement up to 98.11% accuracy compared to previous related works. We concluded that the combination of different descriptors categories, such as morphological and statistical descriptors, together with space-frequency representations, specifically log Gabor and texture by means of LBP, provided the best classification accuracy rates.  
Along this research, we also come across several other challenging issues, such as the classification of the same diatom type during its life cycle and from different views. A diatom type may present different appearances according to its view with respect to the z-view, and consequently, its morphological and also statistical descriptors can drastically vary. This causes the morphological descriptors to not always analyze the most discriminating features. This could be solved by having more data representing all possible cases or alternatively by using a hierarchical tree classification. Thus, it could be recommended to simplify the classification using morphological refinement in two classes: centric diatoms and pennate diatoms. Afterwards, depending on the taxon, the search would focus on some other details, like texture, striae density (number of stria per micron), lineolae density (pixels per micron), etc. It should be noted that some diatom taxa are almost the same, even for experts, which proves the difficulty of this task.  
Precise segmentation is a critical point for the whole classification process. This is a limitation for handcrafted feature approaches. Effectively, segmentation should be done accurately, although our purpose in the current study focuses on comparing descriptor's discriminant capacity. Therefore, we do not pursue perfect binary masks, but suitable enough to be equally shared by all descriptors.  
In order to handle the above-mentioned difficulties, the authors suggest to explore new classification techniques based on deep learning (Convolutional Neural Networks (CNN)) able to learn further from larger datasets and without segmentation. In this study, it has been proven that the accuracy in traditional methods does not always improve with augmented data (it is also counterproductive due to orientation-invariant features and possible overfitting). An accuracy up to 97.56% was obtained classifying 20 classes with no data augmentation (100 samples/class) versus 98.82% with data augmentation (300 samples/class).  
The authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO), [[http://aqualitas-retos.es/en/](http://aqualitas-retos.es/en/)]([http://aqualitas-retos.es/en/](http://aqualitas-retos.es/en/)).  
[PERSON] designed and performed the experiments. She wrote the paper, and she is the corresponding author. [PERSON] designed and conceived of the experiments. He also revised the manuscript and supervised the research. [PERSON] prepared the original and augmented the database. [PERSON] performed part of the data acquisition and some graphics of the figures. [PERSON] prepared the database to be available for dissemination. [PERSON] supervised the research. [PERSON] performed part of the data acquisition. [PERSON] performed part of the data acquisition, and he made the annotations of the entire original database.  
There is no conflict of interest.