# Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset V1 (CPD-1)
## 2 Related Works  
[PERSON] et al. [11] proposed a 2-stage segmentation pipeline. In the first stage, they utilized Hough transform in order to detect the initial contours based on a circle-like pattern. After the initial contour estimation, three different methods were compared for the fine detection of the pollens' contours. In particular, they utilized a basic edge-contour approach which was based on the gradient image, the snake-contour algorithm and the convex-hull algorithm. However, not all pollen grains have a circle-like shape as the ones examined in this study, originating from the Uricaceae family, and, thus, their approach may perform poorly on other types of pollen grains.  
[PERSON] et al. [12; 13] presented the Pollek13K dataset, which consists of a bit more than 13.000 segmented grains of 5 pollen types, which included a separate class for possible debris. In order to segment each one of the 13k grains, they developed a 3-stage processing pipeline, which consists of a pre-processing stage, the segmentation stage and a post-processing stage. During the preprocessing stage, several noise reduction filters are applied (i.e., mean shift filtering, Gaussian blurring), as well as the [PERSON]'s thresholding method to highlight the foreground and the pollen grains' contours. In order to identify each pollen grain, several morphological operations are used. In addition, having the objects of interest filled with black color while the background filled with white color, any detected object that was smaller or larger than a predefined minimum or maximum size was discarded from the final dataset, based on analyzing the connected components in the image. Finally, in order to highlight only the foreground (i.e., the object of interest) in each of the segmented images and discard any of the background information, a post-processing approach based on several noise reduction filters and morphological operations was used. When we applied this processing pipeline on our data, we found that, although it performs well on large or well separated pollen grains, it really struggles to identify overlapping ones.  
[PERSON] et al. [14] developed a pollen grain segmentation pipeline which is based on watershed algorithm. Prior to the application of the watershed algorithm, the image applied a threshold, as well as several morphological operations [15], similarly to the previously mentioned approaches.  
Finally, [PERSON] et al. [16] developed a pollen grain detection method based on a convolutional neural network (CNN). Their CNN was trained based on a multifocal analysis approach, in which several focus points were used on the camera in order to capture all pollen grains under the optimal focus setting. The camera captured a video across all the focus points, which was used to train the network, after annotating a bounding box around each of the pollen grains. Their approach was trained and tested on 11 different pollen types and outperformed other methods that were based on non-machine learning image processing techniques, achieving recall and precision scores of 99% and intersection over union (IoU) score of 0.9. However, two main drawbacks of this approach is the laborious and time-consuming process of capturing the video, as well as annotating each grain at the various focus points.  
In this study, we present a novel pipeline for segmenting pollen grains from fuchsin-stained microscope images of 20 types of plants gathered in Crete, Greece. The novelty of this study is twofold. First, the proposed method is robust and accurate since it is able to correctly detect and segment pollen grains of various shapes and sizes. Secondly, the proposed dataset is a unique and novel collection of pollen grains from plants across Crete, Greece, which we hope that this will accelerate the development of automated machine learning and deep learning systems for pollen classification, which is an essential step for melisospalynology and honey certification.