A Novel Method for the Separation of Overlapping Pollen Species for Automated Detection and Classification.

Santiago Tello-Mijares,Francisco Flores

doi:10.1155/2016/5689346

Santiago Tello-Mijares, Francisco Flores

Open Access

https://doi.org/10.1155/2016/5689346

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Abstract

The identification of pollen in an automated way will accelerate different tasks and applications of palynology to aid in, among others, climate change studies, medical allergies calendar, and forensic science. The aim of this paper is to develop a system that automatically captures a hundred microscopic images of pollen and classifies them into the 12 different species from Lagunera Region, Mexico. Many times, the pollen is overlapping on the microscopic images, which increases the difficulty for its automated identification and classification. This paper focuses on a method to segment the overlapping pollen. First, the proposed method segments the overlapping pollen. Second, the method separates the pollen based on the mean shift process (100% segmentation) and erosion by H-minima based on the Fibonacci series. Thus, pollen is characterized by its shape, color, and texture for training and evaluating the performance of three classification techniques: random tree forest, multilayer perceptron, and Bayes net. Using the newly developed system, we obtained segmentation results of 100% and classification on top of 96.2% and 96.1% in recall and precision using multilayer perceptron in twofold cross validation.

Highlights

In allergic rhinitis and asthmatic exacerbations, major allergens are dust mites, followed by pollen
The proposed system is accomplished in four stages: image preprocessing (RGB to lab), pollen image segmentation (mean shift and Otsu’s method, followed by the H-minimum process based on the Fibonacci series to identify the overlapping pollen and applying gradient vector flow snake (GVFS), feature extraction, and classification
In order to classify segmented pollen into 12 pollen species and obtain final classification results, we have explored the use of three different classification approaches implemented in Weka (Waikato Environment for Knowledge Analysis) [18, 19]: random tree forests [20] (RTF), a multilayer perceptron (MLP) and a Bayesian network (BN)

Summary

Introduction

In allergic rhinitis and asthmatic exacerbations, major allergens are dust mites, followed by pollen. Martınez Ordaz et al [1] found a significant correlation between the concentration of environmental pollen and the frequency of asthmatic exacerbations in the Lagunera Region in north-central Mexico, with Pearson’s r of 0.63 and a coefficient of determination r of 0.39 (p < 0.01) This is due to the pollution of a metropolitan area of 1.5 million inhabitants and an arid climate where rain is not often present to cleanse the air of pollutants. Interest has increased in automatic systems to identify pollen, which can be helpful for palynologists and medical specialists Such a system could help make climate change studies, medical allergies calendar, and forensic science easier. We proposed a dataset composed of 12 pollen species divided into 12 palynological classes, as can be seen in Table 1 and Figure 1, in order to apply

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