A Semi-Automatic System for Pollen RecognitionA.S.T.H.M.A. Project A Semi-Automatic System for...

A Semi-Automatic System for Pollen RecognitionA.S.T.H.M.A. Project

A Semi-Automatic System for Pollen Recognition

1- INRIA, Sophia-Antipolis, France2- LASMEA, Blaise Pascal University, Clermont-Ferrand, France3- University of Córdoba (UCO), Spain4- Autonomous University of Barcelona (UAB), Cerdanyola del Vallès,Spain

Alain Boucher1, Régis Tomczak2, Pablo Hidalgo3, Monique Thonnat1, Jordina Belmonte4,

Carmen Galan3 and Pierre Bonton2

2A Semi-Automatic System for Pollen RecognitionA.S.T.H.M.A. Project

Introduction

• Goal: Help the technician identify and count per types the pollen grains

• Pollen concentration can be used– for public report

– for the forecast system


System Architecture


System Hardware

A light microscope is driven automatically by a computer


Material and Methods

• System Hardware:– Light microscope Light microscope – 3 axes micro-positionning device3 axes micro-positionning device– CCD colour cameraCCD colour camera– Image acquisition cardImage acquisition card– PC computerPC computer

• Pollen slides are prepared by technicians– Pollen grains are sampled using Hirst traps– Pollen grains are coloured with fuchsine

• Pollen grains are observed with a magnification of 60x


Man-Machine Interface

The system can work in supervised or automatic mode


Pollen Grain Detection and Localisation

Segmentation Localisation

Automatic pollen grain detection


Pollen Grain Extraction

• 3D acquisition of pollen grains– set of images at different depths

Features may appear on different heights

• 100 optical sections• step = 0.5 microns

For each grain


Main Pollen Types Studied and Similars

PoaceaeOlea ParietariaCupressaceae

Populus BrassicaceaeFraxinusLigustrumPhillyreaSalix

BroussonetiaMorusUrtica membranacea

CeltisCoriaria


Pollen Grain Recognition

• Goal is to identify the typeidentify the type of the pollen grain from 3D images3D images

• Grain recognition is done following two stepstwo steps:

– Compute global measures

– Search for specific characteristics

• Pollen knowledgePollen knowledge is used to identify each grain

– Palynology (apertures, reticulum, size, …)

– Aerobiology (flowering period)


Palynological Knowledge

• The system tries to mimic the palynologists

• Pollen knowledge is used to identify each grain

• Knowledge sources from

– Palynology

– Aerobiology


Compute Global Measures

• Help to decide which grain characteristics to look for

• Measures:– Diameter

– Colour (RGB)

– Shape

– ...

Diameter (microns) vs Mean blue colour


Example: Broken Cupressaceae Grains

– Broken Cupressaceae grains are detected by shape:• Form factor: 4 surface / perimeter ²

• Convexity ratio: grain surface / convex hull surface

Broken grain Convex hull


– Only search for possible characteristics

– Found characteristics help to look for others

Search Pollen Characteristics

Image Full Grain

Inside Exine

Blur analysis vs Image number


Example: Poaceae Pore

Image 35 /100 Image 50 / 100 Image 65 / 100 Image 80 / 100


Example: Cupressaceae Cytoplasm

Image 40 / 100 Image 50 / 100 Image 60 / 100


Example: Olea Reticulum Detection

– The reticulum is located at top (or bottom) surface of the grain

– Steps to follow:• Check if the pollen is reticulated

• Localize the reticulum

• Analyze the reticulum


Example: Olea Reticulum Analysis

– Find the image with the sharpest reticulum

– Extraction of a zone with reticulum (muri & lumina)

– Extraction of some lumina (clear or dark)

– Analysis of size and shape of the lumina


Pollen Counting

• Final output: pollen concentration in the air– pollen count per type

– hourly, dayly or weekly concentration

• Unknown grains are reported to the technicians

• Output can be used– for public report

– for the forecast system


Conclusion

• Hard to give classification results so far– With only the 4 chosen pollen types, recognition of almost 100%, but

need more tests including other similar pollen types

• Database of more than 350 digitized pollen grains350 digitized pollen grains (30 different pollen types)

• Steps of development :– 4 allergenic pollen types (Cupressaceae, Olea, Parietaria, Poaceae)

– 11 similar pollen types (Populus, Fraxinus, Morus, Celtis, …)

– 15 other frequent pollen types (Betula, Quercus, Pinus, …)

A Semi-Automatic System for Pollen RecognitionA.S.T.H.M.A. Project A Semi-Automatic System for...

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