3D Semi-Automatic Pollen Recognition Based on Palynological Knowledge Alain Boucher 1 , Pablo J. Hidalgo 2 , Jordina Belmonte 3 , Monique Thonnat 1 and Carmen Galan 2 1- INRIA, Sophia-Antipolis, France 2- University of Córdoba (UCO), Spain 3- Autonomous University of Barcelona (UAB), Cerdanyola del Vallès,Spain
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3D Semi-Automatic Pollen Recognition Based on Palynological Knowledge Alain Boucher 1, Pablo J. Hidalgo 2, Jordina Belmonte 3, Monique Thonnat 1 and Carmen.
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3D Semi-Automatic Pollen Recognition Based on Palynological Knowledge
Alain Boucher1, Pablo J. Hidalgo2, Jordina Belmonte3,
Monique Thonnat1 and Carmen Galan2
1- INRIA, Sophia-Antipolis, France2- University of Córdoba (UCO), Spain3- Autonomous University of Barcelona (UAB), Cerdanyola del Vallès,Spain
9/08/2002 INRIA - UCO - UAB 2
Introduction
European project (1999 - 2001)Prevention and treatment of asthma and
allergyTwo aspects:
• IdentificationIdentification (types and concentrations) of the main aeroallergens (pollen grainspollen grains, dust)
•Forecast of the aeroallergen dispersion
Pollen recognition: two modulesImage acquisition of pollen grains in 3DPollen grain recognitionPollen grain recognition
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Material and methods
Pollen grains are dyed with fuchsine fuchsine ((4
µg/100 ml)
Observation with a light microscopelight microscope (60x)
Automatic digitisation in 3D 3D
Database of more than 350 digitised grains 350 digitised grains
(30 different pollen types)
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Main Pollen Types Studied and Similars
PoaceaeOlea ParietariaCupressaceae
Populus BrassicaceaeFraxinusLigustrumPhillyreaSalix
BroussonetiaMorusUrtica membranacea
CeltisCoriaria
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3D pollen grain digitisation
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
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Palynological knowledge
The system tries to mimic the palynologists Knowledge is necessary to identify pollen grains
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Pollen recognition steps (1/2)
First step: coarse classification
Global measures on the grain (2D)Size, colour (RGB), shape, convexity, ...
Sampling date (external data for flowering season)
First estimations of possible types Sorted hypothesis list
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Use of the pollinic calendar
MANRESA-PORATS. Mean weekly concentrations (P/m3) 1996-1998
Recognition hypotheses includes the sampling dateMust take care of season variation
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Pollen recognition steps (2/2)
Second step: fine classification
Search for specific characteristics (3D)
Need specific knowledge about pollen types
Driven by the hypothesis list test only the strongest hypotheses
Iterate and refine until no ambiguity remains
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3D search in optical slices (key images - less blurred) 2D search in possible zones (regions of interest)
Search in a blurred image sequence
Image Mask
Interior Exine
Blur measure (SML) vs image number
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Above central image Below central image
Sum of bright regions
Sum of dark regions
Example: Cupressaceae cytoplasm
Segmentation of bright regions Segmentation of dark regions
cytoplasm
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Example: Olea reticulum
Network located on the external surface
Visible on top and bottom images Detection steps:
Check if the grain is reticulated Localise the reticulum (3D) Analyse the reticulum
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Results
Test on a database of 350 pollen grains Reference images (pollen grains without dust and pollution) Simulation of the sampling date Leave-one-out method used for validation Results of recognition
Test on a new set of images (different conditions) Low recognition rate between 4 allergenic pollen types (5 classes) : 45 % !! Problem of calibration and robustness for colour variation
Need to improve colour processing (more flexible system) Need to normalise image acquisition conditions