Date post: | 30-Dec-2015 |
Category: |
Documents |
Upload: | gervase-scott |
View: | 226 times |
Download: | 0 times |
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
9/08/2002 INRIA - UCO - UAB 3
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)
9/08/2002 INRIA - UCO - UAB 4
Main Pollen Types Studied and Similars
PoaceaeOlea ParietariaCupressaceae
Populus BrassicaceaeFraxinusLigustrumPhillyreaSalix
BroussonetiaMorusUrtica membranacea
CeltisCoriaria
9/08/2002 INRIA - UCO - UAB 5
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
9/08/2002 INRIA - UCO - UAB 6
Palynological knowledge
The system tries to mimic the palynologists Knowledge is necessary to identify pollen grains
9/08/2002 INRIA - UCO - UAB 7
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
9/08/2002 INRIA - UCO - UAB 8
Use of the pollinic calendar
MANRESA-PORATS. Mean weekly concentrations (P/m3) 1996-1998
0
5
10
15
20
25
30
35
40
45
50
55
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
CHENOPODIACEAE-AMARANTHACEAE Plantago Celtis Coriaria POACEAE total
BELLATERRA-CUPRESSACEAE/Populus. Mean weekly concentrations (P/m3) 1994-1998
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
CUPRESSÀCIES POPULUS
Recognition hypotheses includes the sampling dateMust take care of season variation
9/08/2002 INRIA - UCO - UAB 9
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
9/08/2002 INRIA - UCO - UAB 10
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
9/08/2002 INRIA - UCO - UAB 11
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
9/08/2002 INRIA - UCO - UAB 12
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
9/08/2002 INRIA - UCO - UAB 13
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
Recognition rate between 4 allergenic types + others (5 classes) : 99,7%
Recognition rate between 31 pollen types + others (32 classes) : 77,7%
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
9/08/2002 INRIA - UCO - UAB 14
Future work: aerobiological images
Isolation of the pollen grains from dust