# Multi-scale k-means clustering of multispectral images

Mathias Corcel, Cecile Barron, Fabienne Guillon, Marie Francoise Devaux

► To cite this version:

Mathias Corcel, Cecile Barron, Fabienne Guillon, Marie Francoise Devaux. Multi-scale k-means clustering of multispectral images. Chimiométrie XVII, Jan 2016, Namur, Belgium., 2016. hal-01608725

HAL Id: hal-01608725

https://hal.science/hal-01608725

Submitted on 3 Jun 2020

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# Multi-scale k-means clustering of multispectral images

Corcel Mathias¹, Barron Cécile², Guillon Fabienne¹,
Devaux Marie-Françoise¹

1) UR 1268 BIA, INRA Nantes, France

2) UMR 1208 IATE, INRA Montpellier, France

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Chimiométrie XVII – Namur - 19/01/2016
Large biological
variability

* Many images are acquired for plant
caracterization

- To form a mosaic image representing the cross-section

- To observe multiple sections of the same sample

- To observe multiple samples

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* Large data

- Mosaic image with large number of pixels (~20M pixels / mosaic image without background)

- + number of image per sample

- + number of samples
# Histological analysis through multispectral imaging

* Histological analysis of plant tissues

- Maize (Zea mays) stems

- Many phenolic components present in cell walls

* Hydroxycinnamic acids: ferulic, p-coumaric ...

* Lignins with various types of monomers

* These components are autofluorescent

* Explore the variability of the different tissues using their autofluorescence

- Multispectral fluorescence imaging using 4 different *excitation* conditions of UV or visible light

UV1

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UV2

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Blue

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Green

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# Objective

* Use methods to classify a large number of pixels using all spatial variability with unsupervised classification

* In order to explore the variability

* Using data with large population but small number of variables

- No a priori knowledge on autofluorescence for most tissues

- Not possible to select pixels or regions of interest
# Pyramids in image processing

* Multi-scale representation on an image

* The image is reduced in each level

* Simple pyramid : Mean pyramids with halved resolution each level

- Each new pixel is the mean of 4 pixels

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Principle of classification using
pyramids

• 1) Start at the level with the smallest resolution

• 2) Classify pixels into k clusters
   - Using k-means with Euclidian distance on Principal components

• 3) Select *n* pixels in each cluster
   - Random selection

• 4) Expand the selected pixels in the next level

• 5) Repeat from step 2 until the last level of the pyramid

<figure><img src="image_20.png" /><figcaption>Selection of the pixels in each level</figcaption></figure>
# K-means initialisation : Number and position

* Histogram (+ Smoothing) of the Score plot
  Principal Components 1 and 2

* H-maxima transform

* Gives initial positions for clusters on
  components 1 and 2

* Positions on other components set to 0

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Method used on an image

* Start at level 6
  - Resolution 1/32

* 15 clusters found

* 2000 pixels per cluster selected
  - Constant

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# Comparison of classification with and without pyramids

<figure><img src="image_26.png" /><figcaption>Without pyramids</figcaption></figure>

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* The clusters obtained are largely similar
Comparison of classification with and
without pyramids

Sclerenchyma

With Pyramids

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Parenchyma

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Without Pyramids

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Comparison of classification with and
without pyramids

* Some clusters regroup multiple tissues (Identified by their biological role and/or morphology)

- Their autofluorescence is too similar

- Their react the same way to some enzymes

* And both approaches with/without pyramids have the same clusters

With Pyramids

Phloem + Some
Parenchyma cells

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Without Pyramids

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Conclusion

* Simple adaptation of image analysis and chemometry : image pyramids and k-means, allow to segment large images with satisfactory results

* The number of pixels is reduced by a factor of ~1000, allowing to analyze a batch with many mosaic images

* The segmentation is done using all the initial variability and without loss during the process
# Perspectives

* Test the limits of pyramids

- Further decrease the resolution

* Improve the selection of pixels

- Validate the repeatability of the method due to the random selection of pixels in each cluster

- New method of selection based on the variance inside each mean pixel

* Find a solution to *objectively* compare unsupervised classifications

* Analyse complete collections of images
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Thanks for your attention !

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