Region and Contour Based Cell Cluster Segmentation Algorithm for In-Situ...

Region and Contour Based Cell Cluster Segmentation

Algorithm for In-SituMicroscopy

A. Sheehy1, G. Martinez1, J.-G. Frerichs2, T. Scheper2

1Image Processing and Computer Vision Research Laboratory (IPCV-LAB), Universidad de Costa Rica

2Institute of Technical Chemistry (TCI), Leibniz Universität Hannover

IEEE CCE, Mexico City, Mexico, November 2008

Topics

• Introduction• Existing algorithms• Proposed algorithm• Experimental results• Summary and conclusions

Introduction

Bioreactor

Cell culture

microscope

CCD Camera

Capturedintensityimage

Imageanalysis

Sampling zone

Estimation of importantparameters of thecell culture

In-situ microscopy

IntroductionCell image segmentation

Binary image IB(x,y), x:1..L,y:1..M, with pixels belongingto the cell clusters in whiteand pixels belonging to thebackground in black

Segmentation

Intensity image I(x,y), x:1..L, y:1..M, captured by the in-situ microscope

Existing Algorithms

• Thresholding based segmentation algorithmsEspinoza’s algorithm: Latest contribution for segmenting BHK cells images captured by an in-situ microscope

• Contour based segmentation algorithms• Region based segmentation algorithms• Combinations of the previous

Cell cluster segmentation algorithms can be roughlydivided into 4 main groups:

Espinoza’s Algorithm

a. Estimate the local variance σ2(x,y) at eachimage position (x,y) using a 3x3 window:

1) Global thresholding of the local variance

3 3

3 3

1( , ) ( , )49 m n

m x y I x m y n=− =−

≅ ⋅ + +∑ ∑

[ ]3 3

22

3 3

1( , ) ( , ) ( , )50 m n

x y I x m y n m x yσ=− =−

≅ ⋅ + + −∑ ∑

2

2

0, ( , )( , )

1, ( , )g

Bg

if x y thI x y

if x y th

σ

σ

≤= >

b. Globally threshold the local variance:

thg is estimated applying a maximumlikelihood algorithm (Kittler & Illingworth)

c. Apply a 5x5 median filter to IB and eliminatesmall regions (<0.5%)

with:

a. Find for each segmented region r all the borderpixels Ig(r), g:0...G(r).

Espinoza’s Algorithm

( )

( ) ( )( )

1

1 rHr rl hr

hth I

H =

= ∑

2) Local thresholding of the intensity

c. Locally threshold the intensity in each region r :

d. Apply a 5x5 median filter to IB and eliminatesmall regions (<0.5%)

b. Select from all the border pixels Ig(r) only thoseborder pixels Ih(r), j:0...H(r) that have an intensity value similar to the background intensity value byapplying a RANSAC algorithm

with:

ryxthyxIifthyxIif

yxI r

r

B ∈∀

>≤

= ),(,),(,0),(,1

),( )(

)(

Espinoza’s Algorithm

Lowreliability in low contrastimages:

Lowprecision:

Problems

To segment the background using a region and contour based approachand then to invert the resulting binary image to get the cell cluster regions

Proposed Algorithm

we propose:

Because:

1. The background is homogeneous

2. The cell clusters are non homogeneous with darker intensityvalues and well defined contours

Proposed Algorithm1) Seed selection

a. Select as background seed candidates those pixles that meetthe following rules:

2 22( , ) 0.20x yσ σ< ⋅

2 2( , )I x y m σ> −

where, and are the mean and standard deviation of thepixels’ intensity values in the background

2σ2m

Proposed Algorithmm2 and σ2 , as well as the mean m1 and standard deviation σ1 of the pixels’ intensity values in the cell clusters are estimated modeling the probability density function of the intensity values p(I) as a sum of two weighted Gaussian density functions:

∑ ∑= =

−⋅⋅−⋅−⋅⋅=2

1

2

1

21 2

))(log()(2

)2log(2

))(log()()(j j

jjjjNkkcNNkckcNkL σπ

where N is the total number of pixels, h(I) is the number of pixels withintensity I and k is selected maximizing the following likelihood function:

10

( )k

I

h IcN=

= ∑ 101

1 ( )( )

k

I

h Im Ic k N=

= ⋅ ⋅∑ 2 21 1

01

1 ( )( )( )

k

I

h II mc k N

σ=

= ⋅ − ⋅∑255

21

( )I k

h IcN= +

= ∑255

212

1 ( )( ) I k

h Im Ic k N= +

= ⋅ ⋅∑255

2 22 2

12

1 ( )( )( ) I k

h II mc k N

σ= +

= ⋅ − ⋅∑

Proposed Algorithm

b. Reject those seed candidates that meet at least

one of the following rules:

-One of its neighbors is a contour pixel

-One of its neighbors has and also has a neighboring contour pixel

-One of its neighbors has , and a neighboring contour pixel

1( , )I x y m<

1 1 1( , )m I x y m σ< < +2 2

2( , )x yσ σ>

The contours in the image are obtained with the SUSAN algorithm:

Proposed Algorithm

The intensity values inside a circular mask are compared with the intensityvalue in the center (nucleus). If there isa low amount of pixels inside the maskwith an intensity value similar to theintensity value of the nucleus, then thenucleus is a contour pixel.

Proposed Algorithm2) Region growing

a. Add a pixel to a seed unless it meets at least one of thefollwing rules:

-It is a contour pixel.-It has and a neighboring contour pixel.-It has , and a neighboring contour pixel

1( , )I x y m<1 1 1( , )m I x y m σ< < + 2 2

2( , )x yσ σ>

Intensity image Binary image with segmented background

b. Repeat step a. until no more pixels can be added tothe background region

Proposed Algorithm3) Invert the binary image

Binary image withsegmented background

Inverted binary image

a. Invert the binary image with the segmented background.

b. Apply a 5x5 median filter to the inverted binary image.

Reliability

Experiments in 30 different BHK cell images:

*BHK: Baby Hamster Kidney cells

Experimetal Results

100%30Proposedalgorithm

66%20Espinoza’salgorithm

Percentage ofimages with

most cell clusterscorrectly

segmented

# images withmost cell clusters

correctlysegmented

Algorithm

Espinoza’s algorithm segmentation result

Proposed algorithm segmentation result

Low contrastintensity image

Precision

2 2 2( ) ( )i i id x y= ∆ + ∆

n

dMSE

n

ii∑

== 1

2Mean square positionerror of the boundaries:

Experimetal Results

55,10%1,8 pixel24,02 pixel2

Improvement

AverageMSE for

the proposedalgorithm

AverageMSE for

Espinoza’s algorithm

Manual segmentation Espinoza’s algorithm segmentation result

Proposed algorithm segmentation result

Distance from each boundary pixel i, i:1…n, of the automatically segmented regions to the closest boundary pixel of the manually segmented regions:

Conclusions

The proposed segmentation algorithm is

• 33% more reliable• 55% more accurate

than the Espinoza’s algorithm in low contrast images

Thank you very much for your attention