An Automated Segmentation Method for Microarray Image Analysis

Wei-Bang Chen1, Chengcui Zhang1 and Wen-Lin Liu2

1Department of Computer and Information Sciences2Dept. of Management, Marketing, and Industrial Distribution

University of Alabama at Birmingham March 17, 2006

What is microarray?

DNA microarray was introduced in 1999 by Patrick Brown and Vishwanath Iyer.[1]

Microarray allows biologists to monitor gene expression level in parallel.

[1] V. R. Iyer, et al. "The transcriptional program in the response of human fibroblasts to serum," Science, v283, pp. 83-7, 1999.

Problems and motivations

Uneven backgrounda result of improper counterstain

Inner holes (a donut, comet, or overlap)manufacturing quality of the slide.

ScratchTouching the spots area accidentally

NoisesInadequate washing

A typical microarray slide

y

x

Channel 532 / Channel 635

Block 4 (30 × 30 spots)

1 4

4845

Top margin

Bottom margin

Left margin Right margin

Three-step approach

Background identification and noise removal Background identification Noise removal

Fully automatic griddingSpot segmentation

Three-step approach

Background identification and noise removal Background identification Noise removal

Fully automatic griddingSpot segmentation

Step 1.1 Background identification

Coordinate of slide

Small area 1

Small area 2

Small area 3

Small area 4

Small area 5

TG

TL

Unevenbackground

0 Slide width or height

Background intensity

Local threshold TL

Global threshold TG

Background eliminated by local threshold

Background eliminated by global threshold

Step 1.1 Background identification

ij

mnm

n

ijnm a

aa

aaaAA

1

111

ij

rcr

c

ijcr A

AA

AAAEE

1

111

ijanmA 1

To deal with the uneven background problem, we firstly divide the entire slide into small areas.

Step 1.1 Background identification

GGG

c

jj

r

iiG

rjjjj

iciii

mT

VHcr

m

AAAjEV

AAAiEH

3

minmin1

:,

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11

T 21

21

For global threshold, we use the matrix of mean values of all pixel intensities in the small area to represent the slide.

Step 1.1 Background identification

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iniii

mT

VHnm

m

aaajAv

aaaiAh

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minmin1

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For local threshold, we find the minimum intensity values of each row and columns

Step 1.2 Noise removal

(A) Target pixel is signal

Signal

P1

P1'

P2

P2'

P3

P3'

P4 P5

P4'P5'

P6 P7 P8 P9 P10

P6'P7'P8'P9'P10'

P11 P12 P12' P11' Noise

P1

P1'

P2

P2'

P3

P3'

P4 P5

P4'P5'

P6 P7 P8 P9 P10

P6'P7'P8'P9'P10'

P11 P12 P12' P11'

(B) Target pixel is noise

Three-step approach

Background identification and noise removal

Fully automatic gridding Finding margins Detecting blocks Gridding

Spot segmentation

Step 2.1 Finding margins

Step 2.2 Detecting blocks

Gap between rows

Gap between blocks

Block 1

Block 2

Disrupted background pixels while crossing signals

Continuous background pixels in gaps

Few signals in this area

More signals in this area

Step 2.3 Gridding

(C) Bounding-box witheligible width forcolumn detecting

(D) Bounding-box withneither eligible height nor width

(A) Bounding-box withboth eligible height and width

(B) Bounding-box witheligible height forrow detecting

Step 2.3 Gridding

Three-step approach

Background identification and noise removal

Fully automatic griddingSpot segmentation

Step 3 Spot segmentation

2))(( wbthth mmNNN where,

N is the total number of pixels which pre-labeled as signals

Nth is the number of pixels in the white class (>th)

mb is the mean of the ‘black’ class

mw is the mean of the ‘white’ class

To minimize the intra class, we want to find a threshold th to maximize the follow formula

Step 3 Spot segmentation

Nth = NAll pixels pre-labeled as ‘foreground’ are real signals.

Nth < NPart of the ‘foreground’ pixels belong to noise, inner holes, or outer rims. (Nth / N) ≤ φ

Pixels identified as white are considered as noise (Nth / N) > φ

Only pixels in the ‘white’ class is considered as real signals

Experimental results

Background removal and Noise elimination

(a) Before applying background removal and noise elimination method

(b) After applying background removal and noise elimination method

Experimental resultsSegmentation results

Experimental results Block boundary detection and gridding results

Block boundary detection 5 slides (48blocks for each slide) Recall value: 93% Precision value: 100%

Gridding 1 slide (48 blocks) Recall value: 99.97% Precision value: 100%

Experimental resultsSegmentation results

Conclusions

Our proposed method is a fully automatic and highly parallelizable method Handle uneven background and severe noise Detect block boundaries Generate grids Extract spots simply and effectively Highly parallelizable method

Thank you !!