P53 Image Analysis

Norut IT, Postboks 6434
ResearchPark Tromsø
e-mail: werner.van.belle@itek.norut.no
7-8 February 2005

Werner Van Belle & Kjell-Arild Høgda

Overview

A: Gel Artefacts

B: Image Processing

C: Brainstorm

Artefacts

Artefacts

Inconsistencies in brightness

Blurred images

White Noise

Inverted Images

Ladders/Borders/Lanes

Rotations/Translation & Warps

Washing/Drying

Presence of Scratches/Bubbles/Dots

Resolution

Artefacts: Brightness

Artefacts: Blur

Artefacts: Inversion

Artefacts: Noise

camera ?

index swapping ?

Artefacts: Ladders

Artefacts: Borders

Top border ?

Artefacts: Lanes

Artefacts: Rotation

Artefacts: Translation

Artefacts: Zoom

is relatively consistent !

Artefacts: Non Lineairity

Artefacts: 'Washing'

Artefacts: 'Drying'

Artefacts: Scratches

Artefacts: Bubbles

Artefacts: Dots

Wanted ?

Artefacts: Resolution

poor x/y resolution

poor grey value resolution, often only 16 colors

Artefacts: Clipping

Processing

Step 1: Manual Processing

Remove borders by selecting the appropriate area

Invert images where necessary

Rotate those who are clearly not horizontal

Step 2: Denoising

Tried tresholding, smoothing, spectrum baseline removal, wavelet based denoising, image regularization based on local image variations, non lineair filters...

Afterwards: normalization

Step 2: Denoising

Good results

However

local distribution might change

certain areas become white, due to initial clipping

parameters are not necessarily the best ones

Step 3: Gel Comparison

Before measuring the presence of different spots in the gels, we need to know

where to find them

which ones are the same

Step 3: Similarity [#1]

Idea: Similarity between two gels is based on autocorrelation and cross-correlation

Image is converted to a translation independent representation

Step 3: Similarity [#1]

Measuring the difference between two images results in a value

Lead to similarity matrix

Used to cluster the images

Step 3: Similarity [#1]

Poor Result

Cluster seems to be based on the technique used

dependent on resolution and gamma correction

Local 'form' of the spots play an important role

clipped holes

spots resulting from noise

Does not give a satisfying answer to the relation between the different spots

still too much different spots coming from dots, bubbles, scratches...

Takes into account the 'lanes'

Step 3: Similarity [#2]

Idea: find best translation to match two gels

Cluster based on energy difference

Step 3: Similarity [#2]

Poor result due to fuzzy neighborhood

Does not tell us where to look for a spot

Step 3: Similarity [#3]

Idea: overlay all images and manually mark the regions of interest, based on a mother image

will help in removing all other information such as dots, bubbles, lanes, ladders, scratches and residual washing/drying effects

Step 3: Similarity [#3]

Is better

Step 3: Similarity [#3]

But depends highly on mother image

Step 3: Similarity [#4]

Idea: overlay all images and manually mark the regions of interest, based on a mother image

will form the mask of 'interest'

based on the full one-one similarity matrix

requires a function minimalisation algorithm

Step 3: Similarity [#4]

Step 3: Similarity [#4]

Different proteins ?
Two lanes ?
Scale problems ?

Step 3: Similarity [#4]

Possibilities...

2005-02-04T10:08:41