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Automated Protein
Localization using Image
Analysis
Sankar Venkatraman
September 21st, 2004
AICIP
The Cell
Image source http://www.biologie.uni-hamburg.de/b-online/library/bio201/cellfrlife.html
Background
 DNA (De-oxyribonucleic acid)
 Information required by a living cell to exist resides
inside the nucleus of every cell.

These instructions tell the cell what role it is to play in
the body.

The instructions are in the form of a molecule called
DNA that act like a blueprint with a set of instructions.

DNA strand is made of letters which form words and
which in turn form sentences. Such sentences are
genes.
Background contd…
 GENE

They are the instruction manuals for the body.

They contain directions for building all the
proteins that make our body function.

Each DNA fragment is one gene and each
gene has a specific instruction to carry so as
to produce a protein.
Background contd…

PROTEINS

Proteins are responsible for every function of a cell and are
very small and are usually difficult to see even with the best
microscopes around.

A specific machinery inside the cell reads a gene and creates
an RNA every time there is a need to produce a protein.

RNA moves from nucleus to cytoplasm and where the protein
manufacturing machinery-Ribosome, reads the message and
produces a protein as per the specifications sent out by the
gene.

Thus to make one protein, we need a number of other highly
specialized proteins and thus the humungous number of
proteins.
Proteomics…??
 A “proteome” is defined as the total set of proteins
expressed in a given cell at a given time.
 Proteomics refers to the science and the process of
analyzing and cataloging all the proteins encoded by a
genome.
 A Protein is characterized by
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Structure
Sequence
Expression level
Activity
Location – pretty useful to understand its function
Location proteomics
Importance….
 Protein subcellular location essentially describes
the location within a particular cell type where
one finds a given protein.
 The organelle where the protein is located gives
a context for it to carry out its role.
 Each organelle provides a different biochemical
environment that influences the associations that
a protein may form and the reactions that it may
carry out. Thus the knowledge of such data could
be invaluable to us.
Motivation to use Image Analysis…
 The previous systems predicted the protein
localization with the help of fractionation,
Electron microscopy and Fluorescent
microscopy.
 These are found to be highly biased, time
consuming and inconsistent and thus expose
the vital need for automated approaches to
experimentally determine the sub-cellular
localizations.
Motivation contd…
 Imaging is information rich but has a poor throughput
by itself.
 Automated image analysis can improve the
throughput.
 The motivation is thus to find cells and quantify the
image based information.
 “Location” and “Temporal” proteomics will eventually
lead us to functional assignments.
Basic steps for Image Analysis in
Proteomics
 Epi-fluorescence microscopy, confocal


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microscopy and other fluorescence
imaging techniques
Correction of uneven illumination,
correction for camera response,
computational deconvolution and
background subtraction
Identification of single cells using
thresholding and other segmentation
procedures
Morphological features, texture
features and moment-based features
Pattern recognition, representative
image selection and other analysis
using support vector machines and
neural networks
Image Acquisition
Image Restoration
Image Processing
Feature Extraction
Pattern Analysis
Challenges…
 Segmenting required cells from a given
image against heavy background noise and
overlapping cells.
 Defining robust features aiding location
proteomics.
 Classification of subcellular locations using
pattern recognition techniques.
Current research standing
 Robust features that include morphologial,
texture and moment, have been defined.
 Various pattern classifiers have been
compared and the apt ones realized.
 Experiments conducted on CHO and HeLa
cells.
Our contribution…
 Obtain location proteomics with respect to
time.
 Realize better image processing
techniques that could suit multi-cell images
so as to obtain a better feature set.
 Use Atomic Force Microscopic (AFM)
images in the field of proteomics.
Atomic Force Microscope…
 AFM uses a very small cantilever to tap the sample’s
surface repeatedly. By shining a laser on this
cantilever, a detector can sense how the cantilever is
responding to the sample.
 It can find how high that point is, and how hard, soft
or sticky it is. As the cantilever slowly scans the
surface, it feeds all of the data is receives into a
computer.
 The computer compiles this array of point data into
an image that humans can understand. In this way,
images with resolutions in a nanometer scale can be
obtained, and as the technology matures this
resolution will only improve. In contrast, the upper
limit for a visible light microscope is 1 micron.
Segmentation…
 Thresholding algorithms:
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These caved in for images with high input
noise.
Were very biased and hardly consistent for
different images.
 Thus the need for a better segmentation
technique arises.

SNAKES…??!!
Snakes…
 Active contours, which were first developed
by, Kass, Witkin and Terzopoulos [1] are
mathematically energy minimizing splines.

Splines are mathematical functions used to interpolate or
approximate a finite sequence of data values.
 More intuitively, they are active shapes that
respond and move according to energy
values in an image and usually deform to fit
local minima and thus require appropriate
initialization.
 Snakes are defined as energy function and to find the
best fit between a snake and an object's shape, we
minimize the energy..
E internal : Internal spline energy caused by stretching and
bending.
E image : Measure of the attraction of the image features like
contours.
E constraint : Measure of external constrains either from higher
level shape information or user applied energy.
Classification
SNAKES
Implicit
Parametric
Embed the snake as a zero level set
of a higher dimensional function and
to solve the corresponding equation
of motion.
Consists of an elastic curve that
dynamically conforms to object
shapes in response to internal (elastic)
and external (image and constraint)
forces
Due to dimensional formulation
are not convenient for shape
analysis and user interaction
Easier to integrate image data,
initial estimate, desired contour
properties in a single extraction
process.
e.g.
Dual active contours,
Gradient vector fields
Gradient vector field (GVF) snake
 A traditional snake is a curve X(s) = [x(s),
y(s)], s [0, 1], that moves through the spatial
domain of the image so as to minimize the
energy function
 A snake that minimizes E must satisfy the
Euler equation
GVF contd….
 The GVF field is defined as
v(x,y) = ( u(x,y),v(x,y) )
that essentially minimizes

f is the edge image and alpha is the regularization factor
 The GVF is implemented by using the
following two equations
The all important edges…
 Since the GVF uses edge image to obtain gradients,
we would always prefer an image that would give us
“required” edges in a noise free environment.
 For this, a union of both the images with a spatial
threshold set by the user was used to obtain the
appropriate edge image.
 A deblurring filter was applied prior to the Gaussian
blur was applied so as to preserve edges and reduce
noise respectively.
Sobel edge
Canny edge
Edges…
Union
Union edge
Snakes implementation…
Snake
Input image
Initialization
The goal…!!
Feature Set
I know this…
It’s a XXX protein!!
Future work…
 Feature extraction from the segmented
images.
 Multi-cell segmentation.
 Understand the concept of snakes deeper so
as to exploit its usefulness.
 Devise a better de-noising algorithm so as to
get better edges.
Questions…???