christopher mitchell the cooper union fluorescent microscopy, eigenobjects, and the cellular density...

Christopher MitchellThe Cooper Union

Fluorescent Microscopy, Eigenobjects, and the

Cellular Density Project

Christopher MitchellThe Cooper UnionStevens REU 2007


Overview

Previous Work: Cell Density & Fluorescent Microscopy

Outline of Project Methodology

How Eigenobjects Work

Applying Eigenobjects to Nuclear Density

The Future of the CDP

Note to any future presenters: most of the content is in the accompanying Slide Notes


Fluorescent Microscopy

Attach marker chemical to protein Take picture of tissue Analyze marker distribution and

concentration


Fluorescent Microscopy Uses

Examine tissue structure Identify malignant cellular growth Bind to nuclear protein to view

distribution of nuclei in tissue High density indicative of pre-malignant

growth


Example of Fluorescent Microscopy

Example from webpage analyzed for this presentation:


Cellular Attributes

Normal Cells Well-organized Moderate density Specific protein

distribution

Malignant Cells Chaotic

arrangement High density Different protein

distribution


Advantages of Fluorescent Microscopy

Less invasive Earlier detection More precise identification


The Future of Fluorescent Microscopy

More precise tagging of proteins to identify structures

Ability to tag multiple proteins to gather more information about each cell

Greater understanding of inter-cell and intra-cell structure as a cause and symptom of malignant cellular growth


Project Methodology

Application of some fluorescent microscopy methods to photographic microscopy

Primarily uses variance in cellular density and nuclear proportions between normal and malignant cells

Mechanical identification of suspect samples for further human or other analysis


Advantages ofPhotographic Microscopy Easier to mechanically classify Cheaper to acquire images Human-readable with minimal training


Disadvantages ofPhotographic Microscopy Less cellular detail Fewer unique indicators of normal or

malignant nature


Nuclear Identification Methods

Wavelet method Signal processing-based (mathematical) solution

Eigencell method Computer science (algorithmic) solution


The Signals Method

Increase image contrast Edge detection using wavelets Count nuclei and create density array Apply statistical analysis


The Algorithmic Method

Creating training set of eigennuclei Apply image space > eigennucleus space

> image space transform, find Mean Squared Errors

Identify and count cells Apply statistical analysis


Method Evaluation

For scope of project, algorithmic method chosen

Easier to code, easier to understand without a Signals background

More precise even though less efficient


Using Eigenobjects

To create a training array of eigenobjects, need to start with several training images.

All images must be the same dimensions Example training set:

Varied sizes and rotations, but all 24x24 pixel images


Using Eigenobjects 2

Next, all images packed from rectangles into rows

Eigenvectors created from each row and sorted by associated eigenvalues



In order to streamline the process, the outer product of each row is taken and packed

Yields square, symmetric matrix Each row multiplied by original image

produces one eigenobject



Trained set of eigenobjects is complete and packed into a single array for comparison

To compare an image to the training set, it must be converted to object space and back to image space.

Examples of eigenfaces, eigenobjects made from faces:



Results of image > object > image space transformations:

To determine if the image is the same type of object as training set, take Mean Squared Error (MSE) between input and output


Finding Objects in an Image

Method can be applied to find objects in a larger image

All possible subimages of training set dimensions taken, MSEs calculated

Threshold-filtered to find objects


Applications to Nuclear Density and the CDP

Using eigennuclei, center of all cells in microscope image can be found

Image broken into regions, number of cells in each region found

Statistical analysis to determine cancer presence


The Future of the CDP

Optimizations Multipass approach

Scaling/rotation

Further identification metrics


References Cytodiagnosis of Cancer Using Acridine Orange with Fluorescent Microscopy

(http://caonline.amcancersoc.org/cgi/reprint/10/4/118)

New Cell Imaging Method Identifies Aggressive Cancers Early (http://www.sciencedaily.com/releases/2006/03/060307085017.htm)

The Cellular Density Project (http://beta.cemetech.net/projects/item.php?id=1)

Eigenfaces Group – Algorithmics(http://www.owlnet.rice.edu/~elec301/Projects99/faces/algo.html)

Eigenfaces (http://www.cs.princeton.edu/~cdecoro/eigenfaces/)

christopher mitchell the cooper union fluorescent microscopy, eigenobjects, and the cellular density...

Documents