Colloqium -jan09

8/7/2019 Colloqium -jan09

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Pattern recognition for

diagnostic pathology

Presenter: Shobana Rajendran

Supervisor: Dr Hamzah Arof

Co-Supervisor: Assoc. Prof. Dr Fatimah Ibrahim

January 2009


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Presentation Outline

1. Literature Review2. Progress Report


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Literature Review

1. Julien Mairal, Michael Elad, and Guillermo Sapiro.Sparse Representation for Color ImageRestoration. IEEE Transactions on Image Processing, January 2008;V. 17,1:pp. 53-69

2. M. Aharon, M. Elad, and A. M. Bruckstein, The K-SVD: An algorithm for designing of

overcomplete dictionaries for sparse representations, IEEE Transactions on Image

Processing., November 2006, vol. 54, no. 11, pp. 43114322.

3. M. Elad and M. Aharon, Image denoising via sparse and redundant representations overearned dictionaries, IEEE Transactions on Image Processing., December 2006,vol. 15, no. 12,

pp. 37363745.

4. B. Matalon, M. Elad, and M. Zibulevsky, Improved denoising of images using modeling of the

redundant contourlet transform, in Proc. SPIE Conf. Wavelets, Jul. 2005, vol. 5914.

5. Jamie Shotton, Andrew Blake, Roberto Cipolla. Multiscale Categorical Object Recognition

using Contour Fragments. IEEE Transactions on Pattern Analysis and Machine Intelligence,

July 2008; v.30,7:pp. 1270-1282


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Review: 11. Julien Mairal, Michael Elad, and Guillermo Sapiro.Sparse Representation for

Color Image Restoration. IEEE Transactions on Image Processing, January

2008;V. 17,1:pp. 53-69




3. M. Elad and M. Aharon, Image denoising via sparse and redundant representations over

earned dictionaries, IEEE Transactions on Image Processing., December 2006,vol. 15, no.

12, pp. 37363745.

4. B. Matalon, M. Elad, and M. Zibulevsky, Improved denoising of images using modeling of

the redundant contourlet transform, in Proc. SPIE Conf. Wavelets, Jul. 2005, vol. 5914.



July 2008; v.30,7:pp. 1270-1282


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Julien Mairal, Michael Elad, and Guillermo Sapiro.Sparse Representation for Color

Image3- Restoration. IEEE Transactions on Image Processing, January 2008;V.

17,1:pp. 569

1. To develop an algorithm for removing the white Gaussian noise for color images(

vector-valued images) and by also using K-SVD for learning the dictionary.

2. To describe the extension of the denoising algorithm for proper handling of non-

homogenous noise, with respect to demosaicing an impainting.

BACKGROUND:

The authors have developed an algorithm called K-SVD algorithm to learn a

dictionary that leads to sparse representation on training signals and followed by that,

they have developed another algorithm for the removal of additive white gaussian

noise with grayscale images. As a next step to that, they wanted to extend it to colorimages, that is discussed in this paper.

OBJECTIVE:


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Image Restoration. IEEE Transactions on Image Processing, January 2008;V.

17,1:pp. 53-69

Three sections

1. Example-based denoising methods

2. K-SVD-based grayscale image denoising algorithm

3. Novelties:

1. extension to color images

2. handling color artifacts

3. treatment of non-homogenous noise, along with its relation to demosaicing

and impainting

4. Sparse color image representation

1. denoising of color images

2. extension to non-homogenous noise

3. color image impainting4. color image demosaicing

METHODOLOGY:


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Slide 6

R1 Shobana, 1/19/2009


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Image Restoration. IEEE Transactions on Image Processing, January 2008;V.

17,1:pp. 53-69

RESULTS:

1. Different sizes of atom matrices were considered for training the dictionaries :

5x5x3, 6x6x3, 7x7x3 and 8x8x3

2. 200,000patches were taken from a database of 15000 images with patch

sparsity parameter L=6 ( 6 atoms in the representation).

3. Trained each dictionary with 600 iterations

4. Database for image is Label me.

Result obtained by applying our algorithm with 773 patches on the

mushroom image where a white Gaussian noise of

standard deviation = 25 has been added. (a) Original. (b) Noisy. (c)Denoised Image

CONCLUSION:

This research paper is worthwhile and ideas can be imbibed for our research.


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Review: 2

1. Julien Mairal, Michael Elad, and Guillermo Sapiro.Sparse Representation for Color Image

Restoration. IEEE Transactions on Image Processing, January 2008;V. 17,1:pp. 53-69

2. M. Aharon,M. Elad, and A. M. Bruckstein, The K-SVD: An algorithm for designing of




earned dictionaries, IEEE Transactions on Image Processing., December 2006,vol. 15, no.

12, pp. 37363745.

4. B. Matalon, M. Elad, and M. Zibulevsky, Improved denoising of images using modeling of

the redundant contourlet transform, in Proc. SPIE Conf. Wavelets, Jul. 2005, vol. 5914.



July 2008; v.30,7:pp. 1270-1282


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M. Aharon, M. Elad, and A. M. Bruckstein, The K-SVD: An algorithm for designing

of overcomplete dictionaries for sparse representations, IEEE Transactions on

Image Processing., November 2006, vol. 54, no. 11, pp. 43114322.

OBJECTIVE:

To develop K-SVD algorithm to create an adaptive dictionary that can describe content

of the image effectively, by clustering sparse representations by K-means algorithm.

BACKGROUND:

1. Pursuit algorithms are those, that can decompose signals (sparse) with respect to a

given dictionary.

2. Designing dictionaries can be done by selecting one from a set of linear transforms or

by adapting the signal to a set of training signals.

METHODOLOGY:

The authors proposed a novel algorithm for adapting dictionaries in order to achievesparse signal representation. Given a set of training signals, we seek the dictionary that

leads to the best representation for each member in this set, under strict sparsity

constraints. We present a new methodthe K-SVD algorithmgeneralizing the K-

means clustering process. K-SVD is an iterative method that alternates between sparse

coding of the examples based on the current dictionary and a process of updating the

dictionary atoms to better fit the data. The K-SVD algorithm is flexible and can work withany pursuit method.


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M. Aharon, M. Elad, and A. M. Bruckstein, The K-SVD: An algorithm for designing of



K-SVD algorithm


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M. Aharon, M. Elad, and A. M. Bruckstein, The K-SVD: An algorithm for designing of



RESULTS:

The authors presented the K-SVD algorithm for designing an overcomplete

dictionary that best suits a set of given signals, giving a sparse representation/each.

The authors have also shown how to interpret it as a generalisation of K-meansclustering and also demonstrated in both synthetic and real image tests.

A collection of 500 random blocks that were used fortraining, sorted by their variance.

(a) The learned dictionary. Its elements are sorted in an ascendingorder of their variance and stretched to maximal range for display

purposes. (b) The overcomplete separable Haar dictionary and (c)the overcomplete DCT dictionary are used for comparison.

The root mean square error for 594 new blocks with missing pixelsusing the learned dictionary, overcomplete Haar dictionary, andovercomplete DCT dictionary.


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Review: 3






3. M. Elad and M. Aharon, Image denoising via sparse and redundant representations

over earned dictionaries, IEEE Transactions on Image Processing., December

2006,vol. 15, no. 12, pp. 37363745.





July 2008; v.30,7:pp. 1270-1282


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M. Elad and M. Aharon, Image denoising via sparse and redundant representations


2006,vol. 15, no. 12, pp. 37363745.

OBJECTIVE:

To address the classic image denoising problem by using the specific sparse and

redundant representation of signals over trained dictionaries.

METHODOLOGY:

1. From local to global bayesian reconstruction

A. Sparseland model for image patches

B. From local analysis to global prior

C. Numerical solution

2. Example-based sparsity and redundancy

A. Training o the corpus of image patches

B. Training on the corrupted image


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2006,vol. 15, no. 12, pp. 37363745.

.

The overcomplete DCT dictionary (left).

The trained dictionary for Barbara with = 15, after10 iterations

Zoom views of the denoising results for the image Barbara.

Original Image Noisy Image (24.6 dB, =15) Denoised Image Using

Trained Dictionary (32.39 dB)


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2006,vol. 15, no. 12, pp. 37363745.

In this set of experiments, the dictionary used was of size 64 x256,designed to

handle image patches of size 8 x8 pixels (n= 64, k= 256). Every result reported

is an average over 5 experiments.

RESULTS:

Comparison between the three presented methods (overcomplete DCT, global trained dictionary, and adaptive

dictionary trained on patches from the noisy image)


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Review: 4







earned dictionaries, IEEE Transactions on Image Processing., December 2006,vol. 15, no. 12,

pp.37363745.

4. B. Matalon, M. Elad, and M. Zibulevsky, Improved denoising of images using modelingof the redundant contourlet transform, in Proc. SPIE Conf. Wavelets, Jul. 2005, vol.

5914.

5. Jamie Shotton, Andrew Blake, Roberto Cipolla. Multiscale Categorical Object Recognition using

Contour Fragments. IEEE Transactions on Pattern Analysis and Machine Intelligence, July

2008; v.30,7:pp. 1270-1282


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OBJECTIVE:

To prove that redundancy improves the denoising results

To understand and accept that taking coefficients dependencies into account is

helpful.

B. Matalon, M. Elad, and M. Zibulevsky, Improved denoising of images using modeling

of the redundant contourlet transform, in Proc. SPIE Conf. Wavelets, Jul. 2005, vol.

5914.

The main advantage of contourlet transform over other geometrical representationslike curvelet is that its relatively simple and efficient wavelet-like implementation using

iterative filter banks. Due to its structural resemblance with the wavelet transform,

many image processing tasks applied on wavelets can be seamlessly adapted to

contourlets.

BACKGROUND:


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B. Matalon, M. Elad, and M. Zibulevsky, Improved denoising of images using

modeling of the redundant contourlet transform, in Proc. SPIE Conf. Wavelets, Jul.

2005, vol. 5914.

METHODOLOGY:

Gaussian Scale Mixture model for contourlets

The Bayesian Least Squares Gaussian Scale Mixture (BLS-GSM) is based on

statistical modelling of the coefficients of a multiscale oriented frame, specifically theSteerable Wavelet Transform, but can be applied to other transforms as well.


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B. Matalon, M. Elad, and M. Zibulevsky, Improved denoising of images using

modeling of the redundant contourlet transform, in Proc. SPIE Conf. Wavelets, Jul.

2005, vol. 5914.

RESULTS: -- RETRIEVAL


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Review: 5







earned dictionaries, IEEE Transactions on Image Processing., December 2006,vol. 15, no. 12,

pp.37363745.



5. Jamie Shotton, Andrew Blake, Roberto Cipolla. Multiscale Categorical Object

Recognition using Contour Fragments. IEEE Transactions on Pattern Analysis andMachine Intelligence, July 2008; v.30,7:pp. 1270-1282


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This

Jamie Shotton, Andrew Blake, Roberto Cipolla. Multiscale Categorical Object

Recognition using Contour Fragments. IEEE Transactions on Pattern Analysis and

Machine Intelligence, July 2008; v.30,7:pp. 1270-1282

OBJECTIVE:

To

BACKGROUND:

M

ETHODOLOGY:


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Experiments & Results:


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Pattern recognition for

diagnostic pathology

1.Literature Review

2. Progress Report


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Progress Report

OBJECTIVE:

METHODOLOGY:

To explore and experiment basic image processing techniques on the leukemia

cells for further analysis

The basic image processing techniques that were processed using ImageJ

software are:

1. Segmentation by K-means clustering

2. Feature extraction

3. Granulometry for clusters an OPEN

4. Gray level co-occurrence matrix for texture analysis


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Progress Report

Segmentationis a process of identifying the objects, here nucleus and cytoplasm

from the background

Feature extraction differentiates cytoplasm, nucleus & nucleolus

Segmentation Feature extraction

Courtesy: Image J software


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Granulometry, by etymology, refers to the measurement of granularity and here it is to

extract size distribution from grayscale images.

GLCM is a method to compute measures of texture of the image or intensity variation.

Progress Report


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Progress Report

RESULT AND DISCUSSION:

The image processing techniques has given some idea on the basic available

techniques.

With the available results ( both visual and numerical), we can explore other

techniques to find out which gives optimal ouput.

A book titled Computer Imaging: Digital Image Analysis and Processing ,

Scott E Umbaugh comes with the theory as well as practical approach

accompanied with software CD. This book can be of immense guidance .


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Timeline October 2008

Week 1 2 3 4

Day M T W T F M T W T F M T W T F M T W T F

Literature review

ResearchMethodolgy

Image processing

segmentation and featureanalysis

Reading books

1. About research2. Image Processing

Data Collection

Meeting the Hematologist

System development


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Thank you for kind attention

Documents

Colloqium -jan09