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Transcript of Colloqium -jan09
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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
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 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.
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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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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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
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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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
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
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 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.
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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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
overcomplete dictionaries for sparse representations, IEEE Transactions on Image
Processing., November 2006, vol. 54, no. 11, pp. 43114322.
K-SVD algorithm
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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.
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
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
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
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.
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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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.
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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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.
.
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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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.
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
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
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 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 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
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
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 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.
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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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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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
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