interphase cells removal from metaphase chromosome images based on meta-heuristic grey wolf...
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Interphase Cells Removal from Metaphase
Chromosome Images Based on Meta-Heuristic GreyWolf OptimizerGehad Ismail Sayed
http://www.egyptscience.net
Faculty of Engineering, Cairo University (30-December-2015)
Overview
Introduction Problem Definition Motivation
Related Work Proposed Approach Results and Discussion Conclusion and Future Work
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Faculty of Engineering, Cairo University (30-December-2015)
Introduction
Chromosomes are basic building blocks of life where entire genetic of organisms organized and stored in the form of (DNA)
Karyotyping is considered a type of chromosomal analysis, It used to detect structural or numerical abnormalities among the chromosomes.
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Faculty of Engineering, Cairo University (30-December-2015)
Introduction
Problem Definition Presence of various unwanted noisy objects like stain
particles, dirt and interphases cell can highly decrease the efficiency of automatic karyotype.
Dirt and stain particles can be prevented by taking care in the slide preparation also they can be removed easily be using simple noise filtering or using thresholding technique.
It’s much difficult to prevent the occurrence of interphases.
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Faculty of Engineering, Cairo University (30-December-2015)
Introduction
Motivation Manual karyotype by cytogeneticist is a very
challenging task and very time-consuming.
Automatic interphase cells removal is very challenging task due to intensity level similarity between interphase cell, chromosomes and parts of background
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Faculty of Engineering, Cairo University (30-December-2015)
Related Work
Several approaches for interphase cells removal have been proposed, which can be categorized based on the degree of automation:- Fully automatic
Most of these approaches requires a prior knowledge of values of various parameters associated with the object of interest
i.e. Pixel ranking based segmentation Semi or interactive automatic
It requires a limited user intervention to complete the task. i.e. Snake model, Active contour, …
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Faculty of Engineering, Cairo University (30-December-2015)
Proposed Approach7
Faculty of Engineering, Cairo University (30-December-2015)
Preprocessing PhaseG-Band SelectionG-Band Image Resizing
Proposed Approach
Faculty of Engineering, Cairo University (30-December-2015)
9 40 metaphases were taken from rat bone marrow.
Image dimensions: 2592*1944
Image resolution: 96 DPI.
Dataset Description
Faculty of Engineering, Cairo University (30-December-2015)
Faculty of Engineering, Cairo University (30-December-2015)
Dataset Samples
11Results and Discussion
Faculty of Engineering, Cairo University (30-December-2015)
Preprocessing Phase Results
a) Original Image b) R-Band Imagec) G-Band Image d) B-Band Image
12Results and Discussion
Faculty of Engineering, Cairo University (30-December-2015)
Image Clustering Phase Results
a) FFCM Results Image b) Cluster-1 Imagec) Cluster-2 Image d) Cluster-3 Image
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Faculty of Engineering, Cairo University (30-December-2015)
Results and DiscussionClassification and Enhancement Results
a) Detected Region of Interestb) Final Extracted Chromosomes Imagec) Visualization of Extracted Chromosomes
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Results and DiscussionComparison of Segmentation Results Between Different Kernel
Functions
a) RBF Results b) Quadratic Results c) Linear Results
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Faculty of Engineering, Cairo University (30-December-2015)
Results and Discussion
RBF Qudartic Linear0
20
40
60
80
100
AccuracyPrecisionSensetivityMisclassification RateSpecificity
Comparison Between Different Kernel Functions
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Faculty of Engineering, Cairo University (30-December-2015)
Results and Discussion
Accur
acy
Percis
ion
Sens
etivi
ty
Misclas
sifica
tion R
ate
Spec
ificity
0.0040.0080.00
GWOGA
Comparison Between GWO and GA
17Results and Discussion
Faculty of Engineering, Cairo University (30-December-2015)
The System Performance of Different No. of Wolves in
Terms of Processing Time in Seconds
The System Performance of Different No. of Iterations in Terms of Processing Time in
Seconds
20 40 60 8001234
No. of Wolves
Elap
sed
Tim
e
5 10 15 20020406080
No. of IterationsEl
apse
d Ti
me
Conclusion and Future Work
Conclusion The experimental results show that the proposed
approach gives good chromosomes segmentation results and obtained over all accuracy about 94%.
These results from proposed approach can help for further diagnosis and treatment planning.
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Faculty of Engineering, Cairo University (30-December-2015)
Conclusion and Future Work
Future Work Increase the number of metaphase images dataset to
evaluate the performance of the proposed approach.
Extends the work for metaphase chromosomes image to be able to identify the genetic disease through counting the number of chromosomes in the image. Moreover, solve all the problem of the image that could prevent the system for counting such as the overlapping problem.
Test new version of swarms.
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Faculty of Engineering, Cairo University (30-December-2015)
Thanks and Acknowledgement20
Faculty of Engineering, Cairo University (30-December-2015)