Interphase Cells Removal from Metaphase Chromosome Images Based on Meta-Heuristic Grey Wolf Optimizer

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

2


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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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.

4


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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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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Proposed Approach7


Preprocessing PhaseG-Band SelectionG-Band Image Resizing

Proposed Approach


9 40 metaphases were taken from rat bone marrow.

Image dimensions: 2592*1944

Image resolution: 96 DPI.

Dataset Description



Dataset Samples

11Results and Discussion


Preprocessing Phase Results

a) Original Image b) R-Band Imagec) G-Band Image d) B-Band Image



Image Clustering Phase Results

a) FFCM Results Image b) Cluster-1 Imagec) Cluster-2 Image d) Cluster-3 Image

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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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Results and Discussion

RBF Qudartic Linear0

20

40

60

80

100

AccuracyPrecisionSensetivityMisclassification RateSpecificity

Comparison Between Different Kernel Functions

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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



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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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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Thanks and Acknowledgement20
