abdominal ct liver parenchyma segmentation based on particle swarm optimization

Abdominal CT Liver Parenchyma Segmentation Based

on Particle Swarm Optimization

SRGE Workshop, Cairo University Conference Hall (28-November-2015)

Gehad Ismail Sayed

http://www.egyptscience.net

Overview

Introduction Problem Definition Motivation

Related Work Proposed Approache Results and Discussion Conclusion and Future Works

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Introduction

Problem Definition Liver cancer is one of the most leading death in the

world. Early detection and accurate staging of liver cancer is

considered and important issue Image segmentation is an important task in the image

processing field. Efficient segmentation of images considered important for further object recognition and classification.

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Introduction

Motivation Liver segmentation is essential step for diagnosis liver

disease Manual segmentation of Computed Tomography (CT)

scans are tedious and prohibitively time consuming Automatic Liver segmentation in CT image is a difficult

task due to:- Low level of contrast and blurry edges which characterize the CT

images Gray levels similarity between neighbor organs like spleen, liver and

stomach Variety of liver shape and size

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Related Work

Several approaches for liver segmentation have been proposed, which can be categorized based on the degree of automation:- Fully automatic

Most of these methods respond identically to different patients. They usually produce over segmentation and also give unsatisfied results

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 Approach

Preprocessing Phase

Image Resizing and Median Filter

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Particle Swarm Optimization


15 43 images are middle slice frontal images in JPEG format,

selected from a DICOM from different patients Image dimensions: 630x630 Image resolution: 72 DPI, and bit depth of 24 bits.

Dataset Description


Dataset Samples


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a) Original Image b) Median Filter Results c) Cluster-1 d) Cluster-2 e) Cluster-3


Results and Discussion

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a) Binarized Clustered Image b) Open Morphology Results

c) Image After Selecting Largest Region d) Close Morphology Results

e) Image After Filling HolesSRGE Workshop, Cairo University Conference Hall (28-November-2015)


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a) Gradient Image b) Gradient Image After Normalization

d) Image After Applying Watershed and e) Visualization of Extracted Liver Taking the Largest Region



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Parameter Value (s)Population Size 150

Number of Iterations 100.60.6255

02-2

w 0.4Number of Levels 3

PSO Parameters Settings

12maxXminXmaxVminV



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Authors Year AccuracyJeongjin et al. 2007 70%

Ruchaneewan et al. 2007 86%M. Abdallal 2012 84%Z. Abdallal 2012 92%M. Anter 2013 93%N. Aldeek 2014 87%

Proposed Approach 2015 94%

Comparison Between the Proposed Approach and The Previous Approaches



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Dice (%) Correlation (%) True Positive (%)Using Watershed 91.89 90.62 94.62

Without Using Watershed

89.12 87.94 90.23

Comparison Between Using Watershed in The Proposed Approach and Without Using It



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Dice (%) Correlation (%) True Positive (%) CPU Process Time in Seconds

2 78.80 76.51 71.71 50.263 91.89 90.62 94.62 56.664 87.98 86.83 93.36 62.395 80.48 80.62 88.44 75.69

Comparison Between The Results Obtained From Different Levels



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Dice (%) Correlation (%) True Positive (%)Active Contour 71.87 69.22 72.43

Global Threshold 81.34 79.41 81.19Proposed Approach 91.89 90.62 94.62

Comparison Between The Proposed Approach and The Other Approaches



Conclusion and Future Works

Conclusion The experimental results show that the proposed approach

gives better result compared with other approaches and obtained over all accuracy about 94% of good liver extraction.

These results from proposed approach can help for further diagnosis and treatment planning

Future Works Increase the number of CT images dataset to evaluate the

performance of the proposed approach Test new versions of PSO

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Thanks and Acknowledgement26


abdominal ct liver parenchyma segmentation based on particle swarm optimization

Health & Medicine