ns ws-liver-paper-ica hybrid segmentation approach based on neutrosophic sets and modified...

A Hybrid Segmentation Approach Based on

Neutrosophic Sets and Modified Watershed: A Case

of Abdominal CT Liver ParenchymaGehad Ismail Sayed

http://www.egyptscience.net

Faculty of Engineering, Cairo University (29-December-2015)

Overview

Introduction Problem Definition Motivation

Related Work Proposed Approach Results and Discussion Conclusion and Future Work

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Introduction

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

world. Egypt occupies the fourth rank in the top highest

incidence of liver cancer in 2012 Image segmentation is an important task in the image

processing field. Efficient segmentation of images considered important for further liver disease diagnosis .

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Introduction

Motivation Manual segmentation of liver from Computed

Tomography (CT) scans is tedious and prohibitively time consuming

Automatic Liver segmentation in CT image is a difficult task due to:- Low level contrast and blurry edges of the image which

characterize the CT images Gray levels similarity between liver and neighbor organs like

spleen 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 approaches 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, Median Filter and Histogram

Equalization

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7 30 middle slice frontal images in JPEG format, selected

from a DICOM from different patients are used. 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) Histogram Equalization Results

Results and Discussion


Preprocessing Phase Results

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a) Truth Subset Image b) False Subset Image c) Indeterminate Subset Image



Conversion to Neutrosophic Domain Phase Results

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a) Adaptive Threshold Result b) Filling Holes Resultc) Removing Small Objects Result d) Open and Close Morphology Operators Result

Results and DiscussionPost-processing Phase (Morphology Operators) Results

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a) Normalized Gradient Image b) After Removing Local Maxima Resultc) Close Contour Result d) Modified Watershed Result

Results and DiscussionPost-processing Phase (Modified Watershed) Results

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a) Original Image b) Median Filter Result c) Histogram Equalization Resultd) False Subset Image e) Truth Subset Image f) Indeterminate Subset Imageg) Adaptive Threshold Result h) Extracted Liver using Proposed Approach and GT

Results and DiscussionComplete Example of Neutrosophic Sets and Watershed Algorithm

for Liver Segmentation

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a) Traditional Watershed on Gradient Image b) Segmentation Based on Euclidean Distance Transformation c) Cityblock Distance d) Chessboard Distance e) Quasi-Eculidean Distance f) Segmentation Based on The Proposed Approach

Results and DiscussionComparison Between The Proposed Watershed Algorithm and The

Traditional Ones

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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 95%

Comparison Between the Proposed Approach and The Previous Approaches



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



84.0090.0096.00

Dice (%) Correlation (%) True Positive (%)

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Comparison Between The Proposed Approach and Other Approaches



Adap

tive T

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Region

Growing

Activ

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Global

Thres

hold

Propo

sed Ap

proach

04080

DiceJacardCorrelationTrue Positive

Conclusion and Future Work

Conclusion The experimental results show that the proposed approach

gives better result compared with other approaches and obtained over all accuracy about 95% of good liver extraction.

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

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

performance of the proposed approach Extend this work to tumor extraction and further liver

diagnosis.

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Thanks and Acknowledgement19


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