ns ws-liver-paper-ica hybrid segmentation approach based on neutrosophic sets and modified...
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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)
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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 (29-December-2015)
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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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Faculty of Engineering, Cairo University (29-December-2015)
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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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Faculty of Engineering, Cairo University (29-December-2015)
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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
Faculty of Engineering, Cairo University (29-December-2015)
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Faculty of Engineering, Cairo University (30-December-2015)
Dataset Samples
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a) Original Image b) Median Filter Results c) Histogram Equalization Results
Results and Discussion
Faculty of Engineering, Cairo University (29-December-2015)
Preprocessing Phase Results
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a) Truth Subset Image b) False Subset Image c) Indeterminate Subset Image
Results and Discussion
Faculty of Engineering, Cairo University (29-December-2015)
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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Faculty of Engineering, Cairo University (29-December-2015)
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
Results and Discussion
Faculty of Engineering, Cairo University (29-December-2015)
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Comparison Between Using Watershed in The Proposed Approach and Without Using It
Results and Discussion
Faculty of Engineering, Cairo University (29-December-2015)
84.0090.0096.00
Dice (%) Correlation (%) True Positive (%)
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Comparison Between The Proposed Approach and Other Approaches
Results and Discussion
Faculty of Engineering, Cairo University (29-December-2015)
Adap
tive T
hresho
ld
Region
Growing
Activ
e Con
tour
Global
Thres
hold
Propo
sed Ap
proach
04080
DiceJacardCorrelationTrue Positive
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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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Faculty of Engineering, Cairo University (29-December-2015)
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Thanks and Acknowledgement19
Faculty of Engineering, Cairo University (29-December-2015)