ct computer aided diagnosis system
TRANSCRIPT
CT Computer-Aided Diagnosis System
PRESENTED BY
ABDALLA MOSTAFA ABDALLA
Scientific Research Group in Egypt http://www.egyptscience.net
7 March 2015 - Zewail University for Science and Technology
SRGE Members
Group founder and chair: Professor Aboul Ella Hassanien
Scientific Research Group in Egypt
Agenda Introduction
Problem Definition
Objective
Liver and Medical imaging
Pre-processing
Segmentation
Proposed Approach
Experiments and Results
Conclusion
Introduction
◦ Liver is an important organ in humanbody.
◦ It may have different colors (darkblue cyst, dark brown - cirrhosis,yellow - fatty, green – billarycirrhosis)
◦ It is common to use ComputedTomography (CT) in Computer aideddiagnosis systems (CAD)
Problem Statement
Difficulties associated with liver image segmentation
◦ Liver has different shapes.
◦ Similarities to other organs (muscles, flesh, kidney, spleen).
◦ Similarity between Vessels and cyst.
Objective
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We aim to
• Enhance Region growing technique.
We have chosen
LiverCT
ImagesComputer
Manipulation
CADComputer-Aided Diagnosis System
Liver
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Why Liver?
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The statistics of liver diseases shows that• The ratio of virus C infection is 12.8 % in Egypt.• The ratio of virus C infection is almost 1.2% in
Europe.• 130 thousands people need liver transplantation
In Egypt.
Liver diseases
Cyst
Fatty Liver
Fibrosis
Billary Cirrhosis
Carcinoma
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Different diseases may have different colors
According to• Liver bible• Pathology Atlas• Oncology ref.
So, Image can help in diagnosis
Biopsy
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• It may puncture the lung.• It may fracture rib.• Liver bleeding.
• The worst of allthe sample might not represent the
lesion.
Biopsy has its limitations and risks
CT Image Slicing
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Slicing technique
Liver sliced imageCT machine moves through the abdomen and records the details of liver tissues
Proposed approach
1• Preprocessing
2• Segmentation
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The proposed approach has main two phases
Preprocessing
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The main objective of image preprocessing is to improve the quality of the image being processed by:
• Removing noise.
• Emphasizing certain features.
• Isolating regions of interests.
Liver Segmentation
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Liver segmentation depends on :
• The difficulty of the anatomy of liver.• Liver is surrounded by many organs, similar to its intensity as
spleen, stomach, and kidney.• The nature of liver tissues, and blood vessels.
.
Region Growing Segmentation
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Based on the growth of a homogeneous regionaccording to certain features as intensity, coloror texture.
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Now Let us go to the
Proposed Approach
Phases of Proposed Approach
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Segmentation
Region growing
Preprocessing
Using morphological operations
Connecting Ribs
Morphological operationsMorphological Operations are :-
◦ Structure element.
◦ Dilation.
◦ Erosion.
◦ Opening.
◦ Closing.
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Morphological operationsStructure element
has a shape of square, diamond and cross
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Morphological operations
DilationThe basic effect of the operator on a binary image is to gradually enlarge the boundaries (thicking) of regions of foreground pixels.
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Morphological operationsErosionThe basic effect of the operator on a binary image is to shrink(erode away ) the boundaries of regions of foreground pixels
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Morphological operationsOpening
It is an erosion followed by a dilation.
It can open up a gap between objects connected by a thin bridge of pixels.
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Morphological operationsClosing
is a dilation followed by erosion, it fills some gabs.
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Connecting ribs
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Using contrast stretching to emphasize the ribs boundaries. The ribs will be connected as follows:
Now the image is prepared for the next phase
Segmentation
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•It is partitioning an image into homogeneous regions with respect to intensity, or texture.
•Image segmentation methods can be categorized as • Edge-based methods (discontinuity )• Region-based methods (similarity)
Liver Segmentation
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So, there is a need to get
Separated Liver Regions of Interest
Proposed algorithm
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Experiments and results
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Cleaning image is a process of removing annotation and bed from the image
Experiments and results
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Preparation phase
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Similarity Index
Validation measure
Experiments and results
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Difference betweensegmented and annotated image .
Experiments and results
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Normal Region Growing vs Proposed approach
Experiments and results
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Using the proposed method showed that: The accuracy result using similarity index measure is (SI=91.2% ). The method could segment images that was difficult to segmented before.
Conclusion
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Testing proposed approach, with region growing showed that:
Normal Region growing has the result of 82% accuracy.Proposed approach has the result of 91.2% accuracy.
Future Work
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The future work would be the change of the approach of classification to use Bio-Inspired methodology to :-
• Eliminate the liver separation computational cost.• Generalize the approach for other organs as spleen
and stomach.