INTELLIGENT COMPUTER AIDED DIAGNOSIS SYSTEM FOR LIVER FIBROSIS

Walaa Hussein Ahmed El-Masry

Cairo University - Faculty of Computers & Information

Dept. of Information Technology

Scientific Research Group in Egypt

http://www.egyptscience.net

Supervisor: Professor Aboul Ella Hassanien

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Agenda

Introduction Epidemiology Motivations Scope of work & proposed solutions Liver Fibrosis CAD Systems Medical Imaging Proposed CAD system for liver fibrosis Intelligent CAD system for liver fibrosis Features Extraction Comparative analysis & Evaluation measures Conclusion Future work

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Introduction

Primary malignant liver tumors, including hepatocellular carcinoma (HCC), cause 1.25 millions death per year worlwide. HCC is prevalent in Asia and Africa because of the presence of a large subclinical population with hepatitis C virus infection.

Additionally, during the last two decades,the mortality rate from primary liver cancer is reported to have increased by 41%Although globally liver cancer is ranked 9th as the cause of death due to organ cancer, it is ranked from first to third in many Asian countries.

So, a lot of research efforts have been directed in the field of ‘Medical Image Analysis’ with the aim to assist in diagnosis and clinical studies.

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Epidemiology

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Motivation

A lot of research efforts have been directed in the field of Medical Image Analysis with the aim to assist in diagnosis and clinical studies.

Imaging has become an important component in different fields of biomedical studies and clinical practice. Biologists study cells and generate various data sets, radiologists identify and quantify tumors from MRI and CT images. Analysis and interpretation of these different types of images needs sophisticated computerized quantification and visualization methods.

Until now, the major challenge for radiologists in imaging liver cancer has been the characterization of small cirrhotic nodules smaller than 2 cm in diameter.

Computer algorithms for the delineation of regions of interest is a key component assisting and automating specific radiological tasks. They are of great importance in biomedical imaging applications like tissue volume quantification, diagnosis, localization pathology, study of anatomical structures, treatment planning, partial volume correction of functional imaging data and computer integrated surgery.

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Scope of work & Proposed solutions

This thesis concerns the study and development of different approaches to help physicians via providing important information for surgical planning and early disease detection and analysis.

Developing a CAD system for liver fibrosis based on N-cut and K-Means segmentation algorithms for medical images to solve different medical image segmentation problems.

Developing an intelligent CAD system for liver fibrosis based on invasive weed optimization techniques for solving different medical image segmentation problems.

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Liver Fibrosis

Stages of Liver Fibrosis:

Stage 0: No Fibrosis

Stage 1: Portal Expansion with fibrosis

Stage 2: Bridging fibrosis

Stage 3: Marked bridging fibrosis (early cirrhosis)

Stage 4: Definite Cirrhosis with < 50 %

Stage 5: Definite Cirrhosis with > 50 %

Challenges: distinguish between the late fibrosis stage and tumor.

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CAD Systems

Recently, CAD has become a major research subject in medical imaging and diagnostic radiology. Many different types of CAD schemes are being developed for the detection and/or characterization of lesions in various tissues using medical imaging, including conventional projection radiography, CT, MR imaging, and US.

The basic concept of CAD is to provide computer output as a second opinion to assist radiologists’ image interpretations by improving the accuracy and consistency of radiologic diagnosis and also by reducing the image-reading time.

In recent years, considerable efforts have been made in computer-aided diagnosis (CAD) using medical images to improve a clinician's confidence in the analysis of medical images. Evaluation of medical images by a clinician is qualitative in nature and may vary from person to person.

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Medical Imaging

Medical imaging comprises different imaging

modalities and processes to image human

body for diagnostic and treatment purposes

and therefore has an important role in the

improvement of public health in all

population groups.

allowing doctors to find disease earlier and

improve patient outcomes.

Types of Image Modalities:

Computed Tomography (CT)

Magnetic Resonance Imaging (MRI)

Positron Emission Tomography (PET)

Ultrasound

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CT vs. MRI scan

CT scans are a specialized type of x-ray

MRI uses a magnetic field with radio frequencies introduced into it.

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CT vs. MRI scan

Liver MRI Liver CT

Advantages

Better Tissue Contrast Fast

Higher Sensitivity Readily available

Safer Contrast Less Costly

Disadvantages

Limited availability Nephrotoxic Contrast

Longer examination time Axial only

Higher Cost Less Sensitivity

CT vs. MRI scan12

In case of liver imaging, CT is the most commonly used imaging technique for evaluation of hepatic lesions.

Most radiologists and many referring physicians have a relatively high degree of confidence in looking at CT images.

CAD Frame Work

CAD System for Liver Fibrosis

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Liver CT ImageLiver CT Image

Pre-ProcessingPre-Processing

SegmentationSegmentation

Feature Extraction

Texture, Morphological

Feature Extraction

Texture, Morphological

Liver DiseaseLiver Disease

N-Cut K-Means

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Liver CT image Pre-Processing Preprocessing images commonly involves removing low-frequency background

noise, normalizing the intensity of the individual particles images, removing reflections, and masking portions of images.

The principal purpose of image enhancement is to Enhance the visual appearance of images and Improve the manipulation of datasets

During contrast adjustment, the intensity value of each pixel in the raw image is transformed using a transfer function to form a contrast-adjusted image.

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Segmentation Techniques

Image Segmentation is the process of partitioning a digital Image into multiple segments to simplify or change the representation of an image into something that is more meaningful Image segmentation algorithms play a vital role in numerous biomedical imaging application.

we applied two segmentation algorithms based on clustering and graph partitioning methods, the first algorithm is k-means which is based on clustering method, and the second one is normalized cut which is based on graph partitioning to segment samples of liver images.

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Normalized Cut

Normalized cut models the image into a graph. It models each pixel of the image as a node in the graph, and set an edge between two nodes if there are similarities between them.

Figure : (a) is the original image, and in (b) this image has been modeled as a graph: each pixel as a node, and a pair of nodes have an edge only if their distance is equal to 1. Edges with blue color mean weak similarities, while edges

with red color mean strong similarities.

The normalized cut is composed of two steps: similarity measurement and normalized cut process.

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Normalized Cut

The purpose of the first step is to compute the similarity between pixels and this value is set as the weight on the edge. In order to model all the similarities of an image, all pairs of pixels will contain an edge, which means if an image contains N pixels, there will be totally (N -1)N/2 edges in the corresponding graph. This kind of graph is called ”complete graph” and needs a large memory space.

(a) Fully connected Graph, p,q link between every pair of pixels, cost cpq for each link measures similarity (b) Segmentation by graph cuts, set of links whose removal makes a graph disconnected

To simplify the problem, sometimes we set edges between two nodes only when their distance is smaller than a specific threshold.

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Normalized Cut

An example for modeling an image into a graph

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Normalized Cut

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Experimental Results (N-Cut)

Figure : Normalized cut segmented images results: (a) Original image, (b) Pre-processed image, (c) Normalized clustered cuts, (d) Normalized affected part segmented

results

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K-Means Clustering

K-means clustering is simply to group the objects into K number of groups based on features, K is positive integer number.

The algorithm follows a way to classify a given data set through a certain number of clusters.

The main idea is to assign k-centroids for each cluster; the better way to select k is to place them far away from each other as much as possible.

The next step is to take each point belonging to a given data set and associate it to the nearest centroid

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K-Means Clustering algorithm

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Experimental Results (K-means)

Figure : K-means segmented images results (liver CT samples): (a) Original images, (b) Pre-processed image, (c) K-means clustering results, (d) K-means affected

part segmented results

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Published Papers

Walaa H. El-Masry et al., Graph Partitioning based Automatic

Segmentation Approach for CT Scan Liver Images, IEEE Federated

Conference on Computer Science and Information Systems, 9-12 Sept.,

Wroclaw, Poland, pp. 205-208, 2012.

Walaa H. El-Masry et al., Performance Evaluation of Computed

Tomography Liver Image Segmentation Approaches. The IEEE

International Conference on Hybrid Intelligent Systems (HIS2012). Pune.

India. 4-7 Dec. 2012, pp. 109 – 114

CAD Frame Work

Intelligent CAD System for Liver Fibrosis

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Liver CT ImageLiver CT Image

Pre-ProcessingPre-Processing

SegmentationSegmentation

Feature Extraction

Texture, Morphological

Feature Extraction

Texture, Morphological

Liver DiseaseLiver Disease

Invasive Weed Optimization

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Invasive Weed Optimization (IWO)

Recently, there has been considerable attention for using algorithms which are inspired from natural processes and/or events in order to solve optimization problems.

The term ”weed” is referred to plants whose invasive habits of growth and reproduction serve as a threat to the cultivated plants.

IWO is a population-based algorithm that replicates the colonizing behavior of weeds. The basic characteristic of a weed is that it grows its population within a specified area, which can be substantially large or small.

Initially a specific number of weeds are randomly distribute over the entire search space. These weeds will produce seeds according to their fitness value. The least fit weed produces the lowest number of seeds and the most fit weed produces the highest number of seeds.

The number of seeds produced by each weed varies linearly according to the fitness value.

The total number of weeds and seeds are maintained constant after the population size is reached by eliminating the weaker weeds.

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Invasive Weed Optimization (IWO)

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Invasive Weed Optimization algorithm (IWO)

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We present the IWO for liver image clustering. Invasive Weed Optimization Algorithm, it's an evolutionary meta heuristic algorithm is applied for automatically clustering image without any prior information.

The fitness function various according to each problem. The fitness function used in our algorithm is K-Means Objective function :

Search the smoothed compact cluster. Aims to minimizing an objective function that is defined as:

Which expresses the distance measure between a data point and the cluster center, and an indicator of the distance of the n data points from their respective cluster canters.

Invasive Weed Optimization algorithm (IWO)

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• (Smax) : is the maximum of the population size.• (Smin) : is the minimum of the population size.• The number of clusters that used in our samples and the number of

iterations. • We experimented the samples with 10 iterations, 15 iterations and 50

iterations.

Invasive Weed Optimization algorithm (IWO)

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Experimental Results (IWO)

Figure : IWO images results: (a) Original images, (b) IWO clustering with No. ofiterations=10, (c) IWO clustering with No. of iterations=15, (d) IWO clustering with

No. of iterations=50, (e) IWO affected part segmented results

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Published Paper

Walaa H. El-Masry et al. , Automatic Liver CT Image Clustering based on Invasive Weed Optimization Algorithm, International Conference on Engineering and Technology (ICET 2014), 19 -20 April.

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Features Extraction

Shape based Features

• Area• Perimeter• Circularity• Irregularity• Shape Index

Intensity based Features

• Mean• Variance• Standard Division• Median• Skewness• Kurtosis• Range• Pixel Orientation

Texture based Features

• Contrast• Correlation• Entropy• Energy or Uniformity• Cluster Shade• Sum of square Variance• Inverse difference Moment• Sum Average: Mean• Sum Variance• Sum Entropy• Difference Variance• Difference Entropy• Inertia• Cluster Prominence

Types of FeaturesTypes of Features

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Features ExtractionIntensity/Texture-based features

• Mean: The mean defines the average level of intensity of the image or texture, it's a measure of brightness.

• Standard Deviation: measure of contrast.

• Orientation: the angle (in degrees ranging from -90 to 90 degrees) between the x-axis and the major axis of the ellipse that has the same second-moments as the region.

• Entropy: Entropy is defined as a measure of uncertainty in a random variable, it's a measure of randomness,

Intensity Texture

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Features ExtractionShape-based features

• Area: represents the actual number of pixels in the region.

• Perimeter: The perimeter is defined as the total pixels that constitutes the edge of the object. Perimeter can help to locate the object in space and provide information about the shape of the object.

• Solidity: represents the proportion of the pixels in the convex hull that are also in the region.

• Circularity: is the ratio of the area of the shape to the area of a circle having the same perimeter.

Shape-based

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Features Extraction (N-Cut)

Table : Features of Normalized Cut segmentation results: Liver Samples

)LS1,LS2,LS3,LS4,LS5 and LS6)(number of connected objects =2 for LS1 and 1 for

other samples(

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Features Extraction (K-means)

Features of K-Means segmentation results: Liver Samples

)LS1,LS2,LS3,LS4,LS5 and LS6)(number of connected objects =2 for LS1 and 1 for other samples(

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Features Extraction (IWO)

Features of Invasive Weed Optimization algorithm results: Liver Samples

)LS1,LS2,LS3,LS4,LS5 and LS6)(number of connected objects =2 for LS1 and 1 for

other samples(

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Features Extraction (Ground Truth)

Features of manual segmentation results: Liver Samples

)LS1,LS2,LS3,LS4,LS5 and LS6)(number of connected objects =2 for LS1 and 1

for other samples(

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Evaluation Measures

In Medical research, we use supervised evaluation which is widely used. It computes the difference between the ground truth and a segmentation result using a given evaluation metric.

we used the manual segmentation to reflect the ground truth. Furthermore, we evaluated segmentation algorithms by comparing the result from a segmented image against the result from a manual segmented, which is often referred to as a gold standard or ”ground truth”.

The degree of similarity between the manual segmented and machine segmented images reflects the accuracy of the segmented image.

Evaluation measures are introduced through some parameters such as: Entropy, Mean and Standard deviation.

Accuracy = ∑ CSP/TNP

Where CSP is the correct segmented pixels in ith object and TNP is the total number of pixels in ith object.

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Evaluation Measures

Evaluation measures of N-Cut segmentation results: Liver Samples (LS1, LS2, LS3, LS4, LS5 and

LS6)

Evaluation measures of K-Means segmentation results: Liver Samples (LS1, LS2, LS3, LS4, LS5 and LS6)

N-Cut Evaluation Measures K-Means Evaluation Measures

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Evaluation Measures

Evaluation measures of manual segmented results: Liver Samples (LS1, LS2, LS3, LS4, LS5 and LS6)

Evaluation measures of IWO segmentation results: Liver Samples (LS1,LS2, LS3, LS4, LS5 and LS6)

IWO Evaluation Measures Ground Truth Evaluation Measures

Evaluation Measures43

Figure : ROI segmented liver CT images results: (a) Original image, (b) Desiredmanual segmented region, (c) Normalized affected part segmented results, (d) K-Means

affected part segmented results, (e) IWO affected part segmented results

LS1, LS2 and LS3 LS4, LS5 and LS6

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Comparative Analysis

Table : Accuracy Results for IWO

Table : Accuracy Results for N-Cut

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Comparative Analysis

Table : Accuracy Results for IWO

Table : Accuracy Results for K-Means

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Conclusions

This thesis presented CAD system and intelligent CAD system that can aid radiologists and may be used in the first stage of examination in the near future, the main objective of using an intelligent CAD with liver nodules is to Improve nodule detection by the radiologists.

k-means performs better segmented results in case the region of interest take a closed shape, and has problems when clusters are of differing non-globular shapes in addition to the initial centroid problem.

N-cut get better results with non-circular clusters, also in k-means clustering different initial partitions can result in different final clusters.

It is observed that increasing the number of iterations of invasive weed optimization algorithm for liver CT images, it display better results, but does not essentially increase the percentage of success.

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Future Work Introducing new versions of optimization algorithms such as Discrete Invasive Weed

Optimization (IWO) algorithm inspired from weed colonization to solve medical imaging problems. IWO has got successful results in many practical applications like optimization, developing a recommender system, design of encoding sequences for DNA computing, and other applications.

Integrate features extracted from elastography, e.g. fibro scan, to improve the discriminative power of the feature vector.

Applied the systems to 3D images.

Apply the CAD system and Intelligent CAD system to the early detection of breast cancer, Lung cancer, ... .

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Thank You!