Combining Machine Learning Techniques with Statistical Shape Models in Medical Image Segmentation

Combining Machine Learning Techniques with Statistical Shape Models in Medical Image Segmentation.

ABSTRACT

In this thesis, we implemented Point Distribution Model and basic Active Shape Model algorithm and contributed this to the AUST Computer Vision and Machine Learning code library.

We applied the Active Shape Model to segmenting lateral ventricles of 2D brain images and used machine learning – specifically K-Nearest Neighbour algorithm- to improve segmentation results.

A statistical shape model is created from a training dataset which is used to search for an object of interest in an image.

Active shape model has shown over time to be a reliable image segmentation methodology but its segmentation accuracy is hindered especially by poor initialization which can’t be guaranteed to always be perfect.

In our methodology, we extract features for each landmark using Haar filters. We train a classifier with these features and use the classifier to classify points around the final points of an Active shape model search.

The aim of this approach is to better place points that might have been wrongly placed from the ASM search.

We have used the simple, yet effective K-Nearest Neighbour machine learning algorithm, and have demonstrated the ability of this method to improve segmentation accuracy by segmenting lateral ventricles of the .

TABLE OF CONTENT

Abstract………………… ii

Dedication……………. iii

Acknowledgments……… iv

Table of Contents……….. v

List of Figures…………..ix

CHAPTER ONE

• Introduction………. 1
• Problem Definition…………. 3
• Outline of Thesis…………… 4

CHAPTER TWO

• Image Segmentation……… 5
• First Generation Segmentation Methods………….. 6
  • Thresholding……………. 7
  • Region Growing…………… 8
  • Edge Tracing…………………9
  • Split and Merge………… 10
• Second Generation Segmentation Methods………10
  • K-means Clustering……………10
  • Fuzzy C-means Clustering………….12
  • Active Contour Model (Snakes)……………. 13
• Third Generation Segmentation Methods………..14
  • Active Shape Models………………..15
  • Active Appearance Models…………..16
  • Atlas based segmentation…………. 19

CHAPTER THREE

• Machine Learning………. 21
  • Supervised Learning…………..22
  • Unsupervised Learning……… 23
  • Reinforcement Learning………. 24
• Machine Learning Techniques………….. 24
  • K-Nearest Neighbour Algorithm……….. 24
  • Naïve Bayes Classification………. 25
  • Decision Trees……………. 26
  • Bagging, Boosting, Random Forest………….. 27
  • Clustering…………….. 30
  • Neural Networks…………………31
  • Support Vector Machines………31
• Machine Learning in Image Segmentation……….. 33
• Statistical Shape Models with Machine Learning………36

CHAPTER FOUR

• Active Shape Models……………… 40
  • Landmarks…………………. 40
  • Aligning the Training Set……… 42
  • Modeling Shape Variation………… 43
  • Using Shape Models in Image Search………….. 45

CHAPTER FIVE

• ASM with KNN and Haar Features…………..47
  • Haar Feature Extraction…………….. 47
• KNN-ASM………………………………… 49
• KNN-ASM Framework………… 50
• Results and Discussions……. 50

CHAPTER SIX

• Conclusion……….. 54
• Future Work…..54

REFERENCES……… 55

INTRODUCTION

In this thesis, our aim is to segment images, specifically, medical images. We aim to implement the popular Active Shape Model algorithm [16] and demonstrate its usefulness in segmenting 2d medical images.

Furthermore, we explore improving the results of the Active Shape Model segmentation using machine learning techniques.

Predominantly, the aim of medical image segmentation is to label each pixel in an image to indicate the anatomical structure it belongs to and delineate such structures of interest for the purposes of visualization, diagnosis or medical research.

Segmentation is often a crucial first step in patient diagnosis especially when qualitative and quantitative information about appearance, size, or shape of patient anatomy is desired.

Results of medical image segmentation are useful for many purposes including image guided surgery, detection of anatomical changes over time, detection of pathological diseases, volumetric measurement, visualization and research.

With the increasing importance of the segmentation process in diagnosis, accuracy of the process is important, as this may impact diagnostic accuracy, treatment planning and subsequently treatment.

The segmentation process is unfortunately as difficult as it is important, and the reasons are easy to comprehend.

Computers are not half as good as humans when it comes to ill-defined problems such as object recognition, and when these images contain noise, it makes the process even more difficult for a computer.

REFERENCES

Bhanu , S. Lee and S. Das “Adaptive image segmentaton using multi-objective evaluation and hybrid search methods”, AAAI Fall Symp. Machine Learning in Computer Vision,

Cristinacce and T.F. Cootes, “Boosted Regression Active Shape Models”, Proc. British Machine Vision Conference, Vol. 2, 2007, pp.880-889.

D.J Withey and Z.J Koles, “Medical Image segmentation: Method and Software”. Proceedings of NFSI8 ICFBI, October 12 -14, 2007.

D.J Withey and Z.J Koles, “Three generations of medical image segmentation: Methods and Available Software”. International Journal of Bioelectromagnetism. Vol 9, No 2, 2007.

Etyngier, F. Ségonne, R. Keriven, “Active-contour-based image segmentation using machine learning techniques”, Med Image Comput Assist Interv. 2007;10(Pt1):891-9.

Fuzzy    c-means          clustering.       Available         at: “sites.google.com/site/dataclusteringalgorithms/fuzzy-c-means-clustering-algorithm”, Accessed March 23, 2013.

StudentsandScholarship Team.