Automatic Image Segmentation of Lesions in Multispectral Brain Graduate School of Industrial Engineering and Management National Yunlin University of Science & Technology Master Thesis Automatic Image Segmentation of Lesions in Multispectral Brain Magnetic Resonance Imaging Student：Yu-Ting Hsu Advisor：JaChih Fu, Ph.D.

Outline Introduction Literature Review Research Method Expected Results 2

Outline Introduction Literature Review Research Method Expected Results Outline Introduction Research Background and Motive Research Purpose  Literature Review Research Method Expected Results 3

Research Background and Motive (1/2) Introduction Literature Review Research Method Expected Results Research Background and Motive (1/2) Issues of brain tumor are concerned in recent years. Magnetic Resonance Imaging, MRI Good vision between different soft tissues Does not use ionizing radiation Brain Tissues Grey Matter, GM White Matter, WM Cerebrospinal Fluid, CSF Tumor Edema 4

Research Background and Motive (2/2) Introduction Literature Review Research Method Expected Results Research Background and Motive (2/2) Type of MRI T1-weighted Magnetic Resonance Imaging T2-weighted Magnetic Resonance Imaging Flair Magnetic Resonance Imaging Measure Method of Lesion Regions Manual Semi-automatic segmentation algorithm T1 MRI T2 MRI Flair MRI 5

Research Purpose Multispectral MRI Analysis Introduction Literature Review Research Method Expected Results Research Purpose Multispectral MRI Analysis Develop an automatic segmentation algorithm Segment lesion regions Tumor Edema Original Imaging Image Preprocessing Automatic Image Segmentation Performance Evaluation 6

Outline Introduction Literature Review Research Method Expected Results Outline Introduction Literature Review Single-spectral Image Segmentation Algorithm Heuristic Algorithms Multi-spectral Image Segmentation Algorithm Research Method Expected Results 7

Single-spectral Image Segmentation Algorithm(1/2) Introduction Literature Review Research Method Expected Results Single-spectral Image Segmentation Algorithm(1/2) Active Contours using Level Sets, ACLS Chan (2001) propose Active Contours using Level Sets to detect objects in a given image. Detect boundary energy by , expect is minimized. Original image Initial contour Boundary detection Lesion regions 8

Single-spectral Image Segmentation Algorithm(2/2) Introduction Literature Review Research Method Expected Results Single-spectral Image Segmentation Algorithm(2/2) Energy function of ACLS： Inside Energy Outside Energy λ：Length weighted coefficient ν：Area weighted coefficient μ：Inside energy coefficient g : Gradient ε : Smoothing parameter : Length weighted formula Influence Parameters : Area weighted formula : Inside energy formula 9

ACLS Application Author Year Application Paul et al. 2006 Introduction Literature Review Research Method Expected Results ACLS Application Author Year Application Paul et al. 2006 Applied ACLS for caudate nucleus and lateral ventricle segmentation in child MRI. Taheri et al. 2010 A threshold-based scheme that uses level sets for 3D tumor segmentation. Hwang et al. 2011 Presented the active contour model with shape prior for extracting the cerebellum from T1-weighted brain MR images. Sachdeva et al. 2012 Content-based active contour uses both intensity and texture information present within the active contour to overcome oversegmentation in an image. 10

Heuristic Algorithms(1/2) Introduction Literature Review Research Method Expected Results Heuristic Algorithms(1/2) Methods of Parameters Optimization Taguchi Methods、Genetic Algorithms、Grid Search Algorithms Advantages Disadvantages Taguchi Methods Get optimal factor fitting by less experiences Lack systematize System lacks efficiency May not be the global optimal solution Genetic Algorithms Distributed search Can search large range Coding may encounter difficulties High cost Large calculating time Grid Search Algorithms Good quality If range of parameters is too large, it needs time to calculate optimal parameters. 11

Heuristic Algorithms(2/2) Introduction Literature Review Research Method Expected Results Heuristic Algorithms(2/2) Grid Search Algorithm Multilevel Process Simultaneously on different computers Decide Parameter Range Segment Equidistant Grids β =0~20 Calculate Object Function of Each Grids 20 15 10 5 0 β=5~15 Select Optimal Solution 15 10 5 Segment Grids Yes α=10~20 10 15 20 No Optimal Solution α=0~20 0 5 10 15 20 12

Grid Search Application Introduction Literature Review Research Method Expected Results Grid Search Application Author Year Application Mu et al. 2005 Use grid search to find optimal parameters of V-SVM. Applied v-SVM learning to detect breast cancer. Huang et al. 2008 Use grid search to find optimal parameters of SVM. Let SVM render a diagnosis between the breast cancer. Li et al. 2010 DCE data sensitivities to various parameters were tested using comparisons that effected parametric grid searches of error surfaces. Lin et al. Use grid search to find optimal parameters of SVM. 13

Multi-spectral Image Segmentation Algorithm(1/3) Introduction Literature Review Research Method Expected Results Multi-spectral Image Segmentation Algorithm(1/3) Type of MRI T1、T2、Flair 𝑥 1 = 𝑥 11 , …𝑥 𝑖1 … 𝑥 𝑛1 𝑥 2 = 𝑥 12 , …𝑥 𝑖2 … 𝑥 𝑛2 𝑥 3 = 𝑥 13 , …𝑥 𝑖3 … 𝑥 𝑛3 T1 weighted T2 weighted Flair Conception of Multi-spectral Images： 𝑋= 𝑥 1 , 𝑥 2 , 𝑥 3 14

Multi-spectral Image Segmentation Algorithm(2/3) Introduction Literature Review Research Method Expected Results Multi-spectral Image Segmentation Algorithm(2/3) Support Vector Machine, SVM A binary classifier ＋1 －1 , Objective Function w：Normal vectors of hyperplane b ：Offset 𝑑 + ：the shortest distance of +1 training data to separating hyperplane 𝑑 − ：the shortest distance of -1 training data to separating hyperplane Margin：Maximum-margin hyperplane 15

Multi-spectral Image Segmentation Algorithm(3/3) Introduction Literature Review Research Method Expected Results Multi-spectral Image Segmentation Algorithm(3/3) One-against-all Method, OAA One of methods for multi-class SVM Each of the SVMs separates a single class from all remaining classes (Cortes and Vapnik, 1995) 𝑦 𝑖 =+1：1 𝑦 𝑖 =−1：2 , 3 , … , k 𝑦 𝑖 =+1：2 𝑦 𝑖 =+1：𝑘 𝑦 𝑖 =−1：1 , 3 , … , k 𝑦 𝑖 =−1：1 , 2 , … , k−1 max Class of 𝑥=argmax 𝑖=1,2,…,𝑘 ( 𝑤 𝑖 ∙𝜑 𝑥 + 𝑏 𝑖 ) 𝑖 𝑥 … 16

SVM Application Author Year Application Miranda et al. 2005 Introduction Literature Review Research Method Expected Results SVM Application Author Year Application Miranda et al. 2005 Applied SVM to perform multivariate classification of brain states from whole fMRI volumes without prior selection of spatial features. Chaplot et al. 2006 Support vector machine classifies MR brain images as either normal or abnormal. Lao et al. 2008 SVM classifier is first trained on expert-defined white matter lesions from multi-parametric MRI sequences. Yamamoto et al. 2010 Develop a computerized method which consisted of a rule-based method, a level set method, and SVM for detection of multiple sclerosis (MS) lesions in brain MRI. multiple sclerosis (MS) 多發性硬化證 17

Outline Introduction Literature Review Research Method Expected Results Outline Introduction Literature Review Research Method Research Flowchart Image Preprocessing Parameter Definition Grid Search Heuristic Algorithms ACLS Segmentation Algorithms Multi-spectral Segmentation Algorithms Performance Evaluation Expected Results 18

Research Flowchart Introduction Literature Review Research Method Expected Results Research Flowchart 19

Image Preprocessing Image Resampling Increasing the size of an image Introduction Literature Review Research Method Expected Results Image Preprocessing Image Resampling Increasing the size of an image 512*512 256*256 Original Image Resampling Image Resize 20

Cut the image without lesions Introduction Literature Review Research Method Expected Results Image Preprocessing Select initial contour Differences of pixel in T2 and Flair Select coordinates of this boundary be initial contour MR Images Cut the image without lesions Subtract pixels Lesion Region Boundary of lesion 21

Parameters Definition Introduction Literature Review Research Method Expected Results Parameters Definition Influence parameters of ACLS μ：Inside energy coefficient , ratio of curve moving speed λ：Length weighted coefficient , ratio of curve twist ν：Area weighted coefficient , ratio of curve extension ε：Smoothing parameter , ratio of curve smoothness 22

Grid Search Heuristic Algorithms Introduction Literature Review Research Method Expected Results Grid Search Heuristic Algorithms Find optimal parameters to make objective function maximization. Number of parameters：4 ( μ 、λ、ν、ε ) Generate Initial Parameters Calculate Object Function of Grids Search Next Grid Stop Conditions No Yes Optimal Parameters 23

ACLS Segmentation Algorithms Introduction Literature Review Research Method Expected Results ACLS Segmentation Algorithms MR Images Initial Contour Boundary Detection Lesion Regions 24

Multi-spectral Segmentation Algorithms Introduction Literature Review Research Method Expected Results Multi-spectral Segmentation Algorithms MR Images Select Training Samples SVM-OAA Segmentation Results 25

Performance Evaluation Introduction Literature Review Research Method Expected Results Performance Evaluation Jaccard Similarity Jaccard Similarity Formulation : , 0 < J ( 𝑆 1 , 𝑆 2 ) < 1 Results of Automatic Segmentation Golden Standard 本論文的品質特性是將手動切割作為Golden Standard，與自動切割所得進行Jaccard Similarity運算 假設S1是手動切割，s2是自動切割，交集之部份則為正確分割之區域。 Jaccard Similarity公式則是S1與S2交集/ S1與S2聯集 FA 26

Outline Introduction Literature Review Research Method Expected Results Outline Introduction Literature Review Research Method Expected Results 27

Introduction Literature Review Research Method Expected Results Expected Results Automatic segmentation of lesions in MRI using ACLS algorithm Automatic segmentation of lesions in MRI using multispectral segmentation algorithm Performance evaluation Select the algorithm which has better performance 28

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