Hierarchical Image Segmentation for Identifying Stroke Regions In Apparent Diffusion Coefficient (ADC) Image Maps Anthony Bianchi UCR 2007 Advisor:

Slides:



Advertisements
Similar presentations
Neuro-Imaging High Resolution Ex-Vivo MRI Ex-Vivo DTI of Brain Stem
Advertisements

Top-Down & Bottom-Up Segmentation
Programming Assignment 2 CS 302 Data Structures Dr. George Bebis.
Binary Image Analysis Selim Aksoy Department of Computer Engineering Bilkent University
DANGER DETECTOR FINAL VIP PRESENTATION Krithika Chandrasekar Devang Parekh Shruthi S Reddy.
Image Segmentation Image segmentation (segmentace obrazu) –division or separation of the image into segments (connected regions) of similar properties.
Lecture 07 Segmentation Lecture 07 Segmentation Mata kuliah: T Computer Vision Tahun: 2010.
電腦視覺 Computer and Robot Vision I Chapter2: Binary Machine Vision: Thresholding and Segmentation Instructor: Shih-Shinh Huang 1.
Some Basic Morphological Algorithm
1 s-t Graph Cuts for Binary Energy Minimization  Now that we have an energy function, the big question is how do we minimize it? n Exhaustive search is.
MRI Image Segmentation for Brain Injury Quantification Lindsay Kulkin 1 and Bir Bhanu 2 1 Department of Biomedical Engineering, Syracuse University, Syracuse,
Understanding and Quantifying the Dancing Behavior of Stem Cells Before Attachment Clinton Y. Jung 1 and Dr. Bir Bhanu 2, Department of Electrical Engineering.
1 Binary Image Analysis Binary image analysis consists of a set of image analysis operations that are used to produce or process binary images, usually.
CS 376b Introduction to Computer Vision 04 / 04 / 2008 Instructor: Michael Eckmann.
Computer Vision Basics Image Terminology Binary Operations Filtering Edge Operators.
Smart Traveller with Visual Translator. What is Smart Traveller? Mobile Device which is convenience for a traveller to carry Mobile Device which is convenience.
Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.
Jacinto C. Nascimento, Member, IEEE, and Jorge S. Marques
Behavior Analysis Midterm Report Lipov Irina Ravid Dan Kotek Tommer.
Chapter 3 Binary Image Analysis. Types of images ► Digital image = I[r][c] is discrete for I, r, and c.  B[r][c] = binary image - range of I is in {0,1}
UNDERSTANDING DYNAMIC BEHAVIOR OF EMBRYONIC STEM CELL MITOSIS Shubham Debnath 1, Bir Bhanu 2 Embryonic stem cells are derived from the inner cell mass.
Image Segmentation Seminar III Xiaofeng Fan. Today ’ s Presentation Problem Definition Problem Definition Approach Approach Segmentation Methods Segmentation.
Hierarchical Distributed Genetic Algorithm for Image Segmentation Hanchuan Peng, Fuhui Long*, Zheru Chi, and Wanshi Siu {fhlong, phc,
CS 6825: Binary Image Processing – binary blob metrics
Visualization and Computer Graphics Lab International University Bremen Converting RGB Volume Data to Scalar Fields Tetyana Ivanovska and Lars Linsen School.
MRI Image Segmentation for Brain Injury Quantification Lindsay Kulkin BRITE REU 2009 Advisor: Bir Bhanu August 20, 2009.
September 23, 2014Computer Vision Lecture 5: Binary Image Processing 1 Binary Images Binary images are grayscale images with only two possible levels of.
1 Binary Image Analysis Binary image analysis consists of a set of image analysis operations that are used to produce or process binary images, usually.
Jonathan Dinger 1. Traffic footage example 2  Important step in video analysis  Background subtraction is often used 3.
Chapter 10, Part II Edge Linking and Boundary Detection The methods discussed in the previous section yield pixels lying only on edges. This section.
Data Extraction using Image Similarity CIS 601 Image Processing Ajay Kumar Yadav.
Clinton Jung Advisor: Bir Bhanu Center for Research in Intelligent Systems August 20, 2009.
Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.
ImArray - An Automated High-Performance Microarray Scanner Software for Microarray Image Analysis, Data Management and Knowledge Mining Wei-Bang Chen and.
Image Segmentation and Edge Detection Digital Image Processing Instructor: Dr. Cheng-Chien LiuCheng-Chien Liu Department of Earth Sciences National Cheng.
CS654: Digital Image Analysis
1 Regions and Binary Images Hao Jiang Computer Science Department Sept. 24, 2009.
Image Segmentation in Color Space By Anisa Chaudhary.
Face Image-Based Gender Recognition Using Complex-Valued Neural Network Instructor :Dr. Dong-Chul Kim Indrani Gorripati.
主講者 : 陳建齊. Outline & Content 1. Introduction 2. Thresholding 3. Edge-based segmentation 4. Region-based segmentation 5. conclusion 2.
Digital Image Processing
Machine Vision ENT 273 Regions and Segmentation in Images Hema C.R. Lecture 4.
Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.
Machine Vision ENT 273 Hema C.R. Binary Image Processing Lecture 3.
Color Image Segmentation Mentor : Dr. Rajeev Srivastava Students: Achit Kumar Ojha Aseem Kumar Akshay Tyagi.
Shadow Detection in Remotely Sensed Images Based on Self-Adaptive Feature Selection Jiahang Liu, Tao Fang, and Deren Li IEEE TRANSACTIONS ON GEOSCIENCE.
EE368 Final Project Spring 2003
Course : T Computer Vision
Evaluating Techniques for Image Classification
Computer Vision Lecture 13: Image Segmentation III
Binary Image Analysis Gokberk Cinbis
Mean Shift Segmentation
Brain Hemorrhage Detection and Classification Steps
Computer Vision Lecture 5: Binary Image Processing
Binary Image Analysis: Part 1 Readings: Chapter 3: 3.1, 3.4, 3.8
Group 1: Gary Chern Paul Gurney Jared Starman
How I treat and manage strokes in sickle cell disease
Maximally Stable Extremal Regions
Johnny Suh M.D., Dr. Jacobson M.D., Dr. Pond M.D.
Binary Image Analysis used in a variety of applications:
Binary Image Analysis: Part 1 Readings: Chapter 3: 3.1, 3.4, 3.8
Counting Iron-Absorbed Small Intestinal Cells
Maximally Stable Extremal Regions
Divisions of the Nervous System
Department of Computer Engineering
The Image The pixels in the image The mask The resulting image 255 X
Computer and Robot Vision I
Image segmentation Grey scale image Binary image
Using simple machine learning for image segmentation
Binary Image Analysis used in a variety of applications:
Presentation transcript:

Hierarchical Image Segmentation for Identifying Stroke Regions In Apparent Diffusion Coefficient (ADC) Image Maps Anthony Bianchi UCR 2007 Advisor: Bir Bhanu 8/24/2007

Outline Background How it Happens The Images Why Automatic is Needed Process Flowchart Automatic Thresholding Connected Components Example Results Two Patients Findings Conclusion Acknowledgments

Stroke: 1/4000 live births Arterial-ischemic stroke, 73% Arterial Ischemic Stroke Cerebral arterial thrombosis: possible postnatal etiology of AIS. AIS  Focal Lesion Background

Apparent Diffusion Coefficient (ADC) Image Maps ≤ 5 days ≥ 5 days 25 days 5 days An ADC image map measures the diffusion of water. If the diffusion is low the grayscale value is low.

Why is Automatic Segmentation Needed? Currently manual segmentations is time intensive and inaccurate. Manual segmentations can very over 30% from one person to the next, and can take hours per patient. An automatic segmentation algorithm will be repeatable, and will take minutes per patient. We are currently working with LLUMC. They would like to use this segmentation to classify stroke victims into mild, moderate, and severe. They will use these labels to accept patients for stem cell trials.

Find Image to be Segmented Find Threshold Automatically Split Image Using Threshold Find Connected Components That Satisfy Second threshold Lower Region Higher Region Close and Fill Images # Regions > 1 YES NO Find Connected Components That Satisfy Second threshold # Regions > 1 Separation Results The Process

Automatic Thresholding: Otsu’s method Threshold Found 176  2 w (t) = q 1 (t)  2 1 (t) + q 2 (t)  2 2 (t) Within Group Variance Sum of Probability in Group 1 Variance Group 1 Sum of Probability in Group 2 Variance Group 2 We test every threshold to find the smallest Within Group Variance. A recursive form of the above equation is implemented to cut down computation time.

Connected Components Mask Example of Connected Components A mask gets sent through the image. Each pixels is evaluated by the mask to see if it has a neighboring pixel. If there is a neighboring pixel the selected pixel gains the same label of that pixel. If no neighboring pixel is found a new label is created for that pixel.

Example of the Process Find object to be segmented. Threshold found 149 Object > 50% found Threshold found 98 Object found closed and filled

Patient 1 Patient 2RED = Damage GREEN = Area

For patient 1 the automatically segmented data gave a total damage of 14.7%, while manually segmented images gave a total of 17.7% damage. The reason for the difference between the manual and automatic segmented is because the area used in finding total area in the automatic segmented included spinal fluid. This fluid can be found by the automatic method and can be removed. For patient 2 we found the damaged area to be 3.5%, and the manual segmentation gave a 3.6% result. For patient two the manual segmentation included the cerebral spinal fluid, which was included in the area. Results

Conclusion The experiments show that the automatic method has small differences compared to the manually segmented images. But, it is effective and consistent in finding the damage area in the ADC images. Hypoxic-Ischemic Encephalopathy is another type of stroke that happens every 1/1000 live births. These injuries are diffused through the brain unlike the AIS patients. This segmentation method should be able to detect this type of stroke. The next step is trying to use this method on different MRI types such as T2 image maps. A 3D approach could give better results, because it could connect the structure from slide to slide.

Acknowledgments I would like to thank: Dr. Bhanu for his guidance. Jacqueline Coats, Andy Obenaus, and Stephen Ashwal (from LLUMC) for data and useful information. BRITE advisors for the opportunity. Friends & Family for Support.