Download Detection, Visualization, and Identification of Lung Abnormalities in

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

page
1
page
2
page
3
page
4
page
5
page
6
page
7
page
8
page
9
page
10
page
11
page
12
page
13
page
14
page
15
page
16
page
17
page
18
page
19
page
20
page
21
page
22
page
23
page
24
page
25
page
26
page
27
page
28
Document related concepts
no text concepts found
Transcript 
Detection, Visualization, and Identification of
Lung Abnormalities in Chest Spiral CT Scans
3D CT Image Data
Visualize Whole lung
tissues Using VTK
8 mm
Removing
Background
Making stochastic
Model using Gibbs
Markov Random Field
Apply ICM using
Genetic and EM
algorithm
Abnormality
Detection System
Visualize Abnormal
Tissues Using VTK
Registration
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Medical Imaging
Types of medical Imaging
1. X-ray Imaging
Advantage
Cheap
Disadvantage
It is just a projection of an object
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Example of X-ray Imaging
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Example of X-ray Imaging
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
2. computed tomography (CT)
Advantage
1. better Geometry of the scanned subject
2. Using CT we can build 3-D model of the
scanned subject
3. Give high contrast between bones and soft
tissues
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Disadvantage
1. Ct has harmful effect due to radiation dose (Xray)
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Example of CT
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
3. Magnetic Resonance Imaging (MRI)
Advantage
1. Give high contrast of soft tissues
Disadvantages
1. Does not preserve the geometry of the scanned
subject if it is compared with CT
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Example of MRI
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
4. Ultrasound Imaging
Advantage
1. Real Time Imaging
2. No harmful effect
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Example of Ultrasound Imaging
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Automated Lung Abnormality Detection System
Visualize Whole lung
tissues Using VTK
3D CT Image Data
8 mm
Removing
Background
Making stochastic
Model using Gibbs
Markov Random Field
Apply ICM using
Genetic and EM
algorithm
Abnormality
Detection System
Visualize Abnormal
Tissues Using VTK
Registration
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
System Design
1. Preprocessing Data
Such as you can filter your images in order to
reduce the noise
1. LPF
2. HPF
3. BPF
3. Median filter
4. Gaussian Filter
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Image 3 x 3 pixel
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
1. Remove the background
Starting from the edge of the image,
neighboring pixels are compared. Pixels
having the same gray levels are removed
(I.e., belong to the same region), while those
differing are kept.
Original Image 3x3 pixels
Image 3 x 3 pixels after
applying the algorithm
Original image
Image after removing background
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Chest
Background
Lung
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
How To estimate the Initial Mean for Lung and Chest?
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Abnormal tissues
CT Slice Contain Abnormal Tissues
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Slice_No. 32
Abnormal tissues
Slice_No. 33
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Abnormality Detection Criteria
Each Ring Shape will take three ranks
1. Radial uniformity (R)
2. Position of the ring shape relative to the center of
right or left lung edge (P)
3. Connectivity between different slices (C)
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Abnormality System detection
Abnormal
yes
Remove the Normal
Tissues
Detecting
ring shape
Compute The Total
Rank (R) for Each
ring shape
Tissues
R> 2
No
Normal
Tissues
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
a. Removing the normal tissues
In order to remove the normal tissues of the lung, we will compute the
histogram for each slice and search for its peak, and then remove all pixels
beneath this peak.
Before Removing
normal Tissues
Histogram of the CT
slice
After Removing normal
Tissues
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
c. Ranking
1. NR, measures the uniformity distribution of the edges.
2. NC, measures the connectivity that the pixel (x, y) appears in
the same location in different slices
3. NP, each pixel given a rank NP reflecting its position relative to
the center of the right lung or the left lung.
Total Rank (N)= NR + NC + NP
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
4. Results
(a) Original slice from a
spiral CT scan of a patient
(c) Desired tissues
(b) Slice after removing
the background
(e) The isolated lungs
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
(f) Bronchi, bronchioles and
abnormal tissues
(g) Abnormal tissues detected by
our algorithm
(h) Manual detection by expert
doctor
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Building 3-D model
We use VTK tool to build 3-D model for the whole lung tissues and
abnormal tissues, bronchi, and bronchioles
3-D model for
the whole lung
tissues
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
This Figure shows the abnormal tissues in the 3-D
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
More Results
Computer Vision Image Processing Laboratory
www.cvip.uofl.edu
Related documents