Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Lec12: Shape Models and Medical Image SegmentationUlaş Bağcı
ShapeModeling – M-reps
– Active Shape Models (ASM)
– Oriented Active Shape Models (OASM)
– Application in anatomy recognition and segmentation – Comparison of ASM and OASM
ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)Ulaş Bağcı
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Lec12: Shape Models and Medical Image SegmentationUlaş Bağcı
ShapeModeling – M-reps
– Active Shape Models (ASM)
– Oriented Active Shape Models (OASM)
– Application in anatomy recognition and segmentation – Comparison of ASM and OASM
ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)Ulaş Bağcı
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Initial Introduction of Image processing is included in these slides which contain 1. Introduction of Image Processing
2.Elements of visual perception
3. Image sensing and Quantization
4.A simple image formation model
5.Basic concept of Sampling and Quantization
Reader will find it easy to understand the topics described here in slides . A detailed description of each topic illustrated here.
Please read and if you like do comments also.... Thanks
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Initial Introduction of Image processing is included in these slides which contain 1. Introduction of Image Processing
2.Elements of visual perception
3. Image sensing and Quantization
4.A simple image formation model
5.Basic concept of Sampling and Quantization
Reader will find it easy to understand the topics described here in slides . A detailed description of each topic illustrated here.
Please read and if you like do comments also.... Thanks
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
A comparison of image segmentation techniques, otsu and watershed for x ray i...eSAT Journals
Abstract The most dangerous and rapidly spreading disease in the world is Tuberculosis. In the investigating for suspected tuberculosis (TB), chest radiography is the only key techniques of diagnosis based on the medical imaging So, Computer aided diagnosis (CAD) has been popular and many researchers are interested in this research areas and different approaches have been proposed for the TB detection. Image segmentation plays a great importance in most medical imaging, by extracting the anatomical structures from images. There exist many image segmentation techniques in the literature, each of them having their own advantages and disadvantages. The aim of X-ray segmentation is to subdivide the image in different portions, so that it can help during the study the structure of the bone, for the detection of disorder. The goal of this paper is to review the most important image segmentation methods starting from a data base composed by real X-ray images. Keywords— chest radiography, computer aided diagnosis, image segmentation, anatomical structures, real X-rays.
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
An overview of reader development initiatives in Scottish public libraries and a review of their impact on readers. By Rhona Arthur Assistant Director of the Scottish Library and Information Council (SLIC).
One-Pass Clustering (OPC) is a technique to efficiently generate superpixels in the combined five-dimensional feature space of CIELAB color and XY image plane.
Review of Image Segmentation Techniques based on Region Merging ApproachEditor IJMTER
Image segmentation is an important task in computer vision and object recognition. Since
fully automatic image segmentation is usually very hard for natural images, interactive schemes with a
few simple user inputs are good solutions. In image segmentation the image is dividing into various
segments for processing images. The complexity of image content is a bigger challenge for carrying out
automatic image segmentation. On regions based scheme, the images are merged based on the similarity
criteria depending upon comparing the mean values of both the regions to be merged. So, the similar
regions are then merged and the dissimilar regions are merged together.
A digital image forensic approach to detect whether
an image has been seam carved or not is investigated herein.
Seam carving is a content-aware image retargeting technique
which preserves the semantically important content of an image
while resizing it. The same technique, however, can be used
for malicious tampering of an image. 18 energy, seam, and
noise related features defined by Ryu [1] are produced using
Sobel’s [2] gradient filter and Rubinstein’s [3] forward energy
criterion enhanced with image gradients. An extreme gradient
boosting classifier [4] is trained to make the final decision.
Experimental results show that the proposed approach improves
the detection accuracy from 5 to 10% for seam carved images
with different scaling ratios when compared with other state-ofthe-
art methods.
Image segmentation Based on Chan-Vese Active Contours using Finite Difference...ijsrd.com
There are a lot of image segmentation techniques that try to differentiate between backgrounds and object pixels but many of them fail to discriminate between different objects that are close to each other, e.g. low contrast between foreground and background regions increase the difficulty for segmenting images. So we introduced the Chan-Vese active contours model for image segmentation to detect the objects in given image, which is built based on techniques of curve evolution and level set method. The Chan-Vese model is a special case of Mumford-Shah functional for segmentation and level sets. It differs from other active contour models in that it is not edge dependent, therefore it is more capable of detecting objects whose boundaries may not be defined by a gradient. Finally, we developed code in Matlab 7.8 for solving resulting Partial differential equation numerically by the finite differences schemes on pixel-by-pixel domain.
A new hybrid method for the segmentation of the brain mrissipij
The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue
characterization, presenting an interest in the follow-up of various pathologies such as the multiple
sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The
extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is
proposed; finally the last section is organized around an experimental part allowing the study of the
behavior of our model on textured images. In the aim to validate our model, different segmentations were
down on pathological Brain MRI, the obtained results have been compared to the results obtained by
another models. This results show the effectiveness and the robustness of the suggested approach.
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUEScscpconf
In the first study [1], a combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map were used to perform image segmentation and edge detection
tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging
procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain accurate edge maps of our images without using watershed method. In this technique: We solved the problem of undesirable over segmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study level set methods are used for the implementation of curve/interface evolution under various forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and extract individual components from a medical image. This is done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford–Shah functional for segmentation and level sets. Once we classified our images into different intensity regions based on Markov Random Field. Then we detect regions whose boundaries are not necessarily defined by gradient by minimize an energy of Mumford–Shah functional forsegmentation, where in the level set formulation, the problem becomes a mean-curvature which will stop on the desired boundary. The stopping term does not depend on the gradient of the image as in the classical active contour. The initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one
closed boundary per actual region in the image.
Image segmentation is a computer vision task that involves dividing an image into multiple segments or regions, where each segment corresponds to a distinct object, region, or feature within the image. The goal of image segmentation is to simplify and analyze an image by partitioning it into meaningful and semantically relevant parts. This is a crucial step in various applications, including object recognition, medical imaging, autonomous driving, and more.
Key points about image segmentation:
Semantic Segmentation: This type of segmentation assigns each pixel in an image to a specific class, essentially labeling each pixel with the object or region it belongs to. It's commonly used for object detection and scene understanding.
Instance Segmentation: Here, individual instances of objects are separated and labeled separately. This is especially useful when multiple objects of the same class are present in the image.
Boundary Detection: Some segmentation methods focus on identifying the boundaries that separate different objects or regions in an image.
Methods: Image segmentation can be achieved through various techniques, including traditional methods like thresholding, clustering, and region growing, as well as more advanced techniques involving deep learning, such as using convolutional neural networks (CNNs) and fully convolutional networks (FCNs).
Challenges: Image segmentation can be challenging due to variations in lighting, color, texture, and object shape. Overlapping objects and unclear boundaries further complicate the task.
Applications: Image segmentation is used in diverse fields. For example, in medical imaging, it helps identify organs or abnormalities. In autonomous vehicles, it aids in identifying pedestrians, other vehicles, and obstacles.
Evaluation: Measuring the accuracy of segmentation methods can be complex. Metrics like Intersection over Union (IoU) and Dice coefficient are often used to compare segmented results to ground truth.
Data Annotation: Creating ground truth annotations for segmentation can be labor-intensive, as each pixel must be labeled. This has led to the development of datasets and tools to facilitate annotation.
Semantic Segmentation Networks: Deep learning architectures like U-Net, Mask R-CNN, and Deeplab have significantly improved the accuracy of image segmentation by effectively learning complex patterns and features.
Image segmentation plays a fundamental role in understanding and processing images, enabling computers to "see" and interpret visual information in ways that mimic human perception.
Image segmentation is a computer vision task that involves dividing an image into meaningful and distinct segments or regions. The goal is to partition an image into segments that represent different objects or areas of interest within the image. Image segmentation plays a crucial role in various applications, such as object detection, medical imaging, autonomous vehicles, and more.
3D Reconstruction from Multiple uncalibrated 2D Images of an ObjectAnkur Tyagi
3D reconstruction is the process of capturing the shape and appearance of real objects. In this project we are using passive methods which only use sensors to measure the radiance reflected or emitted by the objects surface to infer its 3D structure.
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Joint3DShapeMatching - a fast approach to 3D model matching using MatchALS 3...Mamoon Ismail Khalid
we extend the global optimization-based
approach of jointly matching a set of images to jointly
matching a set of 3D meshes. The estimated correspon
dences simultaneously maximize pairwise feature affini
ties and cycle consistency across multiple models. We
show that the low-rank matrix recovery problem can be
efficiently applied to the 3D meshes as well. The fast
alternating minimization algorithm helps to handle real
world practical problems with thousands of features. Ex
perimental results show that, unlike the state-of-the-art
algorithm which rely on semi-definite programming, our
algorithm provides an order of magnitude speed-up along
with competitive performance. Along with the joint shape
matching we propose an approach to apply a distortion
term in pairwise matching, which helps in successfully
matching the reflexive sub-parts of two models distinc
tively. In the end, we demonstrate the applicability of
the algorithm to match a set of 3D meshes of the SCAPE
benchmark database
Image Restoration UsingNonlocally Centralized Sparse Representation and histo...IJERA Editor
Due to the degradation of observed image the noisy, blurred, distorted image can be occurred .To restore the image informationby conventional modelsmay not be accurate enough for faithful reconstruction of the original image. I propose the sparse representations to improve the performance of based image restoration. In this method the sparse coding noise is added for image restoration, due to this image restoration the sparse coefficients of original image can be detected. The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, fordenoising the image here we use the histogram clipping method by using histogram based sparse representation to effectively reduce the noise and also implement the TMR filter for Quality image. Various types of image restoration problems, including denoising, deblurring and super-resolution, validate the generality and state-of-the-art performance of the proposed algorithm.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
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2. 2
Outline
Introduction
Edge-based segmentation
Region-based segmentation
– Region Growing
– Split-and-merge
Active contour models (snakes)
Application in medical imaging
Conclusion
Assignment 2
3. 3
Introduction
What is Image segmentation ?
– The different partitioning of an image into non-
overlapping, constituent regions which are
homogeneous with respect to some characteristic
such as intensity or texture.
– Each of such homogeneous regions may
represent an object.
4. 4
Introduction
The shape of an object can be described in terms of:
Its boundary – requires image edge detection
The region it occupies – requires image
segmentation in homogeneous regions, Image
regions generally have homogeneous
characteristics (e.g. intensity, texture)
Segmentation methods are then classified into
– Edge-based
– Region-based
5. 5
Edge-based Segmentation
Emphasis:
– Determine the boundaries that separate regions
Common approaches
– Find edge points in the image
Gradient based methods
Second order methods
– Linking these points in some way to produce
description of edges in terms of lines, curves etc
6. 6
Detecting Edge points-Gradient based
methods
An edge point is a point
where a discontinuity in
gradient occurs across
some line
Different types of
discontinuity are shown
in the figure
7. 7
Gradient based methods (cont.)
The gradient is a vector whose components
measure how rapidly pixel values are changing
with distance, in the x and y directions
8. 8
Gradient based methods (cont.)
Considering dx=dy=1 (pixel spacing) and considering
a point (i,j)
Δx = f(i+1,j) – f(i,j)
Δy = f(i,j+1) – f(i,j)
Δx and Δy can be calculated by convolving the image
with convolution masks
9. 9
Gradient based methods (cont.)
To approximate the gradient along directions at 45o and 135o
to the axes respectively,
known as Roberts edge operator. The corresponding
convolution masks are
Other 3x3 edge operators can be used such as Sobel and
Canny
10. 10
Example
Original image Image produced by
the horizontal
gradient calculation
Image produced by
the vertical gradient
calculation
11. 11
Example
Gradient image formed by combining horizontal
and vertical gradient detection
Gradient image is produced using
the magnitude M form
M = | Δx | + | Δy |
The gradient direction θ is equal
to
Θ = tan-1 (Δy / Δx )
12. 12
Implementation using Matlab
Read image and display it.
I = imread('coins.jpg'); imshow(I)
Apply the Sobel and Canny
edge detectors to the image
and display them
BW1 = edge(I,'sobel');
BW2 = edge(I,'canny');
imshow(BW1) figure,
imshow(BW2)
Original image “coins.jpg”
14. 14
Edge Linking
Edge detectors yield pixels that lie on edges
The objective is to replace many points on
edges with real edges.
Edge linking can be performed by:
– Local edge linkers – where edge points are
grouped according to their relationships with the
neighboring edge points.
– Global Edge Linkers – Hough transform
15. 15
Hough Transform
Allows recognition of global patterns in an
image
Finds curves like straight lines, circles, etc
Suppose that we are looking for straight lines
in an image
– If we take a point (x',y') in the image, all lines
which pass through that pixel have the form
y’ = mx’ +c
16. 16
Hough Transform
This equation can be
written as
c = -x’m + y’
where x’,y’ are constants
and m,c varies
Each different line
through the point (x',y')
corresponds to one of
the points on the line in
(m,c) space
Lines through a
point
17. 17
Hough Transform
All pixels which lie on the
same line in (x,y) space are
represented by lines which
all pass through a single
point in (m,c) space.
The single point through
which they all pass gives
the values of m and c in the
equation of the line y=mx+c.
18. 18
Hough Transform
The y=mx+c form for representing a straight line
breaks down for vertical lines, when m becomes
infinite.
To avoid this problem, it is better to describe straight
lines in the form of
x cos θ + y sin θ = r
i.e. a point in (x,y) space is now represented by a curve
in (r,θ) space rather than a straight line
19. 19
Hough Transform
To detect straight lines in an image
1. Quantize (m,c) space into a two-dimensional array A for
appropriate steps of m and c.
2. Initialize all elements of A(m,c) to zero.
3. For each pixel (x',y') which lies on some edge in the image,
add 1 to all elements of A(m,c) whose indices m and c
satisfy y'=mx'+c.
4. Search for elements of A(m,c) which have large values --
Each one found corresponds to a line in the original image.
20. 20
Hough Transform
To find circles, with equation
(x – a)2 + (y – b)2 = r2
– Every point in (x,y) space corresponds to a surface in (a,b,r) space
(as we can vary any two of a, b and r, but the third is determined
by the equation of the circle).
– The basic method is, thus, modified to use a three-dimensional
array A(a,b,r),
– All points in it which satisfy the equation for a circle are
incremented.
The technique takes rapidly increasing amounts of time for
more complicated curves as the number of variables (and
hence the number of dimensions of A) increases
22. 22
Region Growing
A simple approach to image segmentation is
to start from some pixels (seeds)
representing distinct image regions and to
grow them, until they cover the entire image
Before assigning a pixel x to a region Ri(k),
check if the region is homogeneous: i.e.
H(Ri(k) U {x}) = TRUE
23. 23
Region Growing
The arithmetic mean M and standard
deviation sd can be used to decide if merging
two regions R1,R2 is allowed
if |M1 – M2| < (k)*sd(i) , i = 1, 2 , merge the
two regions
where k is a certain threshold
24. 24
Split-and-Merge
The opposite approach to region growing is region
splitting.
The approach starts with the assumption that the
entire image is homogeneous
If the entire image is not homogeneous, the image is
split into four sub images
This splitting procedure is repeated recursively until
the image is split into homogeneous regions
25. 25
Split-and-Merge
Since the procedure is recursive, it produces
an image representation that can be
described by a tree whose nodes have four
children each
Such a tree is called a Quadtree.
28. 28
Split-and-Merge
Splitting techniques create regions that may
be adjacent and homogeneous, but not
merged.
Split and Merge method is an iterative
algorithm that includes both splitting and
merging at each iteration. It produces more
compact regions than the splitting algorithms
29. 29
Split-and-Merge Algorithm
If a region R is inhomogeneous
(H(R)= False) then split into four sub regions
If two adjacent regions Ri,Rj are
homogeneous (H(Ri U Rj) = TRUE), merge
them
Stop when no further splitting or merging is
possible
30. 30
Outline
Introduction
Edge-based segmentation
Region-based segmentation
– Region Growing
– Split-and-merge
Active contour models (snakes)
Application in medical imaging
Conclusion
31. 31
Active Contour Models (Snakes)
First introduced in 1987 by Kass et al, and gained
popularity since then.
Represents an object boundary as a parametric
curve.
An energy function E is associated with the curve.
The problem of finding object boundary is an energy
minimization problem.
32. 32
Framework for snakes
A higher level process or a user
initializes any curve close to the
object boundary.
The snake then starts
deforming and moving towards
the desired object boundary.
In the end it completely “wraps”
around the object.
(Digram courtesy “Snakes, shapes, gradient vector flow”, Xu, Prince)
33. 33
Snakes
Contour possesses an energy (Esnake) which is defined as the
sum of the three energy terms.
where Einternal represents the internal energy of the spline due to
bending, Eexternal denotes image forces, and Econstraint denotes
external constraint forces.
The energy terms are defined such that the final position of
the contour will have a minimum energy (Emin)
Therefore the problem of detecting objects reduces to an
energy minimization problem.
int intsnake ernal external constraE E E E
34. 34
Outline
Introduction
Edge-based segmentation
Region-based segmentation
– Region Growing
– Split-and-merge
Active contour models (snakes)
Application in medical imaging
Conclusion
35. 35
Application in medical imaging
In-vivo segmentation: automating or facilitating the
delineation of anatomical structures and other
regions of interest
Segmentation methods vary widely depending on
the specific application and imaging modality.
There is currently no single segmentation method
that yields acceptable results for every medical
image.
Selecting an appropriate approach to a
segmentation problem can be a difficult dilemma.
36. 36
Example
In reconstructing a 3D
model of a prostate, the
capsule contour needs to
be extracted from the
slices’ images
Capsule contour
37. 37
Example
The capsule consists of
collagen fibers tissues that
appear under the
microscope as wavy lines
(see figure)
However, the capsule line is sometimes unrecognizable because of the
naturally occurring intrusion of muscle into the prostate gland which
makes the segmentation problem more challenging.
38. 38
Summary
Introduction
Edge-based segmentation
Region-based segmentation
– Region Growing
– Split-and-merge
Active contour models (snakes)
Application in medical imaging
Conclusion
39. 39
Conclusion
Edge detection and region growing algorithms are
very popular in most commercial image analysis
tools
The reason is that they are simple to understand
and to implement, and they are very generic, as
they do not assume specific knowledge about the
objects to be analyzed.
These methods are often the starting point for more
sophisticated model-based methods
40. 40
Conclusion
For complex image data, such as medical images,
their usefulness is quite limited.
Effective image analysis methods must incorporate
a priori knowledge of the considered structures
such as photometric properties (object intensity,
contrast, texture); geometric properties (position,
shape, motion, deformation); and the context, such
as the relative position with respect to the other
objects in the neighborhood