Automatic left ventricle segmentation

Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model With Adaptation on Short-Axis Cardiac MRI

Presented by:

• Basma Gad El Mola• Maha Saad

Under supervision of :

Prof. Dr. Aliaa Yousef

1

AGENDA

Scientific and Medical Background

1. What is Image segmentation?

2. Image segmentation Applications

3. Image Segmentation Techniques

4. What is Cardiac anatomy

5. MRI applied on Cardiac

2

AGENDA (Cont.)

Paper contents

1. Introduction

2. Related Work

3. Automatic LV Segmentation

4. Experiments and Results

5. Conclusion

6. References

3

1- What is Image segmentation

Segmentation refers to

the process of partitioning

a digital image into

multiple regions (sets of

pixels).

4

2- Image segmentation Applications

Grouping in vision

determine image regions

figure

ground

separation

group

frames into

slots5


Iris Recognition

Face

Recognition

Fingerprint

Recognition

Recognition Tasks

6


Medical

Imaging

Locate tumors and other

pathologies

Measure tissue volumes Computer-guided surgery

7

3-Image Segmentation Techniques

Image segmentation depends on lot of factors:

1. Homogeneity of images

2. Texture

3. image content

SO,

There is no single method which can be considered

good for all type of images.

And not all methods equally good for a particular

type of image.

8

3-Image segmentation Techniques

image segmentation approaches divided into

following categories, based on two properties of

image:

1. Detecting Discontinuities

It means to partition an image based on abrupt changes

in intensity

this includes image segmentation algorithms like edge

detection.

2. Detecting Similarities

It means to partition an image into regions that are similar

according to a set of predefined criterion.

This includes image segmentation algorithms like

Thresholding, region growing, region splitting and

merging.9


segmentation

Techniques

Edge detection

Region growing

Classifiers

Clustering

Method

Region-based

10


Region growing

Method Description:

Region growing is a technique for extracting a region of the

image that is connected based on some predefined criteria.

This criteria can be based on intensity information and/or

edges in the image

Limitation:

Its primary disadvantage is that it requires manual in-

traction to obtain the seed point. Thus, for each region that

needs to be extracted, a seed must be planted


Classifiers Method Description:

Classifiers are known as supervised methods since they require training data that are manually segmented and then used as references for automatically segmenting new data.

Limitation:

A disadvantage of classifiers is that they generally do not perform any spatial modeling. This weakness has been addressed in recent work extending classifier methods to segmenting images that are corrupted by intensity in homogeneities


Clustering Approach

Method Description:

Assumes that each region in the image forms a separate

cluster in the feature space. Can be generally broken into two

steps(1) categorize the points in the feature space into

clusters;(2)map the clusters back to the spatial domain to

form separate regions

Limitation:

1. How to determine the number of clusters


Method

Method Description:

Requires that the histogram of an image has a number of

peaks, each corresponds to a region

Limitation:

(1) Does not work well for an image without any obvious

peaks or with broad and flat valleys.


Region-based Approaches

Method Description:

Group pixels into homogeneous regions. Including region

growing, region splitting, region merging or their

combination

Limitation:

(1) Are by nature sequential and quite expensive both in

computational time and memory


Edge detection approaches

Method Description:

Based on the detection of discontinuity, normally tries to

locate points with more or less abrupt changes in gray level.

Limitation:

(1) Does not work well with images in which the edges are

ill-defined or there are too many edges

(2) It is not a trivial job to produce a closed curve or

boundary

4 -What is Cardiac anatomy

17

5-MRI applied on Cardiac

18

Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model With Adaptation on Short-Axis Cardiac MRI

19

1. Introduction

The quantification of myocardial mass and systolic

function is performed for cardiac diagnose.

Medical Images used for measure cardiac function:

( MRI – CT – Ultrasound – X-Ray - SPECT)

An Automatic LV segmentation algorithm for cardiac

cine MRI images using Iterative Thresholding and an

Active Contour Model with Adaptation (ITHACA) is

presented.

We measured the blood volume and the

myocardial mass of the LV using our ITHACA

segmentation algorithm & compared this to

manual tracing and the commercially available

MASS analysis software.20

2-Related Work

cardiac LV segmentation methods using MRI can be categorized as follows:1. Traditional segmentation

2. Graph-based segmentation

3. Active shape model (ASM)4. Level-set algorithm

Much research has been performed in LV segmentation. Each algorithm has tradeoffs among time complexity, inter- or intra-operator variation, and accuracy in clinical practice. These algorithms have not segmented PTMs in detail.

21

2-Related Work

1) Traditional Segmentation Algorithms

Such as (Thresholding – Region growing – Edge-detection –

Clustering)

These algorithms require significant user-interaction to segment

LV.

So, They have been combined with other segmentation

techniques to minimize User-intervention

These Algorithms works will for mid-ventricle slices of LV, but

have problems in basal and apical slices

They also unable to segment the detailed papillary and trabecular

muscles(PTMs)

2-Related Work

2) Graph-based segmentation algorithms

create a graph with an assigned cost in each

pixel or node

Then find a minimum cost path using graph-

searching algorithms

These methods are unable to accurately

segment complex cardiac structures such as

PTMs

have difficulties in the basal and apical slices

2-Related Work

3) Active shape model (ASM)

ACMs segment objects through energy minimization

of internal forces such as rigidity and elasticity, and

external forces such as edges.

Contour initiation is critical to the success of ACM

segmentation.

ACMs have difficulty with low contrast images.

It impose high computational costs for iterative

procedures.

Have limitations in extracting the details of PTMs

2-Related Work

4) Level-set algorithm

well-established method to segment objects in noisy

data

has difficulty in determining the stopping term,

requires strong initialization of segmenting objects.

Have high computational costs.

In summary, much research has been performed in

LV segmentation. Each algorithm has tradeoffs

among time complexity, inter- or intra-operator

variation, and accuracy in clinical practice. These

algorithms have not segmented PTMs in detail

METHOD

Endocardial contour extraction

by iterative thresholding.Epicardial contour extraction by

active contour.

(1/2).

26

Segment Endo using region growing.

To apply region growing:

1. Choose the seed point.

2. Check the neighboring pixels and add them if they are

similar.

3. Repeat step 2 for each of newly added pixel.

4. Stop if no more pixels can be added.

5. Endo threshold = MYOCmean + (2*LVstd).

27

1. Estimate the initial seed point.

LV has roughly a circular shape.

Perform circular Hough transform to ED and ES

phases in mid-ventricular slice, selected by the

user.

Select center point to be the seed point.

28

2. Mean and Standard Deviation of

Blood Signal Estimation

Edge-based region-growing from the seed point is

applied to find LV region that is nearly full-blood.

The mean and standard deviation of this region is

calculated(LVmean, LVstd).

29

3. Myoc Signal Intensity Estimation. Successive lower-bound threshold-based region-growing is

applied.

with same seed point of step 1.

Threshold = LVmean /i Start at i =1 then i increments by 0.1 each iteration.

Sudden increase threshold is one standard deviation away from MYOCmean

30

4. Endo Segmentation.

Apply region growing to Segment Endo with:

Seed point = center of image’s circular Hough transform

Threshold = MYOCmean + (2*LVstd).

31

5. Remaining images segmentation.

A seed propagation technique is applied for remaining

images.

By examining an 11 × 11 pixel window, whose center is the

center of gravity of the segmented LV region in the previous

image.

The pixel with the lowest energy is chosen as the seed point.

Repeat step 2,3 and 4 for remaining images.

32

6. LV Volume Measurement

The total blood volume of the segmented LV is

calculated from:

x is intensity.

h(x) is the histogram.

w(x) is the weighting function used for calculating

partial voxel effects.

33

METHOD

Endocardial contour extraction

by iterative thresholding.Epicardial contour extraction by

active contour.

(2/2).

34

Active Contour Model (Snake).

Introduced by Kass and Terzopoulos in 1987.

Based on energy minimization.

Energy minimization are used to compute the equilibrium configuration.

The final position of the contour will have a minimum energy (Emin).

35

Snake energy.

EElastic

EBlending

EExternal

ESnake

EConstraintsEInternal

36

1. Circular Map Generation.

Since the LV has roughly a circular shape

37

2. Edge Information Extraction and Filtering.

Use the Canny edge extractor to extract edges.

Edge information that comes from the endocardial

region is filtered out.

38

3.1. Modified External Force .

If a contour is seeded in zero gradient, areas, there

will be no sufficient external force to move the

contour.

Gradients less than threshold are set to the closest

values (along increasing radius) greater than the

threshold. 39

3.2. Movement Constraint Definition.

1. The contour is initialized at the endocardial border.

2. The initialized contour should move iteratively in

the direction of increasing radius r.

3. To constraint contour movementMYOCmax = MYOCmean + 2* LVstd.

Due to intensity variation of myocardium

MYOCmin = MYOCmean * 0.4 Regions

of signal intensity over or below MYOCmin.

40

4. Active Contour Model Segmentation.

The contour only moves in the radial direction .

Stops if it meets the movement constraint.

The average difference between contour points

before and after each iteration is calculated .

Iteration is stopped if the average difference is below

0.01 pixels, which means that the internal and

external energy is minimized.

41

5. Epicardial Contour Updating and

Coordinate Transform).

Epicardial contour can have zigzag patterns, a low-

pass filter is applied to make it smooth.

Then, the epicardial contour is transformed to

Cartesian domain again.

42

EXPERIMENTS AND RESULTS.

Data was acquired from 38 patients (15 males, their

age: 52.4 ±15.1 years).

The LV was imaged in 610 slices, 2028 phases.

A total of 339 images were segmented by ITHACA

then results compared to both manual tracing and

the commercial MASS software.

Manual tracing was performed by an experienced

physician.

43

EXPERIMENTS AND RESULTS Cont...

Metric Manual tracing ITHACA segmentation MASS software

Blood

volume

144.5 mL ±50.0 141.6 mL ±48.7 164.5 mL ±55.1

Myocardial

mass

128.1 ± 50.9 g 128.9 ± 49.0 g 129.1 ± 57.5 g

Manual intervention ITHACA segmentation MASS software

At some basal-most

slices

4.7% 5.0%

Generating endocardial

contour in most apical

slices

Doesn’t require Requires manual

intervention

Generating epicardial

contour

1.5% 51.6%

44

CONCLUSION.

ITHACA is a new algorithm introduced to

automatically segment LV.

Iterative Thresholding is used to identify the

Endocardial then ACM is used to identify the

Epicardial.

ITHACA provided substantial improvement over the

commercial MASS software in LV segmentation.

Future work will consider automation at basal slices.

45

REFERENCES: H. Lee , N. Codella , M. Cham , J. Weinsaft and Y. Wang "Automatic

left ventricle segmentation using iterative thresholding and active contour model with adaptation on short-axis cardiac MRI", IEEE Trans. Biomed. Eng., vol. 57, no. 4, pp.905 -913 2010

N. Codella, J. W. Weinsaft, M. D. Cham, M. Janik, M. R. Prince, and Y. Wang, “Left ventricle: Automated segmentation by using myocardial effusion threshold reduction and intra-voxel computation atMR imaging,” Radiology, vol. 248, no. 3, pp. 1004–1012, Sep. 2008.

AbhishekChandale,Divakarsingh “ Comparative Study of Different Technique for Medical Image Segmentation: A Survey”, vol. 11,No. 1,pp. 2196-2174,Sep.2013

Rajeshwar Dass, Priyanka, Swapna Devi” Image Segmentation Techniques”, IJECT Vol. 3, Issue 1, Jan. - March 2012

46

Automatic left ventricle segmentation

Engineering

Transcript of Automatic left ventricle segmentation