Automatic Segmentation of Myocardial Blood Flow in Left Ventricle From a PET Cardiac Image Using Watershed and Graphcut Algorithm

(1)

International Journal of Recent Advances in Engineering & Technology (IJRAET)

_______________________________________________________________________________________________

ISSN (Online): 2347 - 2812, Volume-4, Issue -7, 2016 69

Automatic Segmentation of Myocardial Blood Flow in Left Ventricle From a PET Cardiac Image Using Watershed and Graphcut

Algorithm

1Sindhu C S, ²Shaiyni R, ³Baburaj M

1,2Department of ECE College of Engineering, Thalassery Po Eranjoli, PIN-670107

3Department of ECE Govt Engineering College, Kannur, PIN-670563

Abstract— The segmentation of PET cardiac image is necessary to reveal the Myocardial blood flow during rest and stress and to detect the cardiac diseases in early stages.

This study highlights the use of fast automatic graph-cut segmentation for the separation of myocardial blood flow in the left ventricle, from PET cardiac images. Acquired noisy PET cardiac image can be de-noised using Median filter which remove almost all noises without losing edge information. Pre-segmentation using watershed transform speed up the segmentation process and improve the segmentation quality. The early studies reveals that graph- cut segmentation give both background and regional information and can be used to segment cardiac image.

The whole work has been done on the MATLAB 8.3 platform.

Index Terms Biomedical Signal processing, PET MBF, Image Segmentation, Graph-cut, MATLAB

I. INTRODUCTION

Positron emission tomography (PET) myocardial perfusion imaging in concert with tracer kinetic modelling affords the assessment of regional myocardial blood flow (MBF) of the left ventricle in absolute terms .Assessment of MBF both at rest and stress provides insight into early and subclinical abnormalities in coronary arterial vascular function. Quantitative approaches that measure MBF with PET identify multi vessel coronary artery disease. Cardiac PET imaging with dynamic or static image acquisition is frequently applied to measure regional myocardial perfusion [1,2]

The current difficulties associated with accurate geometric interpretation of PET data pose fundamental limitations to the full utilization of this powerful imaging modality for planning more accurate and effective radiotherapy treatments. If the full potential of PET is to be properly assessed and utilized, improvement in this situation is an immediate need. So segmentation of PET images is required for accurate delineation of target volume. Manual segmentation of cardiac image is a long and difficult process. PET scans produce data highly contaminated by noise. As a result, dynamic curves of radiotracer activity that are derived from these data are error-prone and the resulting perfusion estimates are usually biased [3].

Image segmentation is a process of the image analysis and the image comprehension. The process of partitioning the image into multiple regions is called image segmentation. All of the pixels in a region are similar with respect to some characteristics. Till today, there are a large number of methods present that can extract the required foreground from background[4],[5]

.However most of these methods are solely based on boundary or regional information which has restricted the segmentation result to a large amount. Since the graph cut based segmentation method which provide both boundary and regional information. Furthermore, graph-cut based method is efficient and accepted worldwide since it can achieve globally optimal result for energy function.

This paper introduces a new method for automatic segmentation of PET cardiac image that make use of edge information in a graph cut optimization frame work. This technique from combinatorial optimization has already demonstrated a great potential for solving many problems In Boykov and Jolly (2001) is that it first demonstrated how to use binary graph cuts to build efficient object extraction tools for N-D applications[6].

Global optima, practical efficiency, numerical robustness, ability to fuse a wide range of visual cues and constraints, unrestricted topological properties of segments, and applicability to N-D problems. Graph cuts based approaches to object extraction have also been shown to have interesting connections with earlier segmentation methods such as snakes, geodesic active contours, and level-sets. We present an application of image segmentation via s/t graph cuts in PET cardiac image.

Slices of images from scanner is segmented using our segmentation method which provide easiness for analysing the images and help to early detection of cardiac diseases.

II. PROPOSED METHOD

1) PET Cardiac Image Segmentation: judgement of myocardial perfusion defects due to stress, with PET, have been firmly accomplished as an eminent diagnostic tool for the evaluation of patients with suspected CAD [1]. However, there are distinct limitations with visual or

(2)

_______________________________________________________________________________________________

semi quantitative assessment of regional myocardial perfusion defects that may be get over by absolute quantification with PET By assessing MBFs non- invasively, PET may associate early functional and structural abnormalities of the coronary artery circulation before its advancement to symptomatic CAD ensues

The requirement of this segmentation method came from cardiac positron emission tomography (PET) image processing. One problem in cardiac PET image processing is how to extract the myocardium tissue, the region of interest (ROI), from the input images. The input PET images contain high level of noise around the myocardium. This is formed due to the radiotracer appearing in the blood pool before it is all absorbed by the myocardium tissue. In order to extract the region of interest (ROI), we need to create a robust and effective image segmentation method.

In this method, a user only require to set hard constraints indicating part of the target, and then the segmentation method will generate a satisfactory result. In the human myocardium PET image segmentation, the region of interest for each PET image is the human myocardium tissue. Since the locations of human myocardium do not change too much between each set of input PET images, a user only needs to set a bounding box which roughly encloses the myocardium tissue in one input PET image, and then this segmentation method can generate the accurate ROI for each input image automatically.

The main goal of cardiac PET image segmentation is to first develop a segmentation method to extract the myocardium from the input PET images, and then based on the segmentation results, implement further tasks. An example of myocardium PET image is shown in Figure 4.1(a). In this example, the input myocardium PET images are generated by the PET scanner with FDG (fluorodeoxyglucose) as tracer.

2). Previous works

Using Deformable model a segmentation is implemented in 2008 for statistical analysis initialization of deformation contours [7].Heart boundaries (ROI)are tracked with the help of two moving curves using level set and Bayesian classifier clustering algorithm using mean shift used for centre location. In geodesic deformable models introduced a force field for influencing the speed term . [8]

The segmented volumes of the cardiac structures correspond well the simulated volumes with independent component analysis ICA [9] and with ICA on healthy volunteer data positions of these structures made preliminary tests , from the segmented portion of image used an iterative process to calculate ICA for optimization and VOI is also calculated.

3). Proposed method

The proposed method includ the following stages.

A. Pre-processing

Acquired image contain noises and pre-processing accompany a median filter for de-noising which will increase the accuracy of segmentation. A nonlinear process useful in reducing salt and pepper and impulsive noise by preserving the edge information. A window is sliding over the image and median value of the windowed image is assigned to the corresponding pixel.

B. Graph-cut segmentation

Graph-cut algorithm is initialised by Automatic identification of one or more seed points representing the background and object serves as segmentation hard constraints. The general function C calculated on image segmentation f follows the Gibbs model [10]

C (f) = C data(f)+ C smooth(f) (1)

The minimize C (f), a special class of arc weighted graphs Gst = (NU{s, t|}, E) is employed. In addition to the set of nodes N corresponding to pixels of the image I, the node set of G_stContains two specific end nodes, namely the source s and the sink t. These terminals are hard linked with the segmentation seed points. The arcs E in G_stcan be categorised as n-links and t-links. The n- links connect pairs of neighbouring pixels and smoothness term for determining the cost C_smooth (f) .The t links connect pixels and terminals with costs derived from the data term C _data(f). An s-t cut in G_sis a set of arcs whose removal partitions the node into two disjoint subsets.

The minimum s-t cut problem and its dual, polynomial time algorithms can be used for solving the maximum flow problem which are classic combinatorial problems .Figure 1 shows simple example of the use of graph cut for segmentation. Here O and B sets of image pixels are corresponding to object and background seeds. All object pixels are connected to the object seed terminal or background seed terminal. Let each

Fig. 1. (a)Image with seeds (b) Segmentation results (c) Graph (d) Cut

Image pixels i_k take a binary label L where obj and bgd represent the object and background labels, respectively. The labelling vector L={L1,L2,..lI} defines the resulting binary segmentation .The cost function C that is minimized to achieve optimal labeling may be defined as a λ weighted combination of a regional

(3)

_______________________________________________________________________________________________

property term R(L) and a boundary property term B(L) [10].

R_p(obj) is cost associated with labelling pixel p as object and R_p (bjd) is cost of labelling the same pixel as background.

B_(p,q) is cost associated with a local labelling

discontinuity between neighbouring pixels p, q. The minimum s-t cut problem can be solved by finding a maximum flow from the source s to the sink t. The max flow algorithms can be categorised as Push relabel methods and augmenting path methods

i) Algorithm of Graph-cut Segmentation Graph-cut segmentation algorithm can be delineated as follows

1. Create an arc-weighted directed graph corresponding in size and dimensionally to the image to be segmented.

2. Identify object and background seeds example points required to be part of the background object in the final segmentation .Create two special graph nodes, source s and sink t. Connect all seeds with either the source or the sink node based on their object or background label

3. Associate appropriate arc cost with each link of the formed graph according to table

4. To determine the graphcut, use one of the available maximum flow graph optimization algorithms 5. The marginal s-t cut solution identifies the graph

nodes that correspond to the image boundaries separating the objects and the background.

ii). Automatic graph cut

The most used method for reducing the computational time for the graph cut related algorithm is based on the reduction of the graph nodes during the reconstruction of graph .Conventionally, each pixel in the image will be viewed as one node in the graph .Thus with the increase of image resolution, the graph will be very big and and make the computation of the graph cut slowly .Since the object occupy a small region in the whole image , graph size can be reduced [11-12] The most popular approach used in graph cut is watershed algorithm[13]In water shed algorithm, the gradient image is viewed as a topological surface while gradient values are regarded as the height. After the pre segmentation of watershed, the pixel value of the new point will be the average intensity of each cluster. Thus weight of the t links can be set by comparing the value of the super point with the established histogram of the object and background [11]

,only the region around the boundary are considered and segmented by watershed based graph cut.

De noised image pre-processed using watershed algorithm and myocardial blood flow segmented using graph-cut algorithm. HSV Colour mapping is applied to the segmented image. The block diagram of proposed method is shown in figure2.

III. EXPERIMENTAL RESULT

In order to illustrate the implementation process of the proposed algorithm we use pet cardiac image with FDG as tracer, In Figure 3.(a), the donut shaped region is the myocardium tissue which is the region of interest (ROI) that we want to extract from the input image in the experiment. As can been seen in figure 3.(a), the input image is corrupted by noise and sampling artefacts. De noised image is shown in figure 3(b).

The result of segmentation using conventional is shown in figure 3(c). Region partitioning using graph-cut algorithm implemented in fig 3(d). K-mean clustering and for clarity,

Fig. 2. Block diagram of proposed method

Fig. 3. (a)Blood pool of the left ventricle in slice of image from PET image scanner (b) De noised

(4)

_______________________________________________________________________________________________

image.(c)segmentation using conventional method ( d ) PET cardiac image segmented using proposed method finally colour mapping is also used . Images during rest and stress condition are segmented like this way. And found out the difference between the segmented images in two conditions. From these output early detection of cardiac diseases are possible. From this experiments it is clear that Graph cut segmentation form an output which give more detailed output image than existing method.

IV. CONCLUSION

In this work myocardial blood flow in left ventricle of PET cardiac image is automatically segmented using graph cut algorithm. Pre- processing is done by means of median filtering which reduces the noise by preserving the edge information Key points are found using water shed algorithm, which made the graph-cut segmentation faster. Colour mapping is introduced for increasing visual information. Method overcomes many shortcomings of existing methods, and inaccuracies caused by low resolution and unavoidable corruptions, which are unable to be effectively solved by other PET image segmentation techniques. Experiments have shown that this method yields acceptable segmentation results, and is successfully applied in the human myocardium image .The segmented PET images are used for checking the possibility of cardiac diseases.

Therefore, it has a wide range of perspectives in medical applications for early detection of cardiac diseases.

However without ground truth values in human studies, reference could not obtain . Further large studies required to fortify the application in a clinical environment.and more data needed for that. The future research will focus on the following aspects also.Implement Geodesic graph-cut segmentation method which may give better result than this work and to create a GUI for the segmentation which can segment more images in short time.

V. ACKNOWLEDGMENT

The authors express sincere thanks to Abdul Shukoor, Department of Radiology, MIMS Hospital for his suggestions and his help in image acquisition.

REFERENCES

[1] Thomas H. Schindler, Heinrich R. Schelbert, Alessandra Quercioli, “Cardiac PET Imaging for the Detection and Monitoring of Coronary Artery Disease and Microvascular Health”,VOL.3 PP- 623-642, Maryland, 2010.

[2] Paolo G. Camici, Ornella E. Rimoldi “The Clinical Value of Myocardial Blood Flow

Measurement”, The Journal of nuclear medicine, Vol. 50 , No. 7 , pp. 1076-1088 , July 2009 [3] L. Golanowski, R.A. deKemp, R.S. Beanlands,

T.D. Ruddy, “Computer simulations and dynamic PET studies”, Proceedings of the 22th International Conference, IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, pp. 10961099, 2000.

[4] Rafael C Gonzals, Richard E Woods Steven L .Eddins, “Digital Image Processing using Matlab” ,Second edition

[5] N.Pal and S.Pal “A review on image segmentation techniques”:, pattern recognition vol.26.no.9 pp 1277-1294 sep 1993

[6] Y. Boykov, V. Kolmogorov, “An experimental comparison of mincut/ max-flow algorithms for energy minimization in vision, IEEE trans on PAMI,vol.26,no.9.pp.1124-1137,2004.

[7] Anu Juslin , Jussi Tohka “Unsupervised Segmentation of Cardiac PET Transmission Images for Automatic Heart Volume Extraction”,Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, pp.1077-1080 Aug 30-Sept 3,2006.

[8] Renato Dedic, Madjid Allili, Roger Lecomte, Adbelhamid Benchakroun, “Cardiac PET Image Segmentation by a Deformable Model with a Force Field Driven Speed Term”, IEEE Nuclear Science Symposium , vol.4no.8 pp.4399- 4403,2008.

[9] M. Magadn, A. Kivimki, U. Ruotsalainen, “ICA Separation of functional components from dynamic cardiac PET data”, In proc. of IEEE Medical Imaging Conference (MIC2003), pp.

2618-2622, 2003

[10] Milan Sonka,Vaclav Hlavac,Roger Boyle,

“Image processing,Analysis and Machine Vision”, Third edition Cengage Learning, 2008 [11] Y. Peng, R. Liu, “Object segmentation based on

watershed and graph cut,, International congress on CISP, pp.1431-1435,2010.

[12] A. X. Falcao, J. K. Udupa, F. K. Miyazawa, “An ultra-fast usersteered image segmentation paradigm: live wire on the fly”, IEEE trans on Medical Imaging,vol.19,no.1,pp.55-62,2002 [13] Z. Yu, M. Xu, Z. Gao, “Biomedical image

segmentation via constrained graph cuts and presegmentation”, International conference of the IEEE on EMBC, pp.5714-5717,2011

