الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
CardioVascular Diseases (CVDs) are considered the number one cause of mortality
and the major health concern according to recent statistics worldwide. In Europe,
nearly half (47%) of all deaths are from the CVDs. About 85% of overall mortality
of middle- and low-income countries is due to the CVDs too. Accurate early
detection can effectively reduce the mortality rate caused by the CVDs. Left
ventricle of the heart is very important in early detection and diagnosis of CVDs.
Image processing in the field of biomedical analysis plays an important role in the
detection of CVDs through the cardiac segmentation. To determine the shape of the
left ventricular cavity, cardiac image segmentation is performed.
Many studies and researches introduced several segmentation techniques. Image
segmentation techniques can be divided into two main groups. The first group
contains the classical techniques for image segmentation and the second group
contains the random walk segmentation techniques.
This thesis tackles the cardiac segmentation problem. Implementation of different
segmentation techniques has been carried out and comparative studies are done to
determine the most accurate and fastest segmentation techniques. The comparison
is done by evaluation of the quality and speed of segmentation. The quality is
measured using similarity Dice Metric (DM) coefficient, Peak Signal to Noise
Ratio (PSNR) and HauSdorff distance (HS). The average execution time is
measured in each technique to determine the speed of execution. Three
Dimensional (3D) multi-slice short axis Cardiac Magnetic Resonance (CMR)
database for different case studies is used to demonstrate the implementation results
of the different segmentation techniques.
A comparative study has been carried out on the first group of different classical
segmentation techniques. The classical segmentation techniques can be categorized
as: edge based techniques such as Caselles technique and region based techniques
such as Bernard-Friboulet technique, Li technique, Lankton technique, Shi-Karl
technique and Chan-Vese technique.
Edge-based techniques such as Caselles technique gives better results when the
initialization step is suitable and the image has high intensity gradient at the edge
between the objects in the image.
Wideband region-based techniques such as Bernard-Friboulet and Li give worse
similarity results as they tend to oversegment the image and take much more time.
Narrowband region-based techniques such as Shi-Karl, Chan-Vese and Lankton-
Yezzi algorithms are not disturbed by the presence of bright region on a wide range
region far from the initial contour or by the smooth variations of intensity inside the
object. The image results of Lankton-Yezzi segmentation technique is sensitive to
Abstract
IV
initialization. Shi-Karl, Chan-Vese techniques give good segmentation results in
term of both the similarity and computation time. Shi-Karl technique gives the best
results among the first group of segmentation techniques in similarity and execution
time.
In this thesis, another comparative study has been carried out by implementation of
the second group of segmentation techniques. Random walk is a multi-label image
segmentation technique that is based on graph-theoretic electrical potentials.
Random walk technique for image segmentation gives better results in terms of
both the similarity and computation speed than Shi-Karl segmentation technique.
The second group contains three random walk techniques; which are Basic Random
Walk (BRW) with seeds technique, High Speed Random Walk (HSRW) with precomputations
technique and Extended Random Walk (ERW) with priors technique.
Random walk techniques depend on a small set of marked pixels or seeds. The
Experimental results show that BRW technique with seeds gives high quality
results in medium speed. ERW technique results are the most accurate
segmentation technique because it takes in consideration more characteristics about
the regions due to the incorporated priori and the execution of segmentation has
medium speed. HSRW technique gives high quality results are close to the
corresponding similarity measurements in basic random walk technique with seeds.
The pre-computations in HSRW offline mode reduce the execution online time.
HSRW technique performs the segmentation process in very high speed.
A novel segmentation technique for cardiac image segmentation is proposed by
mixing the characteristics of the HSRW with pre-computation model and ERW
with prior model to improve the segmentation. The speed of segmentation in the
proposed technique is very high. The proposed technique for cardiac segmentation
is a robust and very accurate technique for the delineation of the Left Ventricle
(LV) endocardium and epicardium to segment the cavity and myocardium of LV of
the human heart. The LV performance parameters such as LV volume in diastolic
and systolic phases, Stroke Volume (SV) and Ejection Fraction (EF) of the LV can
be estimated from the segmented images. This technique considers the intensity
values and performs efficiently with blur images and weak edges. The LV
performance estimation is based on the accuracy of the segmentation and it helps
the doctors in early detection and monitoring of CVDs.