DISCUSSION AND CONCLUSIONS
In this paper, we have presented an automated technique to segment transaxial data in 3D cardiac SPECT studies.The
proposed methodology incorporates a priori knowledge of the LV structure and selectively employs various image
processing and computer vision techniques to extract voxels corresponding to the left ventricle from the 3D dataset.
This segmentation approach is an inherent part of a system to automatically determine the 3D orientation of the LV.
Information regarding the pose of the LV is essential for obliquely slicing the 3D dataset to accurately quantify and
visualize the blood perfusion in the myocardium. The volume segmentation methodology discussed here has been
tested and evaluated in conjunction with the 3D LV orientation determination process. Since the presence of any other
objects (in terms of voxels) besides the LV interferes with the pose determination process, accurate computation of
the LV pose correlates well with correct object segmentation. Fifty(50) Thallium-201 and fifty(50) Tc-99m cardiac
SPECT studies were used to evaluate the segmentation methodology. A qualitative analysis of the segmentation technique using these 100 datasets has yielded quite encouraging results. The ability to accurately determine the orientation of the LV [10] without human intervention quantitatively corroborates the correctness of this segmentation
approach.
ACKNOWLEDGMENTS
This work was supported in part by grants R 29 LM04692 from the National Library of Medicine and 1 RO1
HL42052-01 from the National Institutes of Health.
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METHODOLOGY AND RESULTS
The algorithm currently under development consists of a sequence of tasks performed either sequentially on each
slice of the 3D data or in a unified way to the entire data volume. The various steps in the algorithm are briefly outlined here.
The first step in the processing involves intelligent adjustment of the range of intensity values in the volume. This
step is followed by appropriate scaling of intensity values in the dataset to lie within the determined intensity range.
Before the GLH is computed, the range of slices for the histogram, is determined. This range is determined by computing the energy in each slice of the 3D dataset. If the slice with the maximum energy lies in the latter slices of the
volume, suggesting the presence of the liver in the data, the latter slices are excluded when computing the GLH. Once
the histogram is computed, the entries of the histogram are passed through a low-pass-filter to eliminate any possible
noise in the statistics. The values in this “smoothed” histogram are then fit to a decaying exponential as shown in Figure 2. The exponential fit to the histogram is then employed to compute the optimal threshold value for the 3D data.
The optimal threshold value is chosen to be that intensity value at which the slope of the exponential fit approaches
zero. This computed threshold value is then restrained within predetermined limits based on the isotope used for the
SPECT acquisition.
The contents of each transaxial image are then processed using the morphological technique [9] of erosion using a 2x2 structuring element. The
erosion process disconnects and thins out the walls of myocardium making it feasible to separate the two ventricles (left and right). A labeling
technique is next employed to identify and mark each distinct mass in each
image. All smaller masses except the largest one and those lying within a
Fit
pre-determined region (based on analysis of the mid-ventricular slice and
Histogram
in general the cardiac anatomy) in each image are deleted. At this stage all
remaining masses are taken to be part of the left ventricular myocardium.
Threshold
Each segmented slice is then dilated to recover any possible viable data
voxels. 3D connectivity of these 2D masses is also analyzed and the single
largest connected mass in the 3D volume dataset is thus extracted.
Figure 2
The final stages of this volume segmentation algorithm further
excludes undesirable regions. This stage of processing is
necessitated by the presence of voxels pertaining to the liver
and other organs in the vicinity of the heart in a cardiac SPECT
acquisition. This is achieved by limiting the radial expanse of
the segmented data in each slice and by incrementally increasing the threshold value of each transaxial slice until the cavity
(region of low voxel intensity) is visible in the mid-ventricular
slice. This final step concludes the segmentation algorithm and
results in a 3D dataset including only those voxels that are part
of the left ventricular myocardium. The result of the application of this algorithm on the 3D dataset in Figure 1 is illustrated in Figure 3. It must be emphasized here that the entire
process was carried out without any human intervention. The
applications of this algorithm are many, especially in the realm
of SPECT data analysis. The authors of this paper are currently
employing the results of this segmentation algorithm for automatic determination of the left ventricle (LV) orientation and
medial axis [10].
Figure 3: Result of automatic segmentation of
3D data in Figure 1.
AUTOMATIC SEGMENTATION OF 3D CARDIAC SPECT IMAGERY
Rakesh Mullick and Norberto F. Ezquerra*
Dept. of Electrical Engineering and *College of Computing
GVU Center, Georgia Institute of Technology, Atlanta, GA 30332
Email: rakesh@cc.gatech.edu
ABSTRACT
The automatic visualization and quantitative analysis of cardiac SPECT data requires the ability to automatically segment and extract voxels representing the heart. The attributes of the 3D data make this task quite
challenging. In this paper, we attempt to address these issues and propose an algorithm which successfully
detects the voxels belonging to the Left Ventricle (LV) of the heart and filters out the noise and all other
interfering organs. The algorithm relies on various image processing and pattern analysis techniques as well
as the constraints put forward by the anatomy. The final outcome of this algorithm is a segmented 3D dataset
containing voxels pertaining only to the LV. This filtered dataset is then employed for automatic determination of LV orientation. The results show that this methodology is a very promising approach to segmentation
of cardiac SPECT imagery.
INTRODUCTION
Significant work in the area of segmentation of medical imagery has been limited to high resolution magnetic resonance images [1, 2, 3, 4]. Some of these algorithms also employ techniques based on expert systems, neural networks
and other high level image understanding systems. Research in the area of segmentation of SPECT data in particular,
has been directed towards accurate volume determination of organs [5, 6, 7]. Various techniques [5, 6] for optimum
segmentation based on a gray level histogram (GLH) and a V filter have been suggested in the literature. A comparative study of the image segmentation methods for volume quantification in SPECT [8] by Long et. al implies that a
method based on 3D edge detection is most suitable for minimal operator intervention, accuracy, and consistency in
estimation of object volume. In this paper, we present a methodology which consists of an algorithm unifying various
image processing and computer vision techniques. In addition, more recent techniques of morphological image processing and connected component labeling have also been employed to further enhance the segmentation process [9].
DATA CHARACTERISTICS
Inherent limitations of SPECT imaging, yields a challenging
segmentation problem. The specific aim in nuclear medicine,
and in particular Tl201 and Tc99m cardiac SPECT imagery is to
study the physiology and not the structure of the imaged organ.
However, it is desirable to infer structural information from
these images. Thus, the functional information can be misleading, especially in the presence of hypoperfused regions in the
data. This can make the interpretive task increasingly difficult.
Figure 1: Transaxial slices from a Tc-99m cardiac SPECT acquisition.
The constant “beating” of the heart has an additional blurring
effect on this inherently low-resolution imaging modality. Current resolutions of such imaging systems range from 4 mm to 7
mm, resulting in small sized 3D datasets. Thus small errors (≤ 1
voxel) in localization of extracted features can lead to considerable anomalies in the quantitative analysis of the LV [7]. In
addition, Compton scattering and other physical phenomena
make the data quite noisy. Therefore, the data can be viewed as
low-resolution, noisy, temporally integrated, and sparse.
Therein lies the importance of proper segmentation, to minimize possible data loss and maximize the available data. A
sample normal patient volume (3D) dataset in the form of consecutive transaxial slices is presented in Figure 1.