GIT-GVU-93-29

Automatic Segmentation of 3D Cardiac SPECT Imagery

Rakesh Mullick
Norberto F. Ezquerra

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.

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].

SPECT data, automatic segmentation, medical imaging, 3D dataset, gray level histogram (GLH), V filter, image processing, computer vision

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