Abstract
Molecular imaging can detect abnormal functions of living tissue. Functional abnormality in gene expression or metabolism can be represented as altered volume or probe intensity. Accurate measurement of volume and probe intensity in tissue mainly relies on image segmentation techniques. Thus, segmentation is a critical technique in quantitative analysis. We developed an automatic object marker-driven three dimensional(3D) watershed transform for quantitative analysis of functional images. To reduce the discretization error in volume measurement less than 5%, the size criteria for digital spheres were investigated to provide the minimum volume. When applied to SPECT images, our segmentation technique produced 89% or higher accuracy in the volume and intensity of tumors and also showed high correlation with the ground truth segmentation (ρ> 0.93). The developed 3D method did not require interactive object marking and offered higher accuracy than a 2D watershed approach. Furthermore, it computed faster than the segmentation technique based on the marker-driven gradient modification.
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Chae, Ys., Kim, D. (2007). Automatic Marker-Driven Three Dimensional Watershed Transform for Tumor Volume Measurement. In: Szczuka, M.S., et al. Advances in Hybrid Information Technology. ICHIT 2006. Lecture Notes in Computer Science(), vol 4413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77368-9_15
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DOI: https://doi.org/10.1007/978-3-540-77368-9_15
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