The 2D transfer function based on scalar value and gradient magnitude (SG-TF) is popularly used in volume rendering. However, it is plagued by the boundary-overlapping problem: different structures with similar attributes have the same region in SG-TF space, and their boundaries are usually connected. The SG-TF thus often fails in separating these structures (or their boundaries) and has limited ability to classify different objects in real-world 3D images. To overcome such a difficulty, we propose a novel method for boundary separation by integrating spatial connectivity computation of the boundaries and set operations on boundary voxels into the SG-TF. Specifically, spatial positions of boundaries and their regions in the SG-TF space are computed, from which boundaries can be well separated and volume rendered in different colors. In the method, the boundaries are divided into three classes and different boundary-separation techniques are applied to them, respectively. The complex task of separating various boundaries in 3D images is then simplified by breaking it into several small separation problems. The method shows good object classification ability in real-world 3D images while avoiding the complexity of high-dimensional transfer functions. Its effectiveness and validation is demonstrated by many experimental results to visualize boundaries of different structures in complex real-world 3D images.

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Lung cancer is the leading cause of cancer-related death worldwide and this also stimulates the development of various computeraided diagnosis (CAD) systems. But the conventional lung segmentation methods can't satisfy the needs of the clinicians in lung cancer diagnosis and surgery. It is very important to provide a segmentation and visualization framework for the clinicians instead of radiologists in outpatient service. Therefore we propose a visually guided method based on a 2D feature space and spatial connectivity computation to reduce the dependence on the radiologists for lung segmentation and visualization. Our framework consists of three main processing steps. Firstly, a 2D feature space of CT scalar versus gradient magnitude is constructed. Secondly, the attribute distribution region of the lungs is selected in the 2D feature space, and then the lungs are extracted from the determined voxels by spatial connectivity computation. Finally, the lungs and pulmonary nodules are visualized simultaneously with different colors and opacities in volume rendering. Experimental results show that the proposed framework is efficient for outpatient service and can provide an intuitive segmentation process and nodules information.