The Body-Centered Cubic (BCC) and Face-Centered Cubic (FCC) lattices have been analytically shown to be more efficient sampling lattices than the traditional Cartesian Cubic (CC) lattice, but there has been no estimate of their visual comparability. Two perceptual studies (each with N=12 participants) compared the visual quality of images rendered from BCC and FCC lattices to images rendered from the CC lattice. Images were generated from two signals: the commonly used Marschner-Lobb synthetic function and a computed tomography scan of a fish tail. Observers found that BCC and FCC could produce images of comparable visual quality to CC, using 30-35 percent fewer samples. For the images used in our studies, the L_2 error metric shows high correlation with the judgement of human observers. Using the L_2 metric as a proxy, the results of the experiments appear to extend across a wide range of images and parameter choices.

Optical coherence tomography (OCT) is a non-invasive, depth resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We present an automatic segmentation algorithm to detect intra-retinal layers in OCT images acquired from rodent models of retinal degeneration. We adapt Chan-Vese's energy-minimizing active contours without edges for OCT images, which suffer from low contrast and are highly corrupted by noise. We adopt a multi-phase framework with a circular shape prior in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional. The results from various synthetic experiments and segmentation results on 20 OCT images from four rats are presented, demonstrating the strength of our method to detect the desired retinal layers with sufficient accuracy and average Dice similarity coefficient of 0.85, specifically 0.94 for the the ganglion cell layer, which is the relevant layer for glaucoma diagnosis.

This paper presents a user study of the visual quality of an imaging pipeline employing the optimal body-centered cubic (BCC) sampling lattice. We provide perceptual evidence supporting the theoretical expectation that sampling and reconstruction on the BCC lattice offer superior imaging quality over the traditionally popular Cartesian cubic (CC) sampling lattice. We asked 12 participants to choose the better of two images: one image rendered from data sampled on the CC lattice and one image that is rendered from data sampled on the BCC lattice. We used both synthetic and CT volumetric data, and confirm that the theoretical advantages of BCC sampling carry over to the perceived quality of rendered images. Using 25% to 35% fewer samples, BCC sampled data result in images that exhibit comparable visual quality to their CC counterparts.