Background CMR typically quantifies LV mass (LVM) via manual planimetry (MP), but this approach is time consuming and does not account for partial voxel components - myocardium admixed with blood in a single voxel. Automated segmentation (AS) can account for partial voxels, but this has not been used for LVM quantification. This study used automated CMR segmentation to test the influence of partial voxels on quantification of LVM. Methods and Results LVM was quantified by AS and MP in 126 consecutive patients and 10 laboratory animals undergoing CMR. AS yielded both partial voxel (ASPV) and full voxel (ASFV) measurements. Methods were independently compared to LVM quantified on echocardiography (echo) and an ex-vivo standard of LVM at necropsy. AS quantified LVM in all patients, yielding a 12-fold decrease in processing time vs. MP (0:21±0:04 vs. 4:18±1:02 min; p<0.001). ASFV mass (136±35gm) was slightly lower than MP (139±35; Δ=3±9gm, p<0.001). Both methods yielded similar proportions of patients with LV remodeling (p=0.73) and hypertrophy (p=1.00). Regarding partial voxel segmentation, ASPV yielded higher LVM (159±38gm) than MP (Δ=20±10gm) and ASFV (Δ=23±6gm, both p<0.001), corresponding to relative increases of 14% and 17%. In multivariable analysis, magnitude of difference between ASPV and ASFV correlated with larger voxel size (partial r=0.37, p<0.001) even after controlling for LV chamber volume (r=.28, p=0.002) and total LVM (r=0.19, p=0.03). Among patients, ASPV yielded better agreement with echo (Δ=20±25gm) than did ASFV (Δ=43±24gm) or MP (Δ=40±22gm, both p<0.001). Among laboratory animals, ASPV and ex-vivo results were similar (Δ=1±3gm, p=0.3), whereas ASFV (6±3gm, p<0.001) and MP (4±5gm, p=0.02) yielded small but significant differences with LVM at necropsy. Conclusions Automated segmentation of myocardial partial voxels yields a 14-17% increase in LVM vs. full voxel segmentation, with increased differences correlated with lower spatial resolution. Partial voxel segmentation yields improved CMR agreement with echo and necropsy-verified LVM.

Visually convincing content-aware image resizing, which preserves semantically important image content, has been actively researched in recent years. This paper proposes a resizing detector that reveals the trace of seam carving and seam insertion. To unveil the evidence of seam carving, we exploit energy bias of seam carved images. In addition, the correlation between adjacent pixels is analyzed to estimate the inserted seams. Empirical evidence from a large database of test images demonstrates the superior performance of the proposed detector under a variety of settings.

Cardiac disease is the leading cause of death in the world. Quantification of cardiac function is performed by manually calculating blood volume and ejection fraction in routine clinical practice, but it requires high computational costs. In this study, an automatic left ventricle (LV) segmentation algorithm using short-axis cine cardiac MRI is presented. We compensate coil sensitivity of magnitude images depending on coil location, classify edge information after extracting edges, and segment LV by applying region-growing segmentation. We design a weighting function for intensity signal and calculate a blood volume of LV considering partial voxel effects. Using cardiac cine SSFP of 38 subjects with Cornell University IRB approval, we compared our algorithm to manual contour tracing and MASS software. Without partial volume effects, we achieved segmentation accuracy of 3.3mL±5.8 (standard deviation) and 3.2mL±4.3 in diastolic and systolic phases, respectively. With partial volume effects, the accuracy was 19.1mL±8.8 and 10.3mL±6.1 in diastolic and systolic phases, respectively. Also in ejection fraction, the accuracy was-1.3%±2.6 and-2.1%±2.4 without and with partial volume effects, respectively. Results support that the proposed algorithm is exact and useful for clinical practice.

This paper addresses the way to compose paronamic images from images taken the same objects. With the spread of digital camera, the panoramic image has been studied to generate with its interest. In this paper, we propose a panoramic image generation method using scaling and rotation invariant features. First, feature points are extracted from input images and matched with a RANSAC algorithm. Then, after the perspective model is estimated, the input image is registered with this model. Since the SURF feature extraction algorithm is adapted, the proposed method is robust against geometric distortions such as scaling and rotation. Also, the improvement of computational cost is achieved. In the experiment, the SURF feature in the proposed method is compared with features from Harris corner detector or the SIFT algorithm. The proposed method is tested by generating panoramic images using 640×480 images. Results show that it takes 0.4 second in average for computation and is more efficient than other schemes.