We propose a new multiframe algorithm to enhance the spatial resolution of frames in video sequences. Our technique specifically accounts for the possibility that motion estimation will be inaccurate and compensates for these inaccuracies. Experiments show that our multiframe enhancement algorithm yields perceptibly sharper enhanced images with significant signal-to-noise ratio (SNR) improvement over bilinear and cubic B-spline interpolation.

No abstract

No abstract

No abstract

The Mumford-Shah model is extensively used in image segmentation. Its energy functional causes the content of the segments to remain homogeneous and the segment boundaries to become short. However, the problem is that optimization of the functional can be very slow. To attack this problem, we propose a reduced two-phase Mumford-Shah model to segment images having one prominent object. First, initial segmentation is obtained by the k-means clustering technique, further minimizing the Mumford-Shah functional by the Douglas-Rachford algorithm. Evaluation of segmentations with various error metrics shows that 70 percent of the segmentations keep the error values below 50%. Compared to the level set method to solve the Chan-Vese model, our algorithm is significantly faster. At the same time, it gives almost the same or better segmentation results. When compared to the recent k-means variant, it also gives much better segmentation with convex boundaries. The proposed algorithm balances well between time and quality of the segmentation. A crucial step in the design of machine vision systems is the extraction of discriminant features from the images, which is based on low-level segmentation which can be obtained by our approach.

14149 Background: Pancreatic cancer is the second most common gastrointestinal malignancy in the United States, where it ranks 4th among all deaths caused by cancers. Early detection of pancreatic cancer remains a challange. Methods: MR images with different T1 and T2 weighting from the anatomical regions with the same imaging parameters were obtained. ISODATA segmentation algorithm, a multivariate method was used to reliable detect the various clusters in the data sets. Results: In all the four subjects, the pancreatic region was found to be from a different (and unique) cluster. This cluster covered the pancreatic region in all the four subjects. Conclusions: The ISODATA algorithm presented could detect the pancreatic region without manual tracing. No significant financial relationships to disclose.

the use of dynamic MRI (dMRI) to assess multi-compartment prolapse has added to the understanding of anatomical defects within the pelvic floor. We sought to determine the prevalence of defecatory dysfunction in women with MRI-identified posterior vaginal wall defects (MRIPD). MATERIALS AND METHODS: We retrospectively reviewed data of women who presented to a tertiary referral center for pelvic medicine and underwent a pelvic dMRI. At the time of dMRI, patients were administered an intra-rectal and intra-vaginal mixed solution containing ultrasound gel, barium, and gadolinium. MRI was performed on 1.5T and 3T scanners: Axial, coronal, and sagittal fast T2 sequences (HASTE, SSFSE). Multiple dynamic mid sagittal slices were acquired at rest and during maximal pelvic floor straining. Prolapse of all vaginal compartments was measured and graded by published guidelines. MRIPD was considered present if grade 1 or higher. Physical exam posterior defect (PEXPD) was considered present if Baden-Walker grade 1 or higher. Defecatory symptoms were obtained from the Pelvic Floor Distress Inventory and patient presenting complaint. Defecatory symptoms, MRIPD, and PEXPD were analyzed using logistic regression to calculate adjusted odds ratios; confidence intervals of 95% and p-values of <0.05 were considered significant. RESULTS: Between 1/1/2013 and 7/1/2014, 116 patients underwent dMRI. Median age was 61 years (range 29-87); mean BMI was 26.5 kg/m 2 (range 19-41); median vaginal deliveries was 2 (range 0-8). The overall prevalence of MRIPD was 75.8% (88 of 116); 83.3% (40 of 48) in symptomatic patients and 70.7% (48 of 68) in asymptomatic patients. Overall prevalence of defecatory symptoms was 41.4% (48 of 116). In MRIPD patients only, the prevalence of defecatory symptoms was 45.4% (40 of 88): 43.6% in grade 1; 45.2% in grade 2; 50% in grade 3. Defecatory symptoms were not strongly associated with either MRIPD (OR 1.86, CI 0.64-5.84, p 0.265) or PEXPD (OR 0.614, CI 0.26-1.40, p 0.248), while MRIPD and PEXPD were significantly associated with one another (OR 3.5, CI 1.25-11, p 0.021). CONCLUSION: In conclusion, the prevalence of defecatory symptoms in MRIPD patients was similar across grades. Symptoms were not predictive of MRIPD or PEXPD, with most patients being low grade (1 or 2). The detection of posterior defects on dMRI is high and is commonly associated with symptoms.

Mumford-Shah model has been used for image segmentation by considering both homogeneity and the shape of the segments jointly. It has been previously optimized by complex mathematical optimization methods like Douglas-Rachford, and a faster but sub-optimal k-means. However, they both suffer from fragmentation caused by non-convex segments. In this paper, we present hierarchical algorithm called Pairwise nearest neighbor (PNN) to optimize the Mumford-Shah model. The merge-based strategy utilizing the connectivity of the pixels prevents isolated fragments to be formed, and in this way, reaches better quality in case of images containing complex shapes.