Matching colonic polyps using correlation optimized warping, Proceedings of SPIE | 10.1117/12.844352 | DeepDyve
Computed tomographic colonography (CTC) combined with a computer aided detection system has the potential for improving colonic polyp detection and increasing the use of CTC for colon cancer screening. In the clinical use of CTC, a true colonic polyp will be confirmed with high confidence if a radiologist can find it on both the supine and prone scans. To assist radiologists in CTC reading, we propose a new method for matching polyp findings on the supine and prone scans. The method performs a colon registration using four automatically identified anatomical salient points and correlation optimized warping (COW) of colon centerline features. We first exclude false positive detections using prediction information from a support vector machine (SVM) classifier committee to reduce initial false positive pairs. Then each remaining CAD detection is mapped to the other scan using COW technique applied to the distance along the centerline in each colon. In the last step, a new SVM classifier is applied to the candidate pair dataset to find true polyp pairs between supine and prone scans. Experimental results show that our method can improve the sensitivity to 0.87 at 4 false positive pairs per patient compared with 0.72 for a competing method that uses the normalized distance along the colon centerline (p
MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging | Analytical Chemistry
Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m/z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample’s molecular composition. Our approach, MSIWarp (https://github.com/horvatovichlab/MSIWarp), finds an m/z recalibration function by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m/z alignment.
Two-Level Parallelism to Accelerate Multiple Genome Comparisons | SpringerLink
We present a two-level parallel strategy focused in the enhancement of GECKO software for multiple and pairwise genome comparisons. GECKO was developed to break the computational barriers on search...
Mining actionlet ensemble for action recognition with depth cameras | Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
New Experiments on ICP-Based 3D Face Recognition and Authentication | Proceedings of the 18th International Conference on Pattern Recognition - Volume 03