The extraction of objects from advanced geospatial intelligence (AGI) products based on synthetic aperture radar (SAR) imagery is complicated by a number of factors. For example, accurate detection of temporal changes represented in two-color multiview (2CMV) AGI products can be challenging because of speckle noise susceptibility and false positives that result from small orientation differences between objects imaged at different times. These cases of apparent motion can result in 2CMV detection, but they obviously differ greatly in terms of significance. In investigating the state-of-the-art in SAR image processing, we have found that differentiating between these two general cases is a problem that has not been well addressed. We propose a framework of methods to address these problems. For the detection of the temporal changes while reducing the number of false positives, we propose using adaptive object intensity and area thresholding in conjunction with relaxed brightness optical flow algorithms that track the motion of objects across time in small regions of interest. The proposed framework for distinguishing between actual motion and misregistration can lead to more accurate and meaningful change detection and improve object extraction from a SAR AGI product. Results demonstrate the ability of our techniques to reduce false positives up to 60%.
Measuring the glomerular number in the entire, intact kidney using non-destructive techniques is of immense importance in studying several renal and systemic diseases. Commonly used approaches either require destruction of the entire kidney or perform extrapolation from measurements obtained from a few isolated sections. A recent magnetic resonance imaging (MRI) method, based on the injection of a contrast agent (cationic ferritin), has been used to effectively identify glomerular regions in the kidney. In this work, we propose a robust, accurate, and low-complexity method for estimating the number of glomeruli from such kidney MRI images. The proposed technique has a training phase and a low-complexity testing phase. In the training phase, organ segmentation is performed on a few expert-marked training images, and glomerular and non-glomerular image patches are extracted. Using non-local sparse coding to compute similarity and dissimilarity graphs between the patches, the subspace in which the glomerular regions can be discriminated from the rest are estimated. For novel test images, the image patches extracted after pre-processing are embedded using the discriminative subspace projections. The testing phase is of low computational complexity since it involves only matrix multiplications, clustering, and simple morphological operations. Preliminary results with MRI data obtained from five kidneys of rats show that the proposed non-invasive, low-complexity approach performs comparably to conventional approaches such as acid maceration and stereology.
Many important applications in clinical medicine can benefit from the fusion of spectroscopy data with anatomical
images. For example, the correlation of metabolite profiles with specific regions of interest in anatomical tumor images
can be useful in characterizing and treating heterogeneous tumors that appear structurally homogeneous. Such
applications can build on the correlation of data from <i>in-vivo</i> Proton Magnetic Resonance Spectroscopy Imaging (<sup>1</sup>HMRSI) with data from genetic and <i>ex-vivo</i> Nuclear Magnetic Resonance spectroscopy. To establish that correlation, tissue samples must be neurosurgically extracted from specifically identified locations with high accuracy. Toward that end, this paper presents new neuronavigation technology that enhances current clinical capabilities in the context of neurosurgical planning and execution. The proposed methods improve upon the current state-of-the-art in neuronavigation through the use of detailed three dimensional (3D) <sup>1</sup>H-MRSI data. MRSI spectra are processed and analyzed, and specific voxels are selected based on their chemical contents. 3D neuronavigation overlays are then generated and applied to anatomical image data in the operating room. Without such technology, neurosurgeons must rely on memory and other qualitative resources alone for guidance in accessing specific MRSI-identified voxels. In contrast, MRSI-based overlays provide quantitative visual cues and location information during neurosurgery. The proposed methods enable a progressive new form of online MRSI-guided neuronavigation that we demonstrate in this study through phantom validation and clinical application.