Perturbation-Based Eigenvector Updates for On-Line Principal Components Analysis and Canonical Correlation Analysis

Principal components analysis is an important and well-studied subject in statistics and signal processing. Several algorithms for solving this problem exist, and could be mostly grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second order statistical criterion (like reconstruction error or output variance), and fixed point update rules with deflation. In this study, we propose an alternate approach that avoids deflation and gradient-search techniques. The proposed method is an on-line procedure based on recursively updating the eigenvector and eigenvalue matrices with every new sample such that the estimates approximately track their true values as would be calculated analytically from the current sample estimate of the data covariance matrix. The perturbation technique is theoretically shown to be applicable for recursive canonical correlation analysis, as well. The performance of this algorithm is compared with that of a structurally similar matrix perturbation-based method and also with a few other traditional methods like Sanger’s rule and APEX.

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Multifeature landmark-free active appearance models: application to prostate MRI segmentation

Active shape models (ASMs) and active appearance models (AAMs) are popular approaches for medical image segmentation that use shape information to drive the segmentation process. Both approaches rely on image derived landmarks (specified either manually or automatically) to define the object's shape, which require accurate triangulation and alignment. An alternative approach to modeling shape is the levelset representation, defined as a set of signed distances to the object's surface. In addition, using multiple image derived attributes (IDAs) such as gradient information has previously shown to offer improved segmentation results when applied to ASMs, yet little work has been done exploring IDAs in the context of AAMs. In this work, we present a novel AAM methodology that utilizes the levelset implementation to overcome the issues relating to specifying landmarks, and locates the object of interest in a new image using a registration based scheme. Additionally, the framework allows for incorporation of multiple IDAs. Our multifeature landmark-free AAM (MFLAAM) utilizes an efficient, intuitive, and accurate algorithm for identifying those IDAs that will offer the most accurate segmentations. In this paper, we evaluate our MFLAAM scheme for the problem of prostate segmentation from T2-w MRI volumes. On a cohort of 108 studies, the levelset MFLAAM yielded a mean Dice accuracy of 88% ± 5%, and a mean surface error of 1.5 mm ±.8 mm with a segmentation time of 150/s per volume. In comparison, a state of the art AAM yielded mean Dice and surface error values of 86% ± 9% and 1.6 mm ± 1.0 mm, respectively. The differences with respect to our levelset-based MFLAAM model are statistically significant . In addition, our results were in most cases superior to several recent state of the art prostate MRI segmentation methods.     

An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery

Active contours and active shape models (ASM) have been widely employed in image segmentation. A major limitation of active contours, however, is in their 1) inability to resolve boundaries of intersecting objects and to 2) handle occlusion. Multiple overlapping objects are typically segmented out as a single object. On the other hand, ASMs are limited by point correspondence issues since object landmarks need to be identified across multiple objects for initial object alignment. ASMs are also are constrained in that they can usually only segment a single object in an image. In this paper, we present a novel synergistic boundary and region-based active contour model that incorporates shape priors in a level set formulation with automated initialization based on watershed. We demonstrate an application of these synergistic active contour models using multiple level sets to segment nuclear and glandular structures on digitized histopathology images of breast and prostate biopsy specimens. Unlike previous related approaches, our model is able to resolve object overlap and separate occluded boundaries of multiple objects simultaneously. The energy functional of the active contour is comprised of three terms. The first term is the prior shape term, modeled on the object of interest, thereby constraining the deformation achievable by the active contour. The second term, a boundary-based term detects object boundaries from image gradients. The third term drives the shape prior and the contour towards the object boundary based on region statistics. The results of qualitative and quantitative evaluation on 100 prostate and 14 breast cancer histology images for the task of detecting and segmenting nuclei and lymphocytes reveals that the model easily outperforms two state of the art segmentation schemes (geodesic active contour and Rousson shape-based model) and on average is able to resolve up to 91% of overlapping/occluded structures in the images.     

Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women. In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. Automatically detecting tumors and extracting lesion boundaries in ultrasound images is difficult due to their specular nature and the variance in shape and appearance of sonographic lesions. Past work on automated ultrasonic breast lesion segmentation has not addressed important issues such as shadowing artifacts or dealing with similar tumor like structures in the sonogram. Algorithms that claim to automatically classify ultrasonic breast lesions, rely on manual delineation of the tumor boundaries. In this paper, we present a novel technique to automatically find lesion margins in ultrasound images, by combining intensity and texture with empirical domain specific knowledge along with directional gradient and a deformable shape-based model. The images are first filtered to remove speckle noise and then contrast enhanced to emphasize the tumor regions. For the first time, a mathematical formulation of the empirical rules used by radiologists in detecting ultrasonic breast lesions, popularly known as the "Stavros Criteria" is presented in this paper. We have applied this formulation to automatically determine a seed point within the image. Probabilistic classification of image pixels based on intensity and texture is followed by region growing using the automatically determined seed point to obtain an initial segmentation of the lesion. Boundary points are found on the directional gradient of the image. Outliers are removed by a process of recursive refinement. These boundary points are then supplied as an initial estimate to a deformable model. Incorporating empirical domain specific knowledge along with low and high-level knowledge makes it possible to avoid shadowing artifacts and lowers the chance of confusing similar tumor like structures for the lesion. The system was validated on a database of breast sonograms for 42 patients. The average mean boundary error between manual and automated segmentation was 6.6 pixels and the normalized true positive area overlap was 75.1%. The algorithm was found to be robust to 1) variations in system parameters, 2) number of training samples used, and 3) the position of the seed point within the tumor. Running time for segmenting a single sonogram was 18 s on a 1.8-GHz Pentium machine.     

Mechanism of Rab geranylgeranylation: formation of the catalytic ternary complex

Rab proteins are geranylgeranylated on one or two C-terminal cysteines by Rab geranylgeranyl transferase (RabGGTase). The reaction is dependent on a Rab-binding protein, termed Rab escort protein (REP). Here, we studied the role of REP in the geranylgeranylation reaction. We first characterized the interaction between REP and ungeranylgeranylated Rab using analytical ultracentrifugation and a fluorescence-based assay. We measured an equilibrium dissociation constant of 0.2 microM for the formation of a 1:1 REP-Rab complex and showed that this interaction relies mostly on ionic bonds and does not involve the two C-terminal cysteine residues. Second, we show that REP is required for recognition of Rab by RabGGTase and therefore that the REP-Rab complex is the true substrate for RabGGTase. Third, we show that free REP inhibits the geranylgeranylation reaction, suggesting that the complex is recognized by RabGGTase primarily via a REP-binding site. Our data suggest a model whereby REP behaves kinetically as an essential activator of the reaction.     

Quantitative measurement of voids formed during liquid impregnation of nonwoven multifilament glass networks using an optical visualization technique

A technique based on matching the refractive index of an invading liquid to that of a fiber mat has been used to study entrapment of air (“voids”) that occurs during forced in-plane radial flow into nonwoven multifilament glass networks. The usefulness of this technique is demonstrated in quantifying and mapping the air pockets. Experiments with a series of fluids, with surface tensions varying from 28 × 10^?3 to 36 × 10^?3 N/m, viscosities from 45 × 10^?3 to 80 × 10^?3 Pa · s, and inlet flow rates from 0.15 × 10^?6 to 0.75 × 10^?6 m³/s, have shown that void content is a function of the capillary number characterizing the flow process. A critical value of capillary number Ca = 2.5 × 10^?3 identifies a zone below which void content increases exponentially with decreasing capillary number. Above this critical value, negligible entrapment of voids is observed.     

Large area CIGS2 thin film solar cells on foils: nucleus of a pilot plant

In earlier research, conversion efficiency of 10.4% (AM1.5) and 9.9% (AM0) has been achieved on small area CuIn_xGa_1−xS₂ (CIGS2) solar cell on 127 μm thick stainless steel substrate. The area of research is mainly focused on studying CIGS2 thin films as solar cell absorber material and growing high efficiency cells on ultralightweight and flexible metallic foils such as 127 μm thick stainless steel and SiO₂ coated 25 μm thick Ti foils. This paper presents the scaling up process of CIGS2 thin film substrate from 2.5 × 2.5 cm² to 10 × 10 cm². Initial scaling up efforts focused on achieving uniform thickness and stress-free films. Process of scaling up consisted of refurbishment of selenization/sulfurization furnace, design and fabrication of scrubber and enlargement of new CdS deposition setup. The scaling up from 2.5 × 2.5 cm² to 10 × 10 cm² substrate size has laid the foundation for PV Materials Lab of Florida Solar Energy Center becoming the nucleus of a pilot plant.     

Photoelectrochemical water splitting using CuIn1−xGaxS2/CdS thin-film solar cells for hydrogen generation

Photoelectrochemical (PEC) efficiency of a PEC cell constructed by series connecting two 0.43 cm² size, 5.95% (AM1.5) efficient CuIn_1−xGa_xS₂ (CIGS2) thin-film photovoltaic (PV) cells having transparent and conducting back contacts, outside the electrolyte, to RuS₂ photoanode and platinum cathode, in the electrolyte, for oxygen and hydrogen generation by water splitting was 2.99%. PV electrolysis efficiency of a similar setup prepared using two CIGS2 PV cells having opaque Mo back contacts and highest achieved efficiency of 11.99% (AM1.5) connected to RuS₂ and Pt electrodes was 8.78%. This significant result points a way toward attaining higher PEC efficiencies.     

Simultaneous segmentation of prostatic zones using Active Appearance Models with multiple coupled levelsets

In this work we present an improvement to the popular Active Appearance Model (AAM) algorithm, that we call the Multiple-Levelset AAM (MLA). The MLA can simultaneously segment multiple objects, and makes use of multiple levelsets, rather than anatomical landmarks, to define the shapes. AAMs traditionally define the shape of each object using a set of anatomical landmarks. However, landmarks can be difficult to identify, and AAMs traditionally only allow for segmentation of a single object of interest. The MLA, which is a landmark independent AAM, allows for levelsets of multiple objects to be determined and allows for them to be coupled with image intensities. This gives the MLA the flexibility to simulataneously segmentation multiple objects of interest in a new image.In this work we apply the MLA to segment the prostate capsule, the prostate peripheral zone (PZ), and the prostate central gland (CG), from a set of 40 endorectal, T2-weighted MRI images. The MLA system we employ in this work leverages a hierarchical segmentation framework, so constructed as to exploit domain specific attributes, by utilizing a given prostate segmentation to help drive the segmentations of the CG and PZ, which are embedded within the prostate. Our coupled MLA scheme yielded mean Dice accuracy values of .81, .79 and .68 for the prostate, CG, and PZ, respectively using a leave-one-out cross validation scheme over 40 patient studies. When only considering the midgland of the prostate, the mean DSC values were .89, .84, and .76 for the prostate, CG, and PZ respectively.