A Joint Transformation and Residual Image Descriptor for Morphometric Image Analysis using an Equivalence Class Formulation

Existing computational anatomy methodologies for morphometric analysis of medical images are often based solely on the shape transformation, typically being a diffeomorphism, that warps these images to a common template or vice versa. However, anatomical differences as well as changes induced by pathology, prevent the warping transformation from producing an exact correspondence. The residual image captures information that is not reflected by the diffeomorphism, and therefore allows us to maintain the entire morphological profile for analysis. In this paper we present a morphological descriptor which combines the warping transformation with the residual image in an equivalence class formulation, to characterize morphology of anatomical structures. Equivalence classes are formed by pairs of transformation and residual, for different levels of smoothness of the warping transformation. These pairs belong to the same equivalence class, since they jointly reconstruct the exact same morphology. Moreover, pattern classification methods are trained on the entire equivalence class, instead of a single pair, in order to become more robust to a variety of factors that affect the warping transformation, including the anatomy being measured. This joint descriptor is evaluated by statistical testing and estimation of class separation by classification, initially for 2-D synthetic images with simulated atrophy and subsequently for a volumetric dataset consisting of schizophrenia patients and healthy controls. Results of class separation indicate that this joint descriptor produces generally better and more robust class separation than using each of the components separately

Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets

<p>Abstract</p> <p>Background</p> <p>Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal) samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a) all genes on the microarray platform and b) a list of known disease-related genes (a priori selection). We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms.</p> <p>Results</p> <p>Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform.</p> <p>Conclusion</p> <p>Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine learning approaches. These findings are relevant to the use of molecular profiling for the identification of candidate biomarker panels.</p

Anomalous absorption, plasmonic resonances, and invisibility of radially anisotropic spheres

This article analyzes the response of a sphere with radially anisotropic permittivity dyadic (RA sphere), in both the electrostatic and full electrodynamic settings. Depending on the values and signs of the permittivity components, the quasistatic polarizability of the RA sphere exhibits several very different interesting properties, including invisibility, field concentration, resonant singularities, and emergent losses. Special attention is given to the anomalous losses that appear in the case of certain hyperbolic anisotropy values. We show that their validity can be justified in a limiting sense by puncturing the sphere at the origin and adding a small imaginary part into the permittivity components. A hyperbolic RA sphere with very small intrinsic losses can thus have significant effective losses making it an effective absorber. With different choices of permittivities, the RA sphere could also perform as a cloak or a sensor. The Mie scattering results by an RA sphere are used to justify the quasistatic calculations. It is shown that in the small parameter limit the absorption efficiency of an RA sphere is nonzero for certain lossless hyperbolic anisotropies. The absorption and scattering efficiencies agree with the quasistatic calculations fairly well for spheres with size parameters up to 1/3.Peer reviewe

Nonrigid Registration of Brain Tumor Resection MR Images Based on Joint Saliency Map and Keypoint Clustering

This paper proposes a novel global-to-local nonrigid brain MR image registration to compensate for the brain shift and the unmatchable outliers caused by the tumor resection. The mutual information between the corresponding salient structures, which are enhanced by the joint saliency map (JSM), is maximized to achieve a global rigid registration of the two images. Being detected and clustered at the paired contiguous matching areas in the globally registered images, the paired pools of DoG keypoints in combination with the JSM provide a useful cluster-to-cluster correspondence to guide the local control-point correspondence detection and the outlier keypoint rejection. Lastly, a quasi-inverse consistent deformation is smoothly approximated to locally register brain images through the mapping the clustered control points by compact support radial basis functions. The 2D implementation of the method can model the brain shift in brain tumor resection MR images, though the theory holds for the 3D case

Robust Automated Tumour Segmentation on Histological and Immunohistochemical Tissue Images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification

Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers

The upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry

Investigation of Titanium Material Deformation for Positioning Stereotactic Navigation System by von Mises Stress Criteria and Finite Element in Engineering

Positioning stereotactic navigation system (PSNS) is a mechanical system to make a 3D coordinate metallic frame for guiding minor targets in the brain to do biopsy, injection where the treatment is needed. A PSNS head frame is fixed to skull by specially designed pins to keep the head from touching within the frame until the surgery completed. There are although complications in establishing a dependable frame of position, such as bone area that bear a fixed spatial liaison to soft tissues. The fixation or positioning techniques of head frame have been explained in various publications, but related research of mechanical stress/strain analysis about PSNS and pins has not been reported in literature. As a result, the study was undertaken to explore the PSNS head frame and pins by commercial CAD/CAM and ANSYS software packages. The research aim was to develop a three dimensional finite element model and von-Mises criteria model to calculate the mechanical behavior response for the stress distribution around PSNS and related material involving physical surface contact flexural and compressive loading mechanisms. Static analyses were carried out to find the generated stresses and deformation on each part of the studied model. The outcomes reveal that the finite element engineering applied to PSNS model may be employed to calculate the regional distribution of stress-strain-deformation growth in the skull. The knowledge gained from the study points toward some of the engineering advances that are beneficial to stereotactic frame design and mechanical engineers

Finite Element Analysis of the Model of Mechanical Behavior of SUS420 Steel Scraping Blade in Printing Engineering

The scraping blade is a critical device for maintaining the uniform thickness of an anilox-developing roll and for removing the excess of ink, water and contamination from the smooth non-engraved portions of the image carrier by controlling the pressure on the developing roller in the printing machine. Not much research can be seen in the literature, related to the material and the geometrical shape of the scraping blade. Due to this fact, a novel simulation method was implemented in ANSYS to control the mechanical behavior of scraping blade and of the anilox rolls. Numerical simulation was carried out using the method of finite element analysis for analysis of the system of SUS420-chrome-containing martensitic stainless steel blade. The purpose of this study was to develop a model of structural behavior to minimize excessive stresses and wears and to achieve an optimal design of the scraping SUS420 stainless steel blade. As a design optimization tool, the finite element analysis was engaged to perform static analysis of scraping structures, scraping blade holders and blades. Flexural deflection analysis and structure optimal design methodology were developed to improve the blade life span. According to the outcomes of this paper, the scraping blade was improved after optimization of the wall thickness and of the tip angle. Thus the smooth scraping quality was improved. These results suggest that a new design with the new blade tip could be beneficial for designers and manufacturers