Hybrid Committee Classifier for a Computerized Colonic Polyp Detection System

We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features (numbers varied from 10-20) were selected for 11 NN classifiers which were again combined to form a NN committee classifier. Finally, a hybrid committee classifier was defined by combining the outputs of both the SVM and NN committees. The method was tested on CTC scans (supine and prone views) of 29 patients, in terms of the partial area under a free response receiving operation characteristic (FROC) curve (AUC). Our results showed that the hybrid committee classifier performed the best for the prone scans and was comparable to other classifiers for the supine scans

Characterization of phenylpropanoid pathway genes within European maize (Zea mays L.) inbreds

<p>Abstract</p> <p>Background</p> <p>Forage quality of maize is influenced by both the content and structure of lignins in the cell wall. Biosynthesis of monolignols, constituting the complex structure of lignins, is catalyzed by enzymes in the phenylpropanoid pathway.</p> <p>Results</p> <p>In the present study we have amplified partial genomic fragments of six putative phenylpropanoid pathway genes in a panel of elite European inbred lines of maize (<it>Zea mays </it>L.) contrasting in forage quality traits. Six loci, encoding C4H, 4CL1, 4CL2, C3H, F5H, and CAD, displayed different levels of nucleotide diversity and linkage disequilibrium (LD) possibly reflecting different levels of selection. Associations with forage quality traits were identified for several individual polymorphisms within the <it>4CL1</it>, <it>C3H</it>, and <it>F5H </it>genomic fragments when controlling for both overall population structure and relative kinship. A 1-bp indel in <it>4CL1 </it>was associated with <it>in vitro </it>digestibility of organic matter (IVDOM), a non-synonymous SNP in <it>C3H </it>was associated with IVDOM, and an intron SNP in <it>F5H </it>was associated with neutral detergent fiber. However, the <it>C3H </it>and <it>F5H </it>associations did not remain significant when controlling for multiple testing.</p> <p>Conclusion</p> <p>While the number of lines included in this study limit the power of the association analysis, our results imply that genetic variation for forage quality traits can be mined in phenylpropanoid pathway genes of elite breeding lines of maize.</p

Segmentation and polyp detection in virtual colonoscopy : a complete system for computer aided diagnosis

El cancer colorectal es una de las mayores causas de muerte por cancer en el mundo. La deteccion temprana de polipos es fundamental para su tratamiento, permitiendo alcanzar tasas del 90% de curabilidad. La tecnica habitual para la deteccion de polipos, debido a su elevada performance, es la colonoscopia optica (tecnica invasiva y extremadamente cara). A mediados de los '90 surge la tecnica denominada colonoscopia virtual. Esta tecnica consiste en la reconstruccion 3D del colon a partir de cortes de tomografia computada. Es por ende una tecnica no invasiva, y relativamente barata, pero la cantidad de falsos positivos y falsos negativos producida por estos metodos esta muy por encima de los maximos aceptados en la practica medica. Los avances recientes en las tecnicas de imagenologia parecerian hacer posible la reduccion de estas tasas. Como consecuencia de esto, estamos asistiendo a un nuevo interes por la colonoscopia virtual. En este trabajo se presenta un sistema completo de diagnostico asistido por computadora. La primera etapa del sistema es la segmentacion, que consiste en la reconstruccion 3D de la superficie del colon a partir del volumen tomografico. El aporte principal en este paso es el suavizado de la imagen. A partir de la superficie, se detectan aquellas zonas candidatas de ser polipos mediante una estrategia multi-escala que permite delinear con precision la zona. Luego para cada candidato se extraen caracteristicas geometricas y de textura, que son calculadas tambien en el tejido que rodea la zona a efectos de compararlas. Finalmente las zonas candidatas se clasifican utilizando SVM. Los resultados obtenidos son prometedores, permitiendo detectar un 100% de los polipos mayoresColorectal cancer is the second leading cause of cancer-related death in the United States, and the third cause worldwide. The early detection of polyps is fundamental, allowing to reduce mortality rates up to 90%. Nowadays, optical colonoscopy is the most used detection method due in part to its relative high performance. Virtual Colonoscopy is a promising alternative technique that emerged in the 90's. It uses volumetric Computed Tomographic data of the cleansed and air-distended colon, and the examination is made by a specialist from the images in a computer. Therefore, this technique is less invasive and less expensive than optical colonoscopy, but up to now the false positive and false negative rates are above the accepted medical limits. Recent advances in imaging techniques have the potential to reduce these rates; consequently, we are currently re-experiencing an increasing interest in Virtual Colonoscopy. In this work we propose a complete pipeline for a Computer-Aided Detection algorithm. The system starts with a novel and simple segmentation step. We then introduce geometrical and textural features that take into account not only the candidate polyp region, but the surrounding area at multiple scales as well. This way, our proposed CAD algorithm is able to accurately detect candidate polyps by measuring local variations of these features. Candidate patches are then classi ed using SVM. The whole algorithm is completely automatic and produces state-of-the-art results, achieving 100% sensitivity for polyps greater than 6mm in size with less than one false positive per case, and 100% sensitivity for polyps greater than 3mm in size with 2:2 false positives per case

Multi-scale and multi-spectral shape analysis: from 2d to 3d

Shape analysis is a fundamental aspect of many problems in computer graphics and computer vision, including shape matching, shape registration, object recognition and classification. Since the SIFT achieves excellent matching results in 2D image domain, it inspires us to convert the 3D shape analysis to 2D image analysis using geometric maps. However, the major disadvantage of geometric maps is that it introduces inevitable, large distortions when mapping large, complex and topologically complicated surfaces to a canonical domain. It is demanded for the researchers to construct the scale space directly on the 3D shape. To address these research issues, in this dissertation, in order to find the multiscale processing for the 3D shape, we start with shape vector image diffusion framework using the geometric mapping. Subsequently, we investigate the shape spectrum field by introducing the implementation and application of Laplacian shape spectrum. In order to construct the scale space on 3D shape directly, we present a novel idea to solve the diffusion equation using the manifold harmonics in the spectral point of view. Not only confined on the mesh, by using the point-based manifold harmonics, we rigorously derive our solution from the diffusion equation which is the essential of the scale space processing on the manifold. Built upon the point-based manifold harmonics transform, we generalize the diffusion function directly on the point clouds to create the scale space. In virtue of the multiscale structure from the scale space, we can detect the feature points and construct the descriptor based on the local neighborhood. As a result, multiscale shape analysis directly on the 3D shape can be achieved

Feature extraction to aid disease detection and assessment of disease progression in CT and MR colonography

Computed tomographic colonography (CTC) is a technique employed to examine the whole colon for cancers and premalignant adenomas (polyps). Oral preparation is taken to fully cleanse the colon, and gas insufflation maximises the attenuation contrast between the enoluminal colon surface and the lumen. The procedure is performed routinely with the patient both prone and supine to redistribute gas and residue. This helps to differentiate fixed colonic pathology from mobile faecal residue and also helps discover pathology occluded by retained fluid or luminal collapse. Matching corresponding endoluminal surface locations with the patient in the prone and supine positions is therefore an essential aspect of interpretation by radiologists; however, interpretation can be difficult and time consuming due to the considerable colonic deformations that occur during repositioning. Hence, a method for automated registration has the potential to improve efficiency and diagnostic accuracy. I propose a novel method to establish correspondence between prone and supine CT colonography acquisitions automatically. The problem is first simplified by detecting haustral folds which are elongated ridgelike endoluminal structures and can be identified by curvature based measurements. These are subsequently matched using appearance based features, and their relative geometric relationships. It is shown that these matches can be used to find correspondence along the full length of the colon, but may also be used in conjunction with other registration methods to achieve a more robust and accurate result, explicitly addressing the problem of colonic collapse. The potential clinical value of this method has been assessed in an external clinical validation, and the application to follow-up CTC surveillance has been investigated. MRI has recently been applied as a tool to quantitatively evaluate the therapeutic response to therapy in patients with Crohn's disease, and is the preferred choice for repeated imaging. A primary biomarker for this evaluation is the measurement of variations of bowel wall thickness on changing from the active phase of the disease to remission; however, a poor level of interobserver agreement of measured thickness is reported and therefore a system for accurate, robust and reproducible measurements is desirable. I propose a novel method which will automatically track sections of colon, by estimating the positions of elliptical cross sections. Subsequently, estimation of the positions of the inner and outer bowel walls are made based on image gradient information and therefore a thickness measurement value can be extracted

Phylogenetics And Molecular Evolution Of Highly Eusocial Wasps

Societies where workers sacrifice their own reproduction and cooperatively nurture the offspring of a reproductive queen caste have originated repeatedly across the Tree of Life. The attainment of such reproductive division of labor enabled the evolution of remarkable diversity in development, behavior, and social organization in the Hymenoptera (ants, bees, and wasps). Wasps of the family Vespidae exhibit a gamut of social levels, ranging from solitary to highly social behavior. The highly social yellowjackets and hornets (Vespinae) have well developed differences in form and function between queens and workers, large colony sizes, and intricate nest architecture. Moreover, certain socially parasitic species in the Vespinae have secondarily lost the worker caste and rely entirely on the workers of a host species to ensure the survival of parasitic offspring. Understanding the evolution of behavioral traits in the Vespinae over long periods of time would be greatly enhanced by a robust hypothesis of historical relationships. In this study, I analyze targeted genes and transcriptomes to address three goals. First, infer phylogenetic relationships within yellowjackets (Vespula and Dolichovespula) and hornets (Vespa and Provespa). Second, test the hypothesis that social parasites are more closely related to their hosts than to any other species (Emery\u27s rule). Third, test the protein evolution hypothesis, which states that accelerated evolution of protein coding genes and positive selection operated in the transition to highly eusocial behavior. The findings of this study challenge the predominant understanding of evolutionary relationships in the Vespinae. I show that yellowjacket genera are not sister lineages, instead recovering Dolichovespula as more closely related to the hornets, and placing Vespula as sister to all other vespine genera. This implies that traits such as large colony size and high paternity are mostly restricted to a particular evolutionary trajectory (Vespula) from an early split in the Vespinae. I demonstrate that obligate and facultative social parasites do not share immediate common ancestry with their hosts, indicating that socially parasitic behavior likely evolved independently of host species. Moreover, obligate social parasites share a unique evolutionary history, suggesting that their parasitic behavior might have a genetic component. Lastly, I analyze transcriptomic data to infer a phylogeny of vespid wasps and use this phylogeny to discover lineage-specific signatures of positive selection. I identify more than two hundred genes showing signatures of positive selection on the branch leading to the highly eusocial yellowjackets and hornets. These positively selected genes involve functions related mainly to carbohydrate metabolism and mitochondrial activity, in agreement with insights from studies of bees and ants. Parallels of functional categories for genes under positive selection suggests that at the molecular level the evolution of highly eusocial behavior across the Hymenoptera might have followed similar and narrow paths