DNA Copy Number Selection Using Robust Structured Sparsity-Inducing Norms.

Array comparative genomic hybridization (aCGH) is a newly introduced method for the detection of copy number abnormalities associated with human diseases with special focus on cancer. Specific patterns in DNA copy number variations (CNVs) can be associated with certain disease types and can facilitate prognosis and progress monitoring of the disease. Machine learning techniques have been used to model the problem of tissue typing as a classification problem.

A web ontology for brain trauma patient computer-assisted rehabilitation.

In this paper we describe CABROnto, which is a web ontology for the semantic representation of the computer assisted brain trauma rehabilitation. This is a novel and emerging domain, since it employs the use of robotic devices, adaptation software and machine learning to facilitate interactive and adaptive rehabilitation care. We used Protégé 4.

Heterogeneous data fusion for brain tumor classification.

Current research in biomedical informatics involves analysis of multiple heterogeneous data sets. This includes patient demographics, clinical and pathology data, treatment history, patient outcomes as well as gene expression, DNA sequences and other information sources such as gene ontology. Analysis of these data sets could lead to better disease diagnosis, prognosis, treatment and drug discovery.

A novel surface registration algorithm with biomedical modeling applications.

In this paper, we propose a novel surface matching algorithm for arbitrarily shaped but simply connected 3-D objects. The spherical harmonic (SPHARM) method is used to describe these 3-D objects, and a novel surface registration approach is presented. The proposed technique is applied to various applications of medical image analysis.

Modeling Time-Intensity Profiles for Pulmonary Nodules in MR Images.

Perfusion magnetic resonance imaging (pMRI) is an important tool to assess tumor angiogenesis for the early detection of lung cancer. This paper presents a novel integrated framework for spatio-temporal modeling of pulmonary nodules in pMRI image sequences. After localizing a nodule region in each image, we perform segmentation in the region to extract nodule boundary, and then use thin-plate spline interpolation for nodule registration along the temporal dimension.

An adaptive approach for image subtraction.

Image subtraction is widely used in angiography as a means of highlighting differences induced by contrast agents. New knowledge of previously unsuspected causes of disease, in particular, secondhand smoke exposure, spurs interest in pushing the limits of early accurate diagnosis. Simple image subtraction induces artifacts causing problems for ensuing measurements and 3D reconstruction.

BP-neural network based-characterization of electrographic magnetohydrodynamic signals in MR.

Electrocardiographs (ECG) signal collected during magnetic resonance (MR) imaging is affected by signal artifact because magnetic fields produce competing signals, from moving conductors in the large vessels. That is called the magnetohydrodynamic effect, which makes it difficult to recognize ST-T changes from ECG signal collected in a magnetic field (MRI). Resolving that problem is important both for accurate triggering (elimination of false triggers from tall peaked T waves) and for monitoring (identifying if or when patient develops ischemia or myocardial injury).

Efficient similarity retrieval for temporal shape sequences: a case study using cardiac MR images.

The spherical harmonics (SPHARM) approach has been used for the representation of shapes in many types of biomedical image data. We propose a SPHARM-based similarity comparison for shape sequences that allows fast similarity searches for dynamic objects and demonstrate it using 3D images of a beating heart. By using spherical harmonics to extract a small number of features that represent cardiac shape in each sequential state, we enable indexing and pruning of database entries with a multidimensional index tree (e.

An extensible framework for sharing clinical guidelines and services.

Accurate and descriptive information from clinical studies guides improvements in health care. Clinical guidelines established by authoritative medical organizations provide such information in a standard form for medical professionals' reference. Previous work on electronically sharing clinical guidelines focuses on the idea of building unified clinical terminologies and sharing resources through centralized data repositories.

A bipartite graph matching framework for finding correspondences between structural elements in two proteins.

A protein molecule consists one or more chains of amino acid sequences that fold into a complex three-dimensional structure. A protein's functions are often determined by its 3D structure, and so comparing the similarity of 3D structures between proteins is an important problem. To accomplish such comparison, one must align two proteins properly with rotation and translation in 3D space.

Cardiac motion analysis to improve pacing site selection in CRT.

Rationale And Objectives: The aim of the study is to build cardiac wall motion models to characterize mechanical dyssynchrony and predict pacing sites for the left ventricle of the heart in cardiac resynchronization therapy (CRT).

Materials And Methods: Cardiac magnetic resonance imaging data from 20 patients are used, in which half have heart failure problems. We propose two spatio-temporal ventricular motion models to analyze the mechanical dyssynchrony of heart: radial motion series and wall motion series (a time series of radial length or wall thickness change).

A prediction framework for cardiac resynchronization therapy via 4D cardiac motion analysis.

We propose a novel framework to predict pacing sites in the left ventricle (LV) of a heart and its result can be used to assist pacemaker implantation and programming in cardiac resynchronization therapy (CRT), a widely adopted therapy for heart failure patients. In a traditional CRT device deployment, pacing sites are selected without quantitative prediction. That runs the risk of suboptimal benefits.

Surface alignment of 3D spherical harmonic models: application to cardiac MRI analysis.

The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected 3D objects and has been used in many applications in medical imaging. Previous SPHARM techniques use the first order ellipsoid for establishing surface correspondence and aligning objects. However, this first order information may not be sufficient in many cases; a more general method for establishing surface correspondence would be to minimize the mean squared distance between two corresponding surfaces.

Spatio-temporal modeling of lung images for cancer detection.

Perfusion magnetic resonance imaging (pMRI) is an important tool in assessing tumor angiogenesis for the early detection of lung cancer. This study presents a novel integrated framework for spatio-temporal modeling of pulmonary nodules in pMRI image sequences. After localizing a nodule region in each image, we perform segmentation in the region to extract the nodule boundary, then use thin-plate spline interpolation for nodule registration along the temporal dimension.

Tumor classification based on DNA copy number aberrations determined using SNP arrays.

High-density single nucleotide polymorphism (SNP) array is a recently introduced technology that genotypes more than 10,000 human SNPs on a single array. It has been shown that SNP arrays can be used to determine not only SNP genotype calls, but also DNA copy number (DCN) aberrations, which are common in solid tumors. In the past, effective cancer classification has been demonstrated using microarray gene expression data, or DCN data derived from comparative genomic hybridization (CGH) arrays.

Overview: Computational analysis and decision support systems in oncology.

Computational analysis tools and decision support systems have increased their penetration in the support of clinical processes and management of medical data and knowledge. Applications range from adjunct tools for diagnosis and disease investigation to the treatment and monitoring of therapeutic procedures. As all medical fields, the field of oncology is affected.

HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data.

Motivation: Recent studies have shown that microarray gene expression data are useful for phenotype classification of many diseases. A major problem in this classification is that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy.