Rodrigo A. Vivanco

EvIdent TM : a functional magnetic resonance image analysis system

EvIdent (EVent IDENTification) is a user-friendly, algorithm-rich, exploratory data analysis software for quickly detecting, investigating, and visualizing novel events in a set of images as they evolve in time and/or frequency. For instance, in a series of functional magnetic resonance neuroimages, novelty may manifest itself as neural activations in a time course. The core of the system is an enhanced variant of the fuzzy c-means clustering algorithm. Fuzzy clustering obviates the need for models of the underlying requisite biological function, models that are often statistically suspect.

Scopira: an open source C++ framework for biomedical data analysis applications -- a research project report

In MRI research labs, algorithms are typically implemented in MATLAB or IDL. If performance is an issue they are ported to C and integrated with interpreted systems, not fully utilizing object-oriented software development. This paper presents Scopira, an open source C++ framework suitable for MRI data analysis and visualization.

EvIdent: a Java-based fMRI data analysis application

EvIdentTM (EVent IDENTification) is a user-friendly, algorithm-rich, graphical environment for detecting, investigating, and visualizing novelty in a set of images. Novelty is identified for a region of interest and its associated characteristics. For functional magnetic resonance imaging, for instance, a characteristic of the region of interest is a time course, which represents the intensity value of voxels over several discrete instances in time. Originally developed for a platform-specific environment using proprietary technology, a new incarnation of EvIdent has been designed using an application programming interface called VIStATM (VISualization Through Analysis). VIStA is written in JavaTM and offers a sophisticated generalized data model, an extensible algorithm framework, and a suite of graphical user interface constructs. This paper describes EvIdent and some of its features, the rationale behind the design of VIStA, and the motivations and challenges of scientific programming using Java.

Discrimination of software quality in a biomedical data analysis system

Object-oriented visualization-based software systems for biomedical data analysis must deal with complex and voluminous datasets within a flexible yet intuitive graphical user interface. In a research environment, the development of such systems are difficult to manage due to rapidly changing requirements, incorporation of newly developed algorithms, and the needs imposed by a diverse user base. One issue that research supervisors must contend with is an assessment of the quality of the systems software objects with respect to their extensibility, reusability, clarity, and efficiency. Objects from a biomedical data analysis system were independently analyzed by two software architects and ranked according to their quality. Quantitative software features were also compiled at varying levels of granularity. The discriminatory power of these software metrics is discussed and their effectiveness in assessing and predicting software object quality is described.

Selecting object-oriented source code metrics to improve predictive models using a parallel genetic algorithm

Predictive models can be used to discover potentially problematic components. Source code metrics can be used as input features to predictive models, however, there are many structural and design measures that capture related metrics of coupling, cohesion, inheritance, complexity and size. Feature selection is the process of identifying a subset of attributes that improves the performance of a predictive model. This paper presents a prototype that implements a parallel genetic algorithm as a search-based feature selection method that enhances a predictive models ability to identify cognitively complex components in a Java application.

Improving predictive models of cognitive complexity using an evolutionary computational approach: a case study

The development of software is a human endeavor and program comprehension is an important factor in software maintenance. Predictive models can be used to identify software components as potentially problematic for the purpose of future maintenance. Such modules could lead to increased development effort, and as such, may be in need of mitigating actions such as refactoring or assigning more experienced developers. Source code metrics can be used as input features to classifiers, however, there exist a large number of structural measures that capture different aspects of coupling, cohesion, inheritance, complexity and size. In machine learning, feature selection is the process of identifying a subset of attributes that improves a classifiers performance. This paper presents initial results when using a genetic algorithm as a method of improving a classifiers ability to discover cognitively complex classes that degrade program understanding.

Fuzzy Region Growing of fMRI Activation Areas

Conventional analysis of fMRI responses in neuroimaging experiments is typically voxel-wise, i.e. independent of spatial neighbourhood information. However, valid responses are likely to be spatially clustered and connected in 3D space. Identifying spatial relations is commonly considered a pre-processing step, isotropic Gaussian filtering for noise reduction for example. Current post-processing methods consider spatial information but not temporal information; once an activation map is obtained, voxels that do not have a sufficient number of spatial neighbors are simply removed. This paper describes how we have successfully incorporated fuzzy region growing into EvIdent®, an fMRI data analysis application. The method uses spatial-temporal information to enhance spatially connected temporally related activation regions.

Fast noniterative registration of magnetic resonance images

EvIdent (EVent IDENTification) is an exploratory data analysis system for the detection and investigation of novelty, identified for a region of interest and its characteristics, within a set of images. For functional magnetic resonance imaging, for instance, a characteristic of the region of interest is a time course, which represents the intensity value of voxels over several discrete instances in time. An essential preprocessing step is the rapid registration of these images prior to analysis. Two dimensional image registration coefficients are obtained within EvIdent by solving a regression problem based on integration of a linearized matching equation over a set of patches in the image space. The registration method is robust to noise, offers a flexible hierarchical procedure, is easily generalizable to 3D registration, and is well suited to parallel processing. EvIdent, written in Java and C++, offers a sophisticated data model, an extensible algorithm framework, and a suite of graphical user interface constructs. We describe the registration algorithm and its implementation within the EvIdent software.

A Pattern Recognition Application Framework for Biomedical Datasets

: Scopira facilitates the development of high-performance applications by providing many useful subsystems, flexible and efficient data models, low-level tools such as memory management and serialization, GUI constructs, high-level visualization modules, and the ability to implement parallel algorithms with MPI. Scopira plug-in extensions have been developed to enable Matlab scripts to easily call any Scopira module, thus facilitating the migration of prototypes to highly efficient C++ applications. Scopira is continuously under development and future capabilities will include the ability to develop distributed programs using agents, applicable to grid-computing data mining applications. Scopira has proven to be a successful programming framework for implementing high-performance biomedical data analysis applications. It is based on C++, an efficient object-oriented language, and the source code is available as an open-source project for other researchers to use and adapt to their own research endeavours. Scopira has been compiled to work on Linux and Windows XP operating systems with a port to the Mac OS under development. Scopira, EvIdent and RDP are freely available for download from www.scopira.org.

Scopira: a pattern recognition application framework for biomedical datasets

Machine learning techniques are widely used in the analysis of biomedical datasets. Modern devices tend to produce voluminous, high-dimensional datasets for which medical practitioners require high-performance, user-friendly programs and researchers need effective algorithm development and testing platforms. Interactive development systems, such as MATIAB, provide for rapid prototyping of algorithms and visualization but at the cost of computational efficiency. We present Scopira, a C++, open source programming framework for the development of biomedical data analysis applications.