Joel A. Rosiene

Experiences with a Real Software Engineering Client

Encounters with a real software engineering client are described, along with the course format and content, project deliverables, and modus operandi. The project is a customary semester-long, team-oriented software development assignment involving the usual stages of the software lifecycle. The services of a real client, in need of software to help her with her day-to-day tracking of business transactions, subsidize our need for an end-user. Her involvement and input gives the class a real-life scenario and also increases the complexity of a medium-scale project by providing details of her software needs. This paper focuses on the lessons learned from using a real-life client. There is no doubt that using a real-life client is not an innovative idea. However, we offer novel ways of transacting and realizing a real-life clients specifications. We also offer some pedagogical techniques and summarize our experiences, with the intent of helping others who are implementing a similar scheme in their software engineering courses

Quantification of Diffusion-weighted Images (DWI) and Apparent Diffusion Coefficient Maps (ADC) in the Detection of Acute Stroke

Magnetic resonance (MR) imaging is an imaging modality that is used in the management and diagnosis of acute stroke. Common MR imaging techniques such as diffusion weighted imaging (DWI) and apparent diffusion coefficient maps (ADC) are used routinely in the diagnosis of acute infarcts. However, advances in radiology information systems and imaging protocols have led to an overload of image information that can be difficult to manage and time consuming. Automated techniques to assist in the identification of acute ischemic stroke can prove beneficial to 1) the physician by providing a mechanism for early detection and 2) the patient by providing effective stroke therapy at an early stage. We have processed DW images and ADC maps using a novel automated Relative Difference Map (RDM) method that was tailored to the identification and delineation of the stroke region. Results indicate that the technique can delineate regions of acute infarctions on DW images and ADC maps. A formal evaluation of the RDM algorithm was performed by comparing accuracy measurements between 1) expert generated ground truths with the RDM delineated DWI infarcts and 2) RDM delineated DWI infarcts with RDM delineated ADC infarcts. The accuracy measurements indicate that the RDM delineated DWI infarcts are comparable to the expert generated ground truths. The true positive volume fraction value (TPVF), between RDM delineated DWI and ADC infarcts, is nonzero for all cases with an acute infarct while the value for non-acute cases remains zero.

A novel quantification method for determining previously undetected silent infarcts on MR-perfusion in patients following carotid endarterectomy

The purpose of this paper is to evaluate the post-operative Magnetic Resonance Perfusion (MRP) scans of patients undergoing carotid endarterectomy (CEA), using a novel image-analysis algorithm, to determine if post-operative neurocognitive decline is associated with cerebral blood flow changes. CEA procedure reduces the risk of stroke in appropriately selected patients with significant carotid artery stenosis. However, 25% of patients experience subtle cognitive deficits after CEA compared to a control group. It was hypothesized that abnormalities in cerebral blood flow (CBF) are responsible for these cognitive deficits. A novel algorithm for analyzing MR-perfusion (MRP) scans to identify and quantify the amount of CBF asymmetry in each hemisphere was developed and to quantify the degree of relative difference between three corresponding vascular regions in the ipsilateral and contralateral hemispheres, the Relative Difference Map (RDM). Patients undergoing CEA and spine surgery (controls) were examined preoperatively, and one day postoperatively with a battery of neuropsychometric (NPM) tests, and labeled “injured” patients with significant cognitive deficits, and “normal” if they demonstrated no decline in neurocognitive function. There are apparently significant RDM differences with MRP scans between the two hemispheres in patients with cognitive deficits which can be used to guide expert reviews of the imagery. The proposed methodology aids in the analysis of MRP parameters in patients with cognitive impairment.

Ray casting approach for boundary extraction and Fourier shape descriptor characterization

There are many significant applications of Fourier Shape Descriptor characterization of boundaries of regions in images. Whenever it is desirable to compare two shapes, independent of rotation, starting point, or compensate for magnification, Fourier Shape Descriptors (FSDs) have merits. FSDs have been proposed for the automatic assessment of packaging; to check alignment of objects for automation; and characterize visual objects in video coding, and compare biomedical regions in medical images. This paper presents a technique to parameterize the boundary of the region of interest (ROI) that utilizes the casting of rays from the center of mass of the region of interest outward to points in the image that lie on the edge of the ROI. This is essentially another technique to obtain the R-S parametrization. At each step the process utilizes the sections of the boundary have radii that are a simple function of theta. The procedure then merges these simple boundary sections to create a periodic complex valued function of the boundary parameterized by a parameter s that is not required to be a function of theta. Once the complex periodic sequence is obtained, the Fourier Transform is taken resulting in the corresponding Fourier Shape Descriptors. Since the technique seeks the intersection of a known ray with the boundary (it is not boundary following), the worst-case behavior of the technique is easily calculated making it suitable for real-time applications. The technique is robust to incomplete boundaries of objects, and can be readily extended to three-dimensional datasets (spherical harmonics). The a simpler version of the technique is currently being used in the automatic selection of the axis of symmetry in Magnetic Resonance Images of the brain, and we will demonstrate the application of the technique on these types of datasets, although the technique has general application.

Enhanced Techniques for Asymmetry Quantification in Brain Imagery

We present an automated generic methodology for symmetry identification and asymmetry quantification, novel method of identifying and delineation of brain pathology by analyzing the opposing sides of the brain utilizing of inherent left-right symmetry in the brain. After symmetry axis has been detected, we apply non-parametric statistical tests operating on the pairs of samples to identify initial seeds points which is defined defined as the pixels where the most statistically significant difference appears. Local region growing is performed on the difference map, from where the seeds are aggregating until it captures all 8-way connected high signals from the difference map. We illustrate the capability of our method with examples ranging from tumors in patient MR data to animal stroke data. The validation results on Rat stroke data have shown that this approach has promise to achieve high precision and full automation in segmenting lesions in reflectional symmetrical objects.

Evaluation of ischemic stroke hybrid segmentation in a rat model of temporary middle cerebral artery occlusion using ground truth from histologic and MR data

A segmentation method that quantifies cerebral infarct using rat data with ischemic stroke is evaluated using ground truth from histologic and MR data. To demonstrate alternative approach to rapid quantification of cerebral infarct volumes using histologic stained slices that requires scarifying animal life, a study with MR acquire volumetric rat data is proposed where ground truth is obtained by manual delineations by experts and automated segmentation is assessed for accuracy. A framework for evaluation of segmentation is used that provides more detailed accuracy measurements than mere cerebral infarct volume. Our preliminary experiment shows that ground truth derived from MRI data is at least as good as the one obtained from the histologic slices for evaluating segmentation algorithms for accuracy. Therefore we can develop and evaluate automated segmentation methods for rapid quantification of stroke without the necessitating animal sacrifice.

Dilation Based Modeling of Perfusion Datasets

A new approach to the modeling of the marker in Perfusion CT and Perfusion MR datasets is outlined and initial results given. The technique is based on estimation of the dilation and delay of an estimated bolus shape and a template fit to an new solution of the heat equation. Initial results are provided.