Biomedical subjects

Kayvan Najarian

Publications and source records attributed to Kayvan Najarian.

3 recordsLinked to original sources

PubMed2004

A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data.

BACKGROUND: The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. Mixture model method (MMM hereafter) is a nonparametric statistical method often used for microarray processing applications, but is known to over-fit the data if the number of replicates is small. In addition, the results of the MMM may not be repeatable when dealing with a small number of replicates. In this paper, we propose a new version of MMM to ensure the repeatability of the results in different runs, and reduce the sensitivity of the results on the parameters. RESULTS: The proposed technique is applied to the two different data sets: Leukaemia data set and a data set that examines the effects of low phosphate diet on regular and Hyp mice. In each study, the proposed algorithm successfully selects genes closely related to the disease state that are verified by biological information. CONCLUSION: The results indicate 100% repeatability in all runs, and exhibit very little sensitivity on the choice of parameters. In addition, the evaluation of the applied method on the Leukaemia data set shows 12% improvement compared to the MMM in detecting the biologically-identified 50 expressed genes by Thomas et al. The results witness to the successful performance of the proposed algorithm in quantitative pathogenesis of diseases and comparative evaluation of treatment methods.

Algorithms↗

PubMed2003

Computer aided vertebral visualization and analysis: a methodology using the sand rat, a small animal model of disc degeneration.

BACKGROUND: The purpose of this study is to present an automated system that analyzes digitized x-ray images of small animal spines identifying the effects of disc degeneration. The age-related disc and spine degeneration that occurs in the sand rat (Psammomys obesus) has previously been documented radiologically; selected representative radiographs with age-related changes were used here to develop computer-assisted vertebral visualization/analysis techniques. Techniques presented here have the potential to produce quantitative algorithms that create more accurate and informative measurements in a time efficient manner. METHODS: Signal and image processing techniques were applied to digitized spine x-ray images the spine was segmented, and orientation and curvature determined. The image was segmented based on orientation changes of the spine; edge detection was performed to define vertebral boundaries. Once vertebrae were identified, a number of measures were introduced and calculated to retrieve information on the vertebral separation/orientation and sclerosis. RESULTS: A method is described which produces computer-generated quantitative measurements of vertebrae and disc spaces. Six sand rat spine radiographs illustrate applications of this technique. Results showed that this method can successfully automate calculation and analysis of vertebral length, vertebral spacing, vertebral angle, and can score sclerosis. Techniques also provide quantitative means to explore the relation between age and vertebral shape. CONCLUSIONS: This method provides a computationally efficient system to analyze spinal changes during aging. Techniques can be used to automate the quantitative processing of vertebral radiographic images and may be applicable to human and other animal radiologic models of the aging/degenerating spine.

Aging↗

PubMed2003

Breast cancer detection in gadolinium-enhanced MR images by static region descriptors and neural networks.

PURPOSE: To automate the diagnosis of malignancy by classifying breast tissues as negative or positive for malignancy in gadolinium-enhanced dynamic magnetic resonance (MR) images, using static region descriptors and a neural network classifier. MATERIALS AND METHODS: We propose a novel approach whereby the classifier evaluates a number of parameters that identify important tumor characteristics, as obtained by digital image processing techniques. These parameters include static signal intensity (SI) after contrast enhancement, mass margin descriptors, evaluation of mass shape by calculation of eccentricity, mass size, and mass granularity by texture analysis. Datasets for 14 patients were obtained by use of the 1.5T PMRTOW Clinical Imager. RESULTS: Statistical performance evaluation of the neural networks indicated 90%-100% sensitivity, 91%-100% specificity, and 91%-100% accuracy. CONCLUSION: Although this work is preliminary, it may reduce overall health-care time and costs, and enable higher accuracy in automated breast cancer detection systems.

Breast Neoplasms↗