Biomedical subjects

Omer Demirkaya

Publications and source records attributed to Omer Demirkaya.

5 recordsLinked to original sources

PubMed2005

Segmentation of cDNA microarray spots using markov random field modeling.

MOTIVATION: Spot segmentation is a critical step in microarray gene expression data analysis. Therefore, the performance of segmentation may substantially affect the results of subsequent stages of the analysis, such as the detection of differentially expressed genes. Several methods have been developed to segment microarray spots from the surrounding background. In this study, we have proposed a new approach based on Markov random field (MRF) modeling and tested its performance on simulated and real microarray images against a widely used segmentation method based on Mann-Whitney test adopted by QuantArray software (Boston, MA). Spot addressing was performed using QuantArray. We have also devised a simulation method to generate microarray images with realistic features. Such images can be used as gold standards for the purposes of testing and comparing different segmentation methods, and optimizing segmentation parameters. RESULTS: Experiments on simulated and 14 actual microarray image sets show that the proposed MRF-based segmentation method can detect spot areas and estimate spot intensities with higher accuracy.

Algorithms↗

PubMed2004

Assessment of reliability of microarray data and estimation of signal thresholds using mixture modeling.

DNA microarray is an important tool for the study of gene activities but the resultant data consisting of thousands of points are error-prone. A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. In this study, we describe an approach based on normal mixture modeling for determining optimal signal intensity thresholds to identify reliable measurements of the microarray elements and subsequently eliminate false expression ratios. We used univariate and bivariate mixture modeling to segregate the microarray data into two classes, low signal intensity and reliable signal intensity populations, and applied Bayesian decision theory to find the optimal signal thresholds. The bivariate analysis approach was found to be more accurate than the univariate approach; both approaches were superior to a conventional method when validated against a reference set of biological data that consisted of true and false gene expression data. Elimination of unreliable signal intensities in microarray data should contribute to the quality of microarray data including reproducibility and reliability of gene expression ratios.

Algorithms↗

PubMed2004

Post-reconstruction filtering of positron emission tomography whole-body emission images and attenuation maps using nonlinear diffusion filtering.

RATIONALE AND OBJECTIVES: Positron emission tomography has been playing an important role as a quantitative molecular imaging modality to measure and image biochemical processes in vivo. The quality of positron emission tomography images may impact the accuracy of the quantitative or semiquantitative information extracted from them. MATERIALS AND METHODS: In this study, the anisotropic diffusion filtering technique was used to de-noise emission images and attenuation maps of a whole body for improving the quantitative accuracy of emission images. The efficacy of the filtering technique was shown on a hot-lesion phantom as well as on whole-body emission images and attenuation maps. RESULTS: The filtering technique showed a superb performance on images reconstructed using the iterative and the filtered back-projection reconstruction techniques. CONCLUSION: Filtering may allow the reconstruction of images with optimal parameters with respect to lesion contrast and image resolution by removing the consequential noise. An improvement of as much as six-fold may be attained. It may also improve the accuracy of the quantitative information, such as standard uptake value extracted from emission images.

Anisotropy↗

PubMed2002

Anisotropic diffusion filtering of PET attenuation data to improve emission images.

Attenuation correction in positron emission tomography (PET) is an essential part of clinical and research studies. However, correction using noisy transmission data acquired over short scan durations has been a problem as the noise is introduced into emission images. This study investigates the effect of smoothing the two-dimensional projections of the attenuation maps (mu-map) using the nonlinear anisotropic diffusion filtering method. Experiments are presented on a whole-body study to qualitatively evaluate the efficacy of the method in reducing the random noise and streak artefacts. The results show that image quality is significantly improved with minimal resolution loss. A reduction in statistical noise was quantitatively demonstrated when the same approach was applied to a cylindrical phantom dataset.

Algorithms↗

PubMedDate not supplied

Role of [18F]fluorodeoxyglucose positron emission tomography in follow-up of differentiated thyroid cancer.

OBJECTIVE: To assess the diagnostic utility of [(18)F]flu-orodeoxyglucose positron emission tomography (FDG PET) in the follow-up of patients with differentiated thyroid cancer (DTC). METHODS: In this study, we used strict definitions of presence and absence of the disease and performed all FDG PET scans while the patients were not taking levothyroxine (LT4). We report the results of conventional FDG PET scans obtained during the follow-up of 50 nonselected patients with DTC (34 female and 16 male patients; median age, 40.5 years; range, 18 to 68). All FDG PET scans and measurement of thyroglobulin (Tg) levels were performed while the patients were not taking LT4 (thyrotropin>or=25 microIU/mL). Tg antibodies were negative in all patients. The initial surgical procedure was total thyroidectomy in all cases, and 26 patients underwent additional operations (2 to 4 procedures). Radioactive iodine (131I) therapy was given to 48 patients (median dose, 5,550 MBq). In 42 patients, FDG PET was used for evaluation of Tg-positive (Tg>2 ng/mL in the absence of LT4 therapy), scan-negative disease. In 8 patients, Tg was 2 ng/mL without LT4 therapy) for >1 year. Disease was considered absent if Tg was <or=2 ng/mL and at least all the following imaging studies were negative: diagnostic radioiodine whole-body scan, chest radiography or spiral computed tomography of the chest, and high-resolution neck ultrasonography. If all these criteria were present, a positive FDG PET scan was considered a false-positive study. RESULTS: FDG PET scans were positive in 27 patients (54%) and negative in 23 (46%). FDG PET results were true-positive in 26 cases, false-positive in 1, true-negative in 7, and false-negative in 16. The sensitivity, specificity, and positive and negative predictive values were 61.9%, 87.5%, 96.3%, and 30.4%, respectively. CONCLUSION: FDG PET scanning is moderately sensitive and specific for detection of persistent or recurrent DTC.

Adolescent↗