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All Journal International Journal of Intelligent Systems and Applications in Engineering
Yuksel, Seniha Esen
Advanced Technology and Science (ATScience)
Computer Science & IT

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Validation of Registration for Renal Dynamic Contrast Enhanced MRI Imaging Yuksel, Seniha Esen
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 3 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.45496

Abstract

In Dynamic Contrast Enhanced Resonance Imaging (DCE-MRI), abdomen is scanned repeatedly and rapidly after injection of a contrast agent. During data acquisition, collected images suffer from the motion induced by the patient if he/she moves or breathes heavily during the scan. Therefore, these images should be aligned accurately to correct the motion. Recently, mutual information (MI) registration has become the first tool to register renal DCE-MRI images before any further processing. However, MI registration is sensitive to initial conditions and optimization methods, and it is bound to fail under certain conditions such as extreme movement or noise in the image. Therefore, if automated image analysis for renal DCE-MRI is to enter the clinical settings, it is necessary to have validation strategies that show the limitations of registration models on known datasets. In this study, two methods are introduced for the validation of registration of renal DCE-MRI images. The first method demonstrates how to use the inverse transform to generate realistic looking DCE-MRI kidney images and use them in validation. The second method shows how to generate checkerboard images and how to evaluate the goodness of registration for real DCE-MRI images. These validation methods can be incorporated into the registration studies to quantitatively and qualitatively demonstrate the success and the limitations of registration models.
Fusion of Target Detection Algorithms in Hyperspectral Images Yuksel, Seniha Esen; Karakaya, Ahmet
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426380

Abstract

Target detection in hyperspectral images is important in many applications including search and rescue operations, defence systems, mineral exploration and border security. For this purpose, several target detection algorithms have been proposed over the years, however, it is not clear which of these algorithms perform best on real data and on sub-pixel targets, and moreover, which of these algorithms have complementary information and should be fused together. The goal of this study is to detect the nine arbitrarily placed sub-pixel targets, from seven different materials from a 1.4km altitude. For this purpose, eight signature-based hyperspectral target detection algorithms, namely the GLRT, ACE, SACE, CEM, MF, AMSD, OSP and HUD, and three anomaly detectors, namely RX, Maxmin and Diffdet, were tested and compared. Among the signature-based target detectors, the three best performing algorithms that have complementary information were identified. Finally these algorithms were fused together using four different fusion algorithms. Our results indicate that with a proper fusion strategy, five of the nine targets could be found with no false alarms.
Co-Authors Karakaya, Ahmet