Mühendislik Fakültesi

Permanent URI for this communityhttps://hdl.handle.net/20.500.12416/2

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A Combined Spatial and Frequency Based Texture Model for Organ Segmentation in Computed Tomography Examinations
(Amer Scientific Publishers, 2014) Hassanpour, Reza; Shahbahrami, Asadollah; Wong, Stephan; Jafari, Aref
The organ segmentation in computed tomography (CT) examination is a tedious and error prone task. The local similarity of the pixels from different organs, and the differences between the pixels of the same organ observed in different examinations are two most challenging problems affecting the segmentation process. In this study, statistical and spectral texture properties are combined with the a-priori knowledge about the human body to develop a model for reliably segmenting organs in CT examinations. The main goal of the developed model is fusing local and global statistics to support spatial-frequency analysis and to maximize the simultaneous localization of energy in both spatial and frequency domains. The feature space dimension is reduced by means of a wrapper technique applied as a pre-processing filter. The proposed classifier utilizes a linear combination (ensemble) of two support vector machines (SVM) where the first SVM classifies the input samples according to their textural information and the second one correct the results of the first classifier by searching the spatial information of those samples in a statistical atlas.
Citation - WoS: 3
Identification of Circulating Tumor Cells Using Plasmonic Resonance Effect: Lab-On Analysis and Modelling
(Amer Scientific Publishers, 2020) Salmanogli, Ahmad; Gokcen, Dincer
Circulating tumor cells are widely used as biomarkers of cancer. Although early detection of these cells is vital for diagnosis and prognosis of deadly cancer, it is still a challenging issue due to the complex matrix of blood and their low presence in the bloodstream. In the present study, we propose a micro-channeled lab-on-a-chip system using two distinct methods based upon dielectrophoretic force and electrical properties of cells to increase the cell detection capability and identification efficiency and accuracy. The dielectric properties of cells contribute to the difference between negatively charged residues on the cell surface. Firstly, the dielectrophoretic force is used to separate background cells; then, the proposed high-accuracy identification method is used to better examine and study the unidentified cells. In the next phase, by amplification of the current of the unidentified cells flowing through the nanoparticle plasmonic resonance effects, the microfluidics output efficiency is significantly improved. As a result, highly accurate cell identification is achieved by taking advantage of the nanoparticle plasmonic properties. Overall, nanoparticle scattering in the plasmonic resonance condition, as well as their plasmonic hybridization, can improve output signal-to-noise ratio.

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