WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
Browse
4 results
Search Results
Conference Object Quasi-Supervised Learning on Dna Regions in Colon Cancer Histology Slides(Institute of Electrical and Electronics Engineers Inc., 2013) Köktürk, Başak Esin; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyThe aim of this study, nuclei base automatic detection of cancerous regions via determination of DNA-rich regions in high definition histology images. In the study; DNA-rich regions were determined using k-means clustering and some mathematical morphology operations, the diseased regions were diagnosed using morphological characteristics via quasi-supervised learning. It's observed that quasi-supervised learning method successfully separates cancerous chromatin regions from others successfully with experiments of colon cross-section histology images.Conference Object Citation - Scopus: 2Model-Free Expectation Maximization for Divisive Hierarchical Clustering of Multicolor Flow Cytometry Data(IEEE, 2014) Köktürk, Başak Esin; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyThis paper proposes a new method for automated clustering of high dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carried out using a model-free expectation maximization scheme that exploits the posterior probability computation capability of the quasi-supervised learning algorithm. The divisions are carried out until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-color flow cytometry datasets showed that the proposed method can accurately capture the prominent clusters without requiring any knowledge on the number of clusters or their distribution models.Conference Object Detection of Vessels in a Retinal Image Using Decomposed Pixel Classification Method(Institute of Electrical and Electronics Engineers Inc., 2015) Sargan, Erman; Köktürk, Başak Esin; 01. Izmir Institute of TechnologyThe aim of this study, determination of blood vessels on high dimensional retinal images using decomposed pixel classification method. In this work intensity histograms are obtained by using vectoral directions. Local minimum and local maximum points are found by using these histograms to detect location of vessels. In addition to this, vessel thickness information are detected by determining a threshold point on these histograms.Article Citation - Scopus: 1Annealing-Based Model-Free Expectation Maximisation for Multi-Colour Flow Cytometry Data Clustering(Inderscience Enterprises Ltd., 2016) Köktürk, Başak Esin; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyThis paper proposes an optimised model-free expectation maximisation method for automated clustering of high-dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carriedout using a model-free expectation maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learningalgorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions arecontinued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-colour flow cytometrydatasets showed that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters ortheir distribution models.