Yazar "Akakin, Hatice Çınar" için listeleme
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An Adaptive Algorithm for Detection of Multiple-Type, Positively Stained Nuclei in IHC images with minimal Prior Information: Application to OLIG2 Staining Gliomas
Akakin, Hatice Çınar; Gökozan, Hamza; Otero, Jose; Gürcan, Metin N. (Spie-Int Soc Optical Engineering, 2015)We propose a method to detect and segment the oligodendrocytes and gliomas in OLIG2 immunoperoxidase stained tissue sections. Segmentation of cell nuclei is essential for automatic, fast, accurate and consistent analysis ... -
Automated detection of cells from immunohistochemically-stained tissues: Application to Ki-67 nuclei staining
Akakin, Hatice Çınar; Kong, Hui; Elkins, Camille; Hemminger, Jessica; Miller, Barrie; Ming, Jin; Gürcan, Metin N. (Spie-Int Soc Optical Engineering, 2012)An automated cell nuclei detection algorithm is described to be used for the quantification of immunohistochemically-stained tissues. Detection and segmentation of positively stained cells and their separation from the ... -
Content-Based Microscopic Image Retrieval System for Multi-Image Queries
Akakin, Hatice Çınar; Gürcan, Metin N. (IEEE-Inst Electrical Electronics Engineers Inc, 2012)In this paper, we describe the design and development of a multitiered content-based image retrieval (CBIR) system for microscopic images utilizing a reference database that contains images of more than one disease. The ... -
A Generalized Laplacian of Gaussian Filter for Blob Detection and Its Applications
Kong, Hui; Akakin, Hatice Çınar; Sarma, Sanjay E. (IEEE-Inst Electrical Electronics Engineers Inc, 2013)In this paper, we propose a generalized Laplacian of Gaussian (LoG) (gLoG) filter for detecting general elliptical blob structures in images. The gLoG filter can not only accurately locate the blob centers but also estimate ... -
HEp-2 Cell Classification using a Deep Neural Network Trained for Natural Image Classification
Benligiray, Burak; Akakin, Hatice Çınar (IEEE, 2016)Deep convolutional neural networks is a recently developed method that yields very successful results in image classification. Deep neural networks, which have a high number of parameters, require a large amount of data ...
