Filtreler

2014 - 20152
Filtreleri Sıfırla

Ayarlar

Yazar: Tek, Faik Boray × WoS Q: Q1 ×

Arama Sonuçları

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Yayın
    Assessment of algorithms for mitosis detection in breast cancer histopathology images
    (Elsevier Science BV, 2015-02) Veta, Mitko; Van Diest, Paul J.; Willems, Stefan Martin; Wang, Haibo; Madabhushi, Anant; Cruz-Roa, Angel; Gonzalez, Fabio; Larsen, Anders Boesen Lindbo; Vestergaard, Jacob Schack Chack; Dahl, Anders Bjorholm; Cireşan, Dan Claudiu; Schmidhuber, Jürgen U.; Giusti, Alessandro; Gambardella, Luca M.; Tek, Faik Boray; Walter, Thomas C.; Wang, Chingwei; Kondo, Satoshi; Matuszewski, Bogdan J.; Precioso, Frédéric; Snell, Violet; Kittler, Josef; De Campos, Teofilo E.; Khan, Adnan M.; Rajpoot, Nasir Mahmood; Arkoumani, Evdokia; Lacle, Miangela M.; Viergever, Max A.; Pluim, Josien P W
    The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues.In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.
  • Küçük Resim
    Yayın
    Robust localization and identification of African clawed frogs in digital images
    (Elsevier Science BV, 2014-09) Tek, Faik Boray; Cannavo, Flavio; Nunnari, Giuseppe; Kale, İzzet
    We study the automatic localization and identification of African clawed frogs (Xenopus laevis sp.) in digital images taken in a laboratory environment. We propose a novel and stable frog body localization and skin pattern window extraction algorithm. We show that it compensates scale and rotation changes very well. Moreover, it is able to localize and extract highly overlapping regions (pattern windows) even in the cases of intense affine transformations, blurring, Gaussian noise, and intensity transformations. The frog skin pattern (i.e. texture) provides a unique feature for the identification of individual frogs. We investigate the suitability of five different feature descriptors (Gabor filters, area granulometry, HoG,(1) dense SIFT,(2) and raw pixel values) to represent frog skin patterns. We compare the robustness of the features based on their identification performance using a nearest neighbor classifier. Our experiments show that among five features that we tested, the best performing feature against rotation, scale, and blurring modifications was the raw pixel feature, whereas the SIFT feature was the best performing one against affine and intensity modifications.