Neural Network Classification of Blood Vessels and Tubules Based on Haralick Features Evaluated in Histological Images of Kidney Biopsy

Automatic classification of histological images and histological knowledge modelling of the human cardivascular system = Clasificación automática de imágenes histológicas y modelado del conocimiento histológico del sistema cardiovascular humano

“…However, this technique encounters several problems due to the low definition in some edges of the tissues and organs (Alén et al, 2006;Fujii et al, 2013;Fakhrzadeh et al, 2013). On the other hand, classification and recognition process are based on: (i) clustering methods such as K-means, mean shift, K-nn, among others are employed for histological image segmentation (Wu et al, 2015;He et al, 2011); (ii) SVM, Bayesian networks and another machine learning algorithms are used to characterise and classify healthy and pathology cells, tissues and organs (d. A. Zampirolli et al, 2010;Krishnan et al, 2010;Veillard et al, 2012); (iii) neural networks are used to the classification task producing good results on automatically learned features (Bevilacqua et al, 2015;Jitaree et al, 2013;Kashif et al, 2016); (iv) learning based approach using bag-of-words model, this method is attractive as it offers good classification accuracy at low computation cost than the texture-feature-based methods (Galaro et al, 2011;Nguyen et al, 2015;Cheng et al, 2012). However, some of these works are focus in histopathologic images which have different characteristics to healthy tissues.…”