We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network.
- Authors
Gheonea, Dan Ionut; Streba, Costin Teodor; Vere, Cristin Constantin; Pirici, Daniel; Şerbănescu, Mircea; Comănescu, Maria; Maria Streba, Letiţia Adela; Ciurea, Marius Eugen; Mogoants, Stelian; Rogoveanu, Ion
- Abstract
Background and Aims. Hepatocellular carcinoma (HCC) remains a leading cause of death by cancer worldwide. Computerized diagnosis systems relying on novel imagingmarkers gained significant importance in recent years. Our aimwas to integrate a novel morphometric measurement-the fractal dimension (FD)-into an artificial neural network (ANN) designed to diagnose HCC. Material and Methods. The study included 21 HCC and 28 liver metastases (LM) patients scheduled for surgery. We performed hematoxylin staining for cell nuclei and CD31/34 immunostaining for vascular elements. We captured digital images and used an in-house application to segment elements of interest; FDs were calculated and fed to an ANN which classified them as malignant or benign, further identifying HCC and LM cases. Results. User intervention corrected segmentation errors and fractal dimensions were calculated. ANNs correctly classified 947/1050 HCC images (90.2%), 1021/1050 normal tissue images (97.23%), 1215/1400 LM (86.78%), and 1372/1400 normal tissues (98%). We obtained excellent interobserver agreement between human operators and the system. Conclusion. We successfully implemented FD as a morphometric marker in a decision system, an ensemble of ANNs designed to differentiate histological images of normal parenchyma from malignancy and classify HCCs and LMs.
- Publication
BioMed Research International, 2014, Vol 2014, p1
- ISSN
2314-6133
- Publication type
Article
- DOI
10.1155/2014/239706