Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 239706, 10 pages
http://dx.doi.org/10.1155/2014/239706
Research Article

Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network

1Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Petru Rares Street, No. 2, 200349 Craiova, Romania
2Department of Medical Informatics, University of Medicine and Pharmacy of Craiova, Petru Rares Street, No. 2, 200349 Craiova, Romania
3Department of Histology, University of Medicine and Pharmacy of Craiova, Petru Rares Street, No. 2, 200349 Craiova, Romania
4Department of Pathology, University of Medicine and Pharmacy “Carol Davilla,” Bucharest, Bulevardul Eroii Sanitari 8, 050474 București, Romania
52nd Medical Department, University of Medicine and Pharmacy of Craiova, Petru Rares Street, No. 2, 200349 Craiova, Romania
6Department of Surgery, University of Medicine and Pharmacy of Craiova, Petru Rares Street, No. 2, 200349 Craiova, Romania

Received 8 December 2013; Revised 10 March 2014; Accepted 25 March 2014; Published 16 June 2014

Academic Editor: Wei Mike Liu

Copyright © 2014 Dan Ionuț Gheonea et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background and Aims. Hepatocellular carcinoma (HCC) remains a leading cause of death by cancer worldwide. Computerized diagnosis systems relying on novel imaging markers gained significant importance in recent years. Our aim was 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.