An automatic computer-aided diagnosis system for liver tumours on computed tomography images

Authors:
S. S. Kumar;R. S. Moni;J. Rajeesh
Affiliations:
Dept. of EIE, Noorul Islam College of Engineering, Kumaracoil 629 180, India;Dept. of EEE, Noorul Islam College of Engineering, Kumaracoil 629 180, India;Dept. of ECE, Noorul Islam College of Engineering, Kumaracoil 629 180, India
Venue:
Computers and Electrical Engineering
Year:
2013

Citing 17
Cited 0

Seeded Region Growing

IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to ROC analysis

Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Real time multiple objects tracking and identification based on discrete wavelet transform

Pattern Recognition
Wavelet based co-occurrence histogram features for texture classification with an application to script identification in a document image

Pattern Recognition Letters
Improving classification performance of sonar targets by applying general regression neural network with PCA

Expert Systems with Applications: An International Journal
Orthogonal moments based texture analysis of CT liver images

Pattern Recognition Letters
Implementational aspects of the contourlet filter bank and application in image coding

EURASIP Journal on Advances in Signal Processing
Classification of Hepatic Tissues from CT Images Based on Texture Features and Multiclass Support Vector Machines

ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Tumor tissue identification based on gene expression data using DWT feature extraction and PNN classifier

Neurocomputing
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition
Performance evaluation of multiple classification of the ultrasonic supraspinatus images by using ML, RBFNN and SVM classifiers

Expert Systems with Applications: An International Journal
A filter bank for the directional decomposition of images: theoryand design

IEEE Transactions on Signal Processing
A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

IEEE Transactions on Information Technology in Biomedicine
Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications

IEEE Transactions on Information Technology in Biomedicine
Comparison of Multiresolution Features for Texture Classification of Carotid Atherosclerosis From B-Mode Ultrasound

IEEE Transactions on Information Technology in Biomedicine
The contourlet transform: an efficient directional multiresolution image representation

IEEE Transactions on Image Processing
Probabilistic neural-network structure determination for pattern classification

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Liver cancer, one of the more common cancer diseases that cause a large number of deaths every year, can be reduced by early detection and diagnosis. Computer-Aided Diagnosis (CAD) can play a key role in the early detection and diagnosis of liver cancer. This paper develops a novel computer-aided diagnosis system focussing on the discriminating power of statistical texture descriptors in characterizing hepatocellular (malignant) from hemangioma (benign) liver tumours. The CAD system consists of three stages: (i) automatic tumour segmentation, (ii) texture feature extraction and (iii) tumour characterization using a classifier. Specifically, four features sets, the original gray level; co-occurrence of gray level; wavelet coefficient statistics and contourlet coefficient statistics are extracted from the tumour region of interest. A probabilistic neural network classifier is used to investigate the ability of each feature set in differentiating malignant from benign tissues. The performance of the CAD system evaluated using a database of images indicates that the highest accuracy achieved is 96.7% and the highest sensitivity and specificity are 97.3% and 96%, respectively that had been obtained with the contourlet coefficient co-occurrence features. The experimental results suggest that the developed CAD system has great potential and promise in the automatic diagnosis of both benign and malignant tumours of liver.