Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images

Abstract

The purpose of this study was to apply a novel method of multiscale echo texture analysis for distinguishing benign (hemangiomas) from malignant (hepatocellular carcinomas (HCCs) and metastases) focal liver lesions in B-mode ultrasound images. In this method, regions of interest (ROIs) extracted from within the lesions were decomposed into subimages by wavelet packets. Multiscale texture features that quantify homogeneity of the echogenicity were calculated from these subimages and were combined by an artificial neural network (ANN). A subset of the multiscale features was selected that yielded the highest performance in the classification of lesions measured by the area under the receiver operating characteristic curve (Az). In an analysis of 193 ROIs consisting of 50 hemangiomas, 87 hepatocellular carcinomas and 56 metastases, the multiscale features yielded a high A: value of 0.92 in distinguishing benign from malignant lesions, 0.93 in distinguishing hemangiomas from HCCs and 0.94 in distinguishing hemangiomas from metastases. Our new multiscale texture analysis method can effectively differentiate malignant from benign lesions, and thus has the potential to increase the accuracy of diagnosis of focal liver lesions in ultrasound images.

Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

“…Almost 70 % of all the HEM cases encountered in routine practice are typical HEMs. Typical HEM appears as a well circumscribed uniformly hyperechoic lesion [5,9,10,15,[21][22][23]. MET is the most common secondary malignant FLL [2,7,9,[21][22][23].…”

“…Atypical MET lesions can appear with extremely variable sonographic appearances ranging from anechoic, hypoechoic, isoechoic, hyperechoic and even with mixed echogenicity [5, 8-10, 23, 24]. Differentiating atypical MET lesions from HCC lesions and HEM lesions is considerably difficult [9,11,16,[21][22][23]25].…”

“…The sonographic appearances of SHCC lesions vary from hypoechoic to hyperechoic [23]. LHCC lesions appear frequently with mixed echogenicity [9,10].…”

“…Yoshida et al [23] evaluated the binary classification between HEM and HCC, HEM and MET as well as HCC and MET lesions with multi-scale wavelet packet texture features and NN classifiers. Mittal et al [15] used FOS, GLCM, GLRLM, GWT and Laws' Features (40) HCC (50) MET (50) NOR (40) Cyst (25) HEM (30) HCC (40) MET ( for classification of NOR, Cyst, HEM, HCC and MET liver classes by using ensemble of NN classifiers.…”

“…In the present work, in order to design an efficient CAD system based on ensemble of NN classifiers, feature dimensionality reduction is carried out by using principal component analysis (PCA) for designing each classifier. In several studies [15,23,28,29], only ROIs within the lesions have been considered for computing texture features. Virmani et al [16,21,22] used both texture and texture ratio features for classification of FLLs.…”