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

Hiroyuki Yoshida*, David D. Casalino, Bilgin Keserci, Abdulhakim Coskun, Omer Ozturk, Ahmet Savranlar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

118 Scopus citations


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 Az 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.

Original languageEnglish (US)
Pages (from-to)3735-3753
Number of pages19
JournalPhysics in Medicine and Biology
Issue number22
StatePublished - Nov 21 2003

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images'. Together they form a unique fingerprint.

Cite this