SVM-Based Characterization of Liver Ultrasound Images Using Wavelet Packet Texture Descriptors

Abstract

A system to characterize normal liver, cirrhotic liver and hepatocellular carcinoma (HCC) evolved on cirrhotic liver is proposed in this paper. The study is performed with 56 real ultrasound images (15 normal, 16 cirrhotic and 25 HCC liver images) taken from 56 subjects. A total of 180 nonoverlapping regions of interest (ROIs), i.e. 60 from each image class, are extracted by an experienced participating radiologist. The multiresolution wavelet packet texture descriptors, i.e. mean, standard deviation and energy features, are computed from all 180 ROIs by using various compact support wavelet filters including Haar, Daubechies (db4 and db6), biorthogonal (bior3.1,bior3.3 and bior4.4), symlets (sym3 and sym5) and coiflets (coif1 and coif2). It is observed that a combined texture descriptor feature vector of length 48 consisting of 16 mean, 16 standard deviation and 16 energy features estimated from all 16 subband feature images (wavelet packets) obtained by second-level decomposition with two-dimensional wavelet packet transform by using Haar wavelet filter gives the best characterization performance of 86.6 %. Feature selection by genetic algorithm-support vector machine method increased the classification accuracy to 88.8 % with sensitivity of 90 % for detecting normal and cirrhotic cases and sensitivity of 86.6 % for HCC cases. Considering limited sensitivity of B-mode ultrasound for detecting HCCs evolved on cirrhotic liver, the sensitivity of 86.6 % for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.

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

“…Virmani et al [16,21,22] used an ROI size of 32×32 pixels. It is worth mentioning that the use of 10×10 pixels and even 25×25 pixels as ROI size yields a smaller number of pixels in comparison to the minimum 800 pixels required to estimate reliable statistics [16,18,[40][41][42]. Yoshida et al [23] used 64×64 pixels as ROI size, possibly because they used high-resolution scanned images instead of real US images.…”

“…The sonographic appearance of normal (NOR) liver is homogeneous with slightly increased echogenicity as compared to the right kidney [18][19][20]. All NOR cases are considered as typical as there is no atypical appearance for NOR liver tissue.…”

“…In fact, in radiology practice, cirrhosis is always considered as precursor to the development of HCC. The detection of SHCCs on the top of coarse and nodular cirrhotic liver is considerably difficult [2,9,16,18] accordingly, only the HCC lesions developed on cirrhotic liver are considered in this study.…”

“…There are certain disadvantages associated with use of conventional gray scale B-mode US for characterization of FLLs: (1) limited sensitivity for detection of small FLLs (<2 cm) developed on cirrhotic liver which is already nodular and coarse-textured [5,[8][9][10]18], (2) in certain cases, sonographic appearance of HCC and atypical MET lesions are overlapping [5, 8-10, 18, 24], (3) sonographic appearances of cystic MET and atypical Cyst are often overlapping [8,9], (4) sonographic appearances of HEM sometimes mimic HCC lesions [9, 11, 15, 17, 21-23, 26, 27] and (5) in certain cases, it is difficult to characterize isoechoic lesions with very slim liver to lesion contrast [2,9,15]. Thus, conventional gray scale B-mode US offers limited sensitivity for detection and characterization of FLLs [2,5,6,[8][9][10].…”

Characterization of Primary and Secondary Malignant Liver Lesions from B-Mode Ultrasound

“…In the present work, support vector machine (SVM) has been chosen for the classification task because classifier designs which use regularization like SVM are less prone to overfitting and obtain good generalization performance to a certain extent even without feature space dimensionality reduction [16][17][18]. Extensive literature surveys on texture classification reveal that SVM has shown remarkable performance for classification of medical images [19][20][21][22][23][24][25][26][27].…”