Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images.
Fatty liver disease (FLD) is reversible disease and can be treated, if it is identified at an early stage. However, if diagnosed at the later stage, it can progress to an advanced liver disease such as cirrhosis which may ultimately lead to death. Therefore, it is essential to detect it at an early stage before the disease progresses to an irreversible stage. Several non-invasive computer-aided techniques are proposed to assist in the early detection of FLD and cirrhosis using ultrasound images. In this work, we are proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method. Higher order spectra (HOS) bispectrum, HOS phase, fuzzy, Kapoor, max, Renyi, Shannon, Vajda and Yager entropies are extracted from CT coefficients. These extracted features are subjected to locality sensitive discriminant analysis (LSDA) feature reduction method. Then these LSDA coefficients ranked based on F-value are fed to different classifiers to choose the best performing classifier using minimum number of features. Our proposed technique can characterize normal, FLD and cirrhosis using probabilistic neural network (PNN) classifier with an accuracy of 97.33%, specificity of 100.00% and sensitivity of 96.00% using only six features. In addition, these chosen features are used to develop a liver disease index (LDI) to differentiate the normal, FLD and cirrhosis classes using a single number. This can significantly help the radiologists to discriminate FLD and cirrhosis in their routine liver screening.
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia.
U Rajendra
UR
Acharya
Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal University, Manipal 576104, India.
U
U
Raghavendra
Iwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate 020-0693 Japan.
Hamido
H
Fujita
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
Yuki
Y
Hagiwara
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
Joel Ew
JE
Koh
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
Tan
T
Jen Hong
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore. Electronic address: vidya.2kus@gmail.com.
Vidya K
VK
Sudarshan
Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia.
Anushya
A
Vijayananthan
Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia.
Chai Hong
CH
Yeong
Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal University, Manipal 576104, India.
Anjan
A
Gudigar
Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia.
Kwan Hoong
KH
Ng
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D016002:Discriminant Analysis; D019277:Entropy; D005234:Fatty Liver / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008103:Liver Cirrhosis / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D014463:Ultrasonography / Q000379:methods*; D055815:Young Adult
Cirrhosis; Curvelet transform; Fatty liver disease; Locality sensitive discriminant analysis; Probabilistic neural network; Ultrasound