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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/11026
Title: Automated stratification of liver disease in ultrasound: An online accurate feature classification paradigm
Authors: Saba L.
Dey N.
Ashour A.S.
Samanta S.
Nath S.S.
Chakraborty S.
Sanches J.
Kumar D.
Marinho R.
Suri J.S.
Published in: Computer Methods and Programs in Biomedicine
Abstract: Purpose: Fatty liver disease (FLD) is one of the most common diseases in liver. Early detection can improve the prognosis considerably. Using ultrasound for FLD detection is highly desirable due to its non-radiation nature, low cost and easy use. However, the results can be slow and ambiguous due to manual detection. The lack of computer trained systems leads to low image quality and inefficient disease classification. Thus, the current study proposes novel, accurate and reliable detection system for the FLD using computer-based training system. Materials and methods: One hundred twenty-four ultrasound sample images were selected retrospectively from a database of 62 patients consisting of normal and cancerous. The proposed training system was generated offline parameters using training liver image database. The classifier applied transformation parameters to an online system in order to facilitate real-time detection during the ultrasound scan. The system utilized six sets of features (a total of 128 features), namely Haralick, basic geometric, Fourier transform, discrete cosine transform, Gupta transform and Gabor transform. These features were extracted for both offline training and online testing. Levenberg-Marquardt back propagation network (BPN) classifier was used to classify the liver disease into normal and abnormal categories. Results: Random partitioning approach was adapted to evaluate the classifier performance and compute its accuracy. Utilizing all the six sets of 128 features, the computer aided diagnosis (CAD) system achieved classification accuracy of 97.58%. Furthermore, the four performance metrics consisting of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) realized 98.08%, 97.22%, 96.23%, and 98.59%, respectively. Conclusion: The proposed system was successfully able to detect and classify the FLD. Furthermore, the proposed system was benchmarked against previous methods. The comparison established an advanced set of features in the Levenberg-Marquardt back propagation network reports a significant improvement compared to the existing techniques. © 2016 Elsevier Ireland Ltd.
Citation: Computer Methods and Programs in Biomedicine (2016), 130(): 118-134
URI: https://doi.org/10.1016/j.cmpb.2016.03.016
http://repository.iitr.ac.in/handle/123456789/11026
Issue Date: 2016
Publisher: Elsevier Ireland Ltd
Keywords: Accuracy
Back propagation network
Fatty liver disease
Gabor transform
Gupta transform
Haralick features
ISSN: 1692607
Author Scopus IDs: 16234937700
55356190900
7005633559
55903798600
56417200500
56041813600
7004263858
57202478211
7005027284
7005613223
Author Affiliations: Saba, L., Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.) di Cagliari - Polo di Monserrato, Università di Cagliari, Italy
Dey, N., Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, United States
Ashour, A.S., Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Egypt
Samanta, S., Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, United States
Nath, S.S., Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, United States
Chakraborty, S., Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, United States
Sanches, J., Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST), Lisbon, Portugal
Kumar, D., Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, United States
Marinho, R., Liver Unit, Department of Gastroenterology and Hepatology, Hospital de Santa Maria, Medical School of Lisbon, Portugal
Suri, J.S., Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, United States, Electrical Engineering Department (Affl.), Idaho State UniversityID, United States
Corresponding Author: Suri, J.S.; Point of Care Devices, Global Biomedical Technologies, Inc.United States; email: jsuri@comcast.net
Appears in Collections:Journal Publications [ME]

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