Automated stratification of liver disease in ultrasound: An online accurate feature classification paradigm

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• 作者：Luca Saba^a ; Nilanjan Dey^b ; Amira S. Ashour^c ; Sourav Samanta^b ; Siddhartha Sankar Nath^b ; Sayan Chakraborty^b ; Joã ; o Sanches^d ; Dinesh Kumar^b ; RuiTato Marinho^e ; Jasjit S. Suri^b ; ^f ; ^{jsuri@comcast.net" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Fatty liver disease ; Haralick features ; Gupta transform ; Gabor transform ; Back propagation network ; Accuracy
• 刊名：Computer Methods and Programs in Biomedicine
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：130
• 期：Complete
• 页码：118-134
• 全文大小：1980 K

文摘

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.