In addition, these selected characteristics are used to develop a liver disease index (LDI) to differentiate normal, FLD and cirrhosis classes using a single number. It has been reported that 9 to 37% of people are affected with NAFLD in different parts of the world [52, 68]. These studies reported accuracies of 82.2% and 82.6% in identifying normal, FLD, and cirrhosis ultrasound images, respectively.
Few studies [36, 55] have used second-order statistics to examine the texture features of ultrasound images from normal, FLD, and cirrhosis ultrasounds. The overview table shows that second order statistics [33], HOS [1], wavelet transform [1, 76] and discrete cosine transform (DCT) [10] are used in the characterization of the liver diseases. Cropped images are then scaled down to 500 × 500 using bicubic interpolation [28] and subjected to contrast-limited adaptive histogram smoothing (CLAHE) [56] to improve the contrast of the ultrasound images.
CLAHE was developed for medical imaging [74] and reduces noise in the homogeneous area of the images. One of the properties of the LSDA algorithm is that it preserves the discriminative and local geometric structure in the data [17]. It is a supervised learning that uses neighborhood classification to predict the class of the test data.
In order to identify the class of the new test data, k number of training data neighbors to the test data will be assessed.
Results
An integrated index, the liver disease index (LDI), is developed in this work using two LSDA coefficients (LSDA7 and LSDA4) to distinguish three classes. This concept was first proposed by Ghista [27] and later used by Acharya for various applications for the identification of medical diseases. The selection of LSDA7 and LSDA4 was made purely based on the empirical study that was conducted on the listed features.
These two features are combined in such a way that the range of values is unique for normal, FLD and cirrhosis classes. It is clear from Table A1 and Figure 3 that LSDA coefficients of entropy features show clear distinction between the three classes. This implies that approximately 4% of the images were incorrectly classified as normal and 100% normal images were correctly classified as normal cases.
NOF: number of functions; TP: true positive; FP: false positive; TN: true negative; FN: false negative. The LDI ranges for subjects with FLD, cirrhosis and normal people are shown in Table 4. It can be seen from Figure 5 that the LDI helps to clearly separate the three classes using a single number.
In addition, we compared our results with other techniques using our set of ultrasound images. It can be seen from Table 5 that our proposed method achieved the highest efficiency with the minimum number of features.
Discussion
In this work, we used CT and extracted different types of entropy features from ultrasound images to evaluate normal patients, patients with FLD and cirrhosis. In addition, LDI is developed using only two features to classify three classes. The main novelty of our proposed method is that it estimates inherent signatures present in images [4].
Fuzzy entropy quantifies uncertainties of the highly irregular signals and is not affected by noise [53]. Thus, the algorithm developed using these significant nonlinear features can effectively classify the normal, FLD, and cirrhosis subjects using ultrasound images. But our proposed method uses entropy features which can identify normal, FLD and cirrhosis images with accuracy of 97.33%, specificity of 100.00% and sensitivity of 96.00% using only six features.
In addition, ultrasonography is a relatively inexpensive and widely available imaging technique that provides important information on liver architecture. It is often the first imaging modality that can be used to detect FLD and liver cirrhosis, which are major risk factors for hepatocellular carcinoma (HCC). However, the sensitivity and specificity of gray-scale ultrasound for the detection of FLD and cirrhosis are less than computed tomography or MRI [64].
Using our proposed CAD algorithm, the liver can be automatically classified into normal, FLD, and cirrhosis using the highest performance gray-scale sonographic images. This is achieved without the need for additional computed tomography or MRI scans, potentially replacing the relatively more expensive shear wave elastography (commonly known as fibroscan). Early detection of FLD and cirrhosis is an essential approach for the clinicians to advise on necessary treatments to prevent the onset of HCC and the related complications.
Automated characterization of three (normal, FLD and cirrhosis) classes without the use of image segmentation techniques. LDI is able to differentiate the three classes using a number, and therefore the diagnosis is quick. The performance of the proposed method needs to be tested with a large diverse database for diversity and applicability.
Conclusion
Appendix
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