Automatic Diagnosis of Liver Diseases from Ultrasound Images (original) (raw)
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Advances in Bioinformatics, 2014
The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as "fatty liver, " "cirrhosis, " and "hepatomegaly" produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that "mixed feature set" is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.
The aim of the study is to establish a computer-aided diagnosis system for diffuse liver diseases such as chronic active hepatitis (CAH) and liver cirrhosis (LC). We introduced an artificial neural network in the classification of these diseases. In this system the neural network was trained by feature parameters extracted from B-mode ultrasonic images of normal liver (NL), CAH and LC. Therefore we need not input any a priori information about these diseases. For input data we used seven parameters calculated by five regions of interest (ROIs) in each image. They are variance of pixel values in an ROI, coefficient of variation, annular Fourier power spectrum, longitudinal Fourier power spectrum, and variation of the means of the five ROIs. In addition, we used two more parameters calculated from a co-occurrence matrix of pixel values in an ROI. The results showed that the accuracies of the neural network were 83.8 % for LC, 90.0 % for CAH and 93.6 % for NL, and that the system was considered to be helpful for clinical and educational use.
Journal of Medical Systems, 2009
Recent advances in digital imaging technology have greatly enhanced the interpretation of critical/pathology conditions from the 2-dimensional medical images. This has become realistic due to the existence of the computer aided diagnostic tool. A computer aided diagnostic (CAD) tool generally possesses components like preprocessing, identification/selection of region of interest, extraction of typical features and finally an efficient classification system. This paper enumerates on development of CAD tool for classification of chronic liver disease through the 2-D image acquired from ultrasonic device. Characterization of tissue through qualitative treatment leads to the detection of abnormality which is not viable through qualitative visual inspection by the radiologist. Common liver diseases are the indicators of changes in tissue elasticity. One can show the detection of normal, fatty or malignant condition based on the application of CAD tool thereby, further investigation required by radiologist can be avoided. The proposed work involves an optimal block analysis (64 × 64) of the liver image of actual size 256 × 256 by incorporating Gabor wavelet transform which does the texture classification through automated mode. Statistical features such as gray level mean as well as variance values are estimated after this preprocessing mode. A non-linear back propagation neural network (BPNN) is applied for classifying the normal (vs) fatty and normal (vs) malignant liver which yields a classification accuracy of 96.8%. Further multi classification is also performed and a classification accuracy of 94% is obtained. It can be concluded that the proposed CAD can be used as an expert system to aid the automated diagnosis of liver diseases.
PCA NN Based Classifier For Liver Diseases from Ultrasonic Liver Images
Emerging Trends in Engineering and …, 2009
This research aims at developing an optimal neural network based DSS, which is aimed at precise and reliable diagnosis of chronic active hepatitis (CAH) and cirrhosis (CRH). The principal component analysis neural network is designed scrupulously for classification of these diseases. The neural network is trained by eight quantified texture features, which were extracted from five different region of interests (ROIs) uniformly distributed in each B-mode ultrasonic image of normal liver (NL), Chronic Active Hepatitis (CAH) and Cirrhosis (CRH). The proposed PCA NN classifier is the most efficient learning machine that is able to classify all three cases of diffused liver with average classification accuracy of 95.23%; 6 cases of cirrhosis out of 7 (6/7), all 7 cases of chronic active hepatitis (7/7) and all 15 cases of normal liver (15/15).
Characterization of liver Disease Based on Ultrasound Imaging System
International Journal of Engineering and Advanced Technology, 2021
Computer-Aided Detection (CAD) systems are one of the most effected tools nowadays in aiding physicians in the detection of liver tumors at early stage. In this paper, the CADe system will be built which has the ability to detect the abnormal tumor inside the liver. In order to create that system, different types of classifiers must be implemented. In our CADe system, a support vector machine (SVM) and K-Nearest Neighbor (KNN) will be used as classifiers. A total number of 120 images including the normal and abnormal cases were collected. Initially, the features will be extracted from database images in order to distinguish between the classes of those liver tumors. Then, by using SVM and KNN the images will be classified into two classes normal and abnormal cases. The paper reveals that SVM and KNN, which demonstrated 100 percent precision, 100 percent sensitivity, and 100 percent specificity, were the best classifiers.
International Journal of Medical Informatics, 1999
Visual criteria for diagnosing diffused liver diseases from ultrasound images can be assisted by computerized tissue classification. Feature extraction algorithms are proposed in the paper to extract the tissue characterization parameters from liver images. The resulting parameter set is further processed to obtain the minimum number of parameters which represent the most discriminating pattern space for classification. This preprocessing step has been applied to over 120 distinct pathology-investigated cases to obtain the learning data for classification. The extracted features are divided into independent training and test sets, and are used to develop and compare both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms of classification based on statistical and neural network methods are presented and tested. We show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data.
International Journal of Biomedical Engineering and Technology, 2007
Image analysis techniques have played an important role in several medical applications. In this paper, the classification of ultrasonic liver images is studied by using texture features extracted from Laws' method, Autocorrelation method, Edge frequency methods, Gabor Wavelet method and Co-occurrence probability method. Then the best features from different methods are combined to improve the classification. The features from these methods are used to classify four sets of ultrasonic liver images-Normal, Cyst, Benign and Malignant, and how well they suit in classifying the abnormalities is reported. A Neural Network classifier is employed to evaluate the performance of these features based on their recognition ability.
Automatic Classification Algorithm for Diffused Liver Diseases Based on Ultrasound Images
IEEE Access
Diffuse liver diseases such as fatty liver and cirrhosis, are leading causes of disability and fatality across the world. Early diagnosis of these diseases is extremely important to save lives and improve the effectiveness of treatment. This study proposes a non-invasive method for diagnosing liver diseases using ultrasound images, by classifying liver tissue as normal, steatosis, or cirrhosis, using feature extraction, feature selection, and classification. First, the correlation, homogeneity, variance, entropy, contrast, energy, long run emphasis, run percentage, and standard deviation are determined. Second, the most efficient features are selected based on the Fisher discriminant and manual selection methods. Third, three voting-based subclassifiers are used, namely, the normal/steatosis, normal/cirrhosis, and steatosis/cirrhosis classifiers. The final liver tissue classification is based on the majority function. Our classification method provides two key contributions: combination of two different feature selection methods, avoiding the limitations of each method while benefiting from their strengths; and classifier categorization into three sub-classifiers, where the overall classification is based on the decision of each individual sub-classifier. We obtained recognition accuracies for the normal/steatosis, normal/cirrhosis, and steatosis/cirrhosis classifiers as 95%, 95.74%, and 94.23%, respectively, and an overall recognition accuracy of 95%, which outperforms other methods. INDEX TERMS Feature extraction, Fisher discriminant, region of interest, majority based classifier, liver diseases.
Detection of pathologic liver using ultrasound images
Biomedical Signal Processing and Control, 2014
Fatty liver or steatosis is a pathology characterized by fat accumulation in the liver cells. Ultrasound is the most common technique used for its evaluation, however the diagnosis is strongly dependent on the physician's expertise and system settings. These drawbacks have motivated the development of procedures for the quantitative analysis of ultrasound images to help the steatosis diagnosis. In this work, three approaches are presented and tested with human liver images. The first one addresses textural analysis of the hepatic parenchyma using five classifiers, 357 features, a feature selector, and classifiers fusion. Its performance is measured by two parameters: accuracy and area under the ROC curve. The second makes use of the hepatorenal coefficient followed by a statistical analysis to discriminate echogenicity differences between liver and kidney. The third is based on the acoustical attenuation coefficient evaluated over a line traced in the images with parallel orientation to the acoustical beam. The use of classifiers fusion has provided better results (accuracy of 0.79), when compared with the performance of the best one considered alone (0.77 for ANN). The hepatorenal coefficient proved to be a good parameter for steatosis detection with calculated sensitivity and specificity of 0.90 and 0.88, respectively. It was observed the hepatorenal coefficient is not influenced by the ultrasound machine parameters. The attenuation coefficient provided lower sensitivity and specificity values than the ones from the hepatorenal coefficient.
IRJET-Classification of Liver Disease Based on US Images
Liver disease is progressive, asymptotic and potentially fatal Diseases. In this study, an automatic hierarchical procedure to classify and stage liver disease using ultrasound images is described. The database for this work is the ultra sonographic images of liver disease along with the healthy conditions. Initially the contrast enhancement is applied to the input image that helps to identify the object, after that discrete wavelet transform is applied which helps to remove the speckle noise, then the approximate component is subjected to K-mean clustering which segments the image with respect to the minimum Euclidian distance. The classification strategy is performed using the classifier such as Neural Network. It is used to analyze the Liver disease which will be useful to doctors for the second opinion.