Ultrasonic liver tissue characterization by feature fusion

Wu, Cheng-Chi; Lee, Wen-Li; Chen, Yung‐Chang; Lai, Cheng-Hung; Hsieh, Kai‐Sheng

doi:10.1016/j.eswa.2012.02.128

Cited by 31 publications

(10 citation statements)

References 28 publications

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“…It is a process of selection of relevant features from serial feature combination set, and resulting feature set is called serial fused feature set. 38…”

Section: Methodsmentioning

confidence: 99%

Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix

Bharti

Mittal

Ananthasivan

2018

Proc Inst Mech Eng H

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Chronic liver diseases are fifth leading cause of fatality in developing countries. Early diagnosis is important for timely treatment and to salvage life. Ultrasound imaging is frequently used to examine abnormalities of liver. However, ambiguity lies in visual interpretation of liver stages on ultrasound images. This difficult visualization problem can be solved by analysing extracted textural features from images. Grey-level difference matrix, a texture feature extraction method, can provide information about roughness of liver surface, sharpness of liver borders and echotexture of liver parenchyma. In this article, the behaviour of variants of grey-level difference matrix in characterizing liver stages is investigated. The texture feature sets are extracted by using variants of grey-level difference matrix based on two, three, five and seven neighbouring pixels. Thereafter, to take the advantage of complementary information from extracted feature sets, feature fusion schemes are implemented. In addition, hybrid feature selection (combination of ReliefF filter method and sequential forward selection wrapper method) is used to obtain optimal feature set in characterizing liver stages. Finally, a computer-aided system is designed with the optimal feature set to classify liver health in terms of normal, chronic liver, cirrhosis and hepatocellular carcinoma evolved over cirrhosis. In the proposed work, experiments are performed to (1) identify the best approximation of derivative (forward, central or backward); (2) analyse the performance of individual feature sets of variants of grey-level difference matrix; (3) obtain optimal feature set by exploiting the complementary information from variants of grey-level difference matrix and (4) analyse the performance of proposed method in comparison with existing feature extraction methods. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 94.5% is obtained by optimal feature set having complementary information from variants of grey-level difference matrix.

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“…It is a process of selection of relevant features from serial feature combination set, and resulting feature set is called serial fused feature set. 38…”

Section: Methodsmentioning

confidence: 99%

Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix

Bharti

Mittal

Ananthasivan

2018

Proc Inst Mech Eng H

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“…In this system, KNN classifier was used and 95.05% accuracy was obtained. Wu et al [25] proposed an optimization algorithm that designed the features. It helped in the automatic liver cirrhosis diagnosis.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of cirrhosis through ultrasound imaging by intensity difference technique

Aggarwal

Bhamrah

Ryait³

2019

J Image Video Proc.

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Cirrhosis is a liver disease that is considered to be among the most common diseases in healthcare. Due to its noninvasive nature, ultrasound (US) imaging is a widely accepted technology for the diagnosis of this disease. This research work proposed a method for discriminating the cirrhotic liver from normal liver through US images. The liver US images were obtained from the radiologist. The radiologist also specified the region of interest (ROI) from these images, and then the proposed method was applied to it. Two parameters were extracted from the US images through differences in intensity of neighboring pixels. Then, these parameters can be used to train a classifier by which cirrhotic region of test patient can be detected. A 2-D array was created by the difference in intensity of the neighboring pixels. From this array, two parameters were calculated. The decision was taken by checking these parameters. The validation of the proposed tool was done on 80 images of cirrhotic and 30 images of normal liver, and classification accuracy of 98.18% was achieved. The result was also verified by the radiologist. The results verified its possibility and applicability for high-performance cirrhotic liver discrimination.

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“…They reported a recognition rate of 81.78% for HCC and cirrhosis on which HCC had evolved. 119 Wu et al 121 proposed two-stage feature fusion method to classify ultrasonic images of liver tissue in three classes: normal, cirrhosis, and hepatitis. GLCM, multiresolution fractal feature, and multiresolution energy feature were extracted, and the resulting fused feature set was used in SVM.…”

Section: Review Of Published Cad Studies In Diffuse Liver Diseasesmentioning

confidence: 99%

Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging

2016

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Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis.

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Ultrasonic liver tissue characterization by feature fusion

Cited by 31 publications

References 28 publications

Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix

Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix

Detection of cirrhosis through ultrasound imaging by intensity difference technique

Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging

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