The effects of TIS and MI on the texture features in ultrasonic fatty liver images

Yuan Zhao; Xinyao Cheng; Mingyue Ding

doi:10.1117/12.2255652

3 March 2017 The effects of TIS and MI on the texture features in ultrasonic fatty liver images

Yuan Zhao, Xinyao Cheng, Mingyue Ding

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101343K (2017) https://doi.org/10.1117/12.2255652
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Nonalcoholic fatty liver disease (NAFLD) is prevalent and has a worldwide distribution now. Although ultrasound imaging technology has been deemed as the common method to diagnose fatty liver, it is not able to detect NAFLD in its early stage and limited by the diagnostic instruments and some other factors. B-scan image feature extraction of fatty liver can assist doctor to analyze the patient’s situation and enhance the efficiency and accuracy of clinical diagnoses. However, some uncertain factors in ultrasonic diagnoses are often been ignored during feature extraction. In this study, the nonalcoholic fatty liver rabbit model was made and its liver ultrasound images were collected by setting different Thermal index of soft tissue (TIS) and mechanical index (MI). Then, texture features were calculated based on gray level co-occurrence matrix (GLCM) and the impacts of TIS and MI on these features were analyzed and discussed. Furthermore, the receiver operating characteristic (ROC) curve was used to evaluate whether each feature was effective or not when TIS and MI were given. The results showed that TIS and MI do affect the features extracted from the healthy liver, while the texture features of fatty liver are relatively stable. In addition, TIS set to 0.3 and MI equal to 0.9 might be a better choice when using a computer aided diagnosis (CAD) method for fatty liver recognition.

Citation Download Citation

Yuan Zhao, Xinyao Cheng, and Mingyue Ding "The effects of TIS and MI on the texture features in ultrasonic fatty liver images", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343K (3 March 2017); https://doi.org/10.1117/12.2255652

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KEYWORDS

Liver

Feature extraction

Ultrasonics

Ultrasonography

Diagnostics

Computer aided diagnosis and therapy

Image classification

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