Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor Classification

Li，Keyu1; Xu，Yangxin1; Zhao，Ziqi2; Meng，Max Q.H.1,2,3

10.1109/ROBIO54168.2021.9739348

2021

978-1-6654-0536-2

2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021

1980-1985

27-31 Dec. 2021

Sanya, China

Abdominal ultrasound imaging has been widely used to assist in the diagnosis and treatment of various abdominal organs. In order to shorten the examination time and reduce the cognitive burden on the sonographers, we present a classification method that combines the deep learning techniques and k-Nearest-Neighbor (k-NN) classification to automatically recognize various abdominal organs in the ultra-sound images in real time. Fine-tuned deep neural networks are used in combination with PCA dimension reduction to extract high-level features from raw ultrasound images, and a k-NN classifier is employed to predict the abdominal organ in the image. We demonstrate the effectiveness of our method in the task of ultrasound image classification to automatically recognize six abdominal organs. A comprehensive comparison of different configurations is conducted to study the influence of different feature extractors and classifiers on the classification accuracy. Both quantitative and qualitative results show that with minimal training effort, our method can "lazily"recognize the abdominal organs in the ultrasound images in real time with an accuracy of 96.67%. Our implementation code is publicly available at https://github.com/LeeKeyu/abdominal_ultrasound_classification.

Abdominal ultrasound Automatic organ recognition Machine learning Ultrasound image classification

其他

英语

EI

20221611977522

Classification (of information) ; Computer aided diagnosis ; Deep neural networks ; Motion compensation ; Nearest neighbor search ; Ultrasonic imaging

Biomedical Engineering:461.1 ; Ergonomics and Human Factors Engineering:461.4 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Computer Applications:723.5 ; Information Sources and Analysis:903.1 ; Optimization Techniques:921.5

2-s2.0-85128229097

Scopus

1.Chinese University of Hong Kong,Department of Electronic Engineering,Hong Kong,Hong Kong
2.Southern University of Science and Technology,Department of Electronic and Electrical Engineering,Shenzhen,China
3.Shenzhen Research Institute,Chinese University of Hong Kong,Shenzhen,China

Li，Keyu,Xu，Yangxin,Zhao，Ziqi,et al. Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor Classification[C],2021:1980-1985.