CNN-Based Medical Ultrasound Image Quality Assessment

Siyuan Zhang,Weiwei Yi,Yifan Wang,Wenguang Hou,Jingxian Dong,Jiayao Jiang,Hocine Cherifi

doi:10.1155/2021/9938367

Siyuan Zhang, Weiwei Yi + Show 5 more

Open Access

https://doi.org/10.1155/2021/9938367

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Journal: Complexity	Publication Date: Jul 1, 2021
Citations: 11	License type: CC BY 4.0

Affiliation: Huazhong University of Science and Technology

Abstract

The quality of ultrasound image is a key information in medical related application. It is also an important index in evaluating the performance of ultrasonic imaging equipment and image processing algorithms. Yet, there is still no recognized quantitative standard about medical image quality assessment (IQA) due to the fact that IQA is traditionally regarded as a subjective issue, especially in case of the ultrasound medical images. As such, the medical ultrasound IQA on basis of convolutional neural network (CNN) is quantitatively studied in this paper. Firstly, a dataset with 1063 ultrasound images is established through degenerating a certain number of original high-quality images. Subsequently, some operations are performed for the dataset including scoring and abnormal value screening. Then, 478 ultrasonic images are selected as the training and testing examples. The label of each example is obtained by averaging the scores of different doctors. Afterwards, a deep CNN network and a residuals network are taken to establish the IQA models. Meanwhile, the transfer learning strategy is introduced here to accelerate the training and improve the robustness of the model considering the fact that the ultrasound image samples are not abundant. At last, some tests are taken to evaluate the IQA models. They show that the CNN-based IQA is feasible and effective.

Highlights

Introduction and MotivationImage quality assessment (IQA) is to quantitatively evaluate the image, which remains a hot topic in image processing field due to the fact that it is regarded as a benchmark for image processing systems and algorithms Tang et al [1], Kim et al [2], and Ma et al [3]
Mortamet et al evaluated the MR image relying on the part of the atmosphere in the image because 40% of image is the atmosphere in the structure MR brain image Mortamet et al [22]
Due to the complexity of the medical ultrasound image content, the shallow neural network may not be able to well simulate the perception of HVS in evaluating the image. This project adopts a deep convolutional neural network to assess the quality of medical ultrasound images. e research on HVS shows that human is sensitive to the deformation between images. erefore, we firstly train a CNN to learn the difference between distorted image and the related undistorted one

Summary

Introduction and Motivation

Image quality assessment (IQA) is to quantitatively evaluate the image, which remains a hot topic in image processing field due to the fact that it is regarded as a benchmark for image processing systems and algorithms Tang et al [1], Kim et al [2], and Ma et al [3]. E traditional IQA can be divided into two kinds: subjective assessment and quantitative assessment Hemmsen et al [8], Kang et al [9], Bosse et al [10], and Kim and Lee [11] For the former one, the image is scored by observers. In the double excitation assessment, the observers score the image after observing the considered image and its related one with high quality. E full reference IQA relies on the original high-quality image in evaluating the considered image. E full or reduced reference IQA relies on some information to judge the dissimilarity between high-quality and low-quality images.

Dataset

CNN for IQA

48 Subsample

Findings

Experiments and Analysis