Abnormality Diagnosis of Musculoskeletal
Radiographs Combined with Shallow Texture
Features
Yunxue Shao and Xin Wang
(&)
College of Computer Science, Inner Mongolia University, Inner Mongolia,
People’s Republic of China
csshyx@imu.edu.cn, 13234751492@163.com
Abstract. In this paper, a new algorithm for anomaly classification combined
with shallow texture features is proposed for radioactive musculoskeletal images. The classification algorithm consists of three steps. The radioactive image is
first preprocessed to enhance image quality. The local binary pattern
(LBP) features of the image are then extracted and merged. Finally, the merged
dataset is sent to the DenseNet169 convolutional neural network to determine
whether it is abnormal. The method presented in this paper achieved an accuracy
of 79.64% on the musculoskeletal radiographs (MURA) dataset, which is
superior to the method that does not combine texture features. The experimental
results show that the shallow texture features of the combined image can more
fully describe the difference between the lesion area and the non-focal area in the
image and the difference between different lesion properties.
Keywords: Local binary pattern (LBP) Á Musculoskeletal radiographs
(MURA) Á DenseNet
1 Introduction
Work-related musculoskeletal disorder (WMSDs) refers to systemic muscle, bone, and
nervous system disorders caused by occupational factors. During the work process, the
musculoskeletal system needs to bear the ergonomic load such as posture load and
strength load, because the operator needs to maintain repetitive movements or a
compulsory position to carry or lift heavy objects. And, the musculoskeletal system of
the limbs is damaged, and there are irritations such as acid, numbness, swelling, and
pain [1]. More than 1.7 billion people worldwide are affected by musculoskeletal
diseases, so it is important for the abnormal detection of musculoskeletal.
In recent years, deep learning has become a hot spot in the field of machine learning
research. Image features extracted by deep convolutional neural networks (DCNN)
have proven to be effective in image classification, segmentation, or retrieval applications. Based on its advantages in non-medical images, DCNN is beginning to be
gradually applied to medical image classification and detection problems. For example,
Spanhol proposed the use of DCNN to classify breast cancer pathology images [2]. Li
proposed a DCNN-based pulmonary nodule classification system [3]; Roth proposed
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 480–486, 2020.
https://doi.org/10.1007/978-981-15-0187-6_57
Radiographs Combined with Shallow Texture
Features
Yunxue Shao and Xin Wang
(&)
College of Computer Science, Inner Mongolia University, Inner Mongolia,
People’s Republic of China
csshyx@imu.edu.cn, 13234751492@163.com
Abstract. In this paper, a new algorithm for anomaly classification combined
with shallow texture features is proposed for radioactive musculoskeletal images. The classification algorithm consists of three steps. The radioactive image is
first preprocessed to enhance image quality. The local binary pattern
(LBP) features of the image are then extracted and merged. Finally, the merged
dataset is sent to the DenseNet169 convolutional neural network to determine
whether it is abnormal. The method presented in this paper achieved an accuracy
of 79.64% on the musculoskeletal radiographs (MURA) dataset, which is
superior to the method that does not combine texture features. The experimental
results show that the shallow texture features of the combined image can more
fully describe the difference between the lesion area and the non-focal area in the
image and the difference between different lesion properties.
Keywords: Local binary pattern (LBP) Á Musculoskeletal radiographs
(MURA) Á DenseNet
1 Introduction
Work-related musculoskeletal disorder (WMSDs) refers to systemic muscle, bone, and
nervous system disorders caused by occupational factors. During the work process, the
musculoskeletal system needs to bear the ergonomic load such as posture load and
strength load, because the operator needs to maintain repetitive movements or a
compulsory position to carry or lift heavy objects. And, the musculoskeletal system of
the limbs is damaged, and there are irritations such as acid, numbness, swelling, and
pain [1]. More than 1.7 billion people worldwide are affected by musculoskeletal
diseases, so it is important for the abnormal detection of musculoskeletal.
In recent years, deep learning has become a hot spot in the field of machine learning
research. Image features extracted by deep convolutional neural networks (DCNN)
have proven to be effective in image classification, segmentation, or retrieval applications. Based on its advantages in non-medical images, DCNN is beginning to be
gradually applied to medical image classification and detection problems. For example,
Spanhol proposed the use of DCNN to classify breast cancer pathology images [2]. Li
proposed a DCNN-based pulmonary nodule classification system [3]; Roth proposed
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 480–486, 2020.
https://doi.org/10.1007/978-981-15-0187-6_57