Investigation of PCA as a compression pre-processing tool for X-ray image classification

Doorsamy, Wesley; Rameshar, Vikash

doi:10.1007/s00521-020-05668-y

Cited by 4 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The support vector machine (SVM) classification is used to categorize IVDs as degenerate or non-degenerate. For 93 clinical sagittal MR pictures of 93 cases, the suggested system was trained, tested, and reviewed [ 11 ]. The trade-off between volume and veracity in X-ray image-based bone fracture categorization is investigated in this study.…”

Section: Related Workmentioning

confidence: 99%

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Xiu

Rong

Zhang

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The area of medical diagnosis has been transformed by computer-aided diagnosis (CAD). With the advancement of technology and the widespread availability of medical data, CAD has gotten a lot of attention, and numerous methods for predicting different pathological diseases have been created. Ultrasound (US) is the safest clinical imaging method; therefore, it is widely utilized in medical and healthcare settings with computer-aided systems. However, owing to patient movement and equipment constraints, certain artefacts make identification of these US pictures challenging. To enhance the quality of pictures for classification and segmentation, certain preprocessing techniques are required. Hence, we proposed a three-stage image segmentation method using U-Net and Iterative Random Forest Classifier (IRFC) to detect orthopedic diseases in ultrasound images efficiently. Initially, the input dataset is preprocessed using Enhanced Wiener Filter for image denoising and image enhancement. Then, the proposed segmentation method is applied. Feature extraction is performed by transform-based analysis. Finally, obtained features are reduced to optimal subset using Principal Component Analysis (PCA). The classification is done using the proposed Iterative Random Forest Classifier. The proposed method is compared with the conventional performance measures like accuracy, specificity, sensitivity, and dice score. The proposed method is proved to be efficient for detecting orthopedic diseases in ultrasound images than the conventional methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Xiu

Rong

Zhang

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…The ICA algorithm was proposed first in 1997 as an extension of principal component analysis by Comon [36]. In recent studies, it has been widely used in image analysis and compression [37,38], speech signal processing [39] and other aspects. Moreover, it can also be used in energy efficiency state identification.…”

Section: Introductionmentioning

confidence: 99%

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Xin,

Jiang,

Jiang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback-Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.

show abstract

Pmir: an efficient privacy-preserving medical images search in cloud-assisted scenario

Li,

Wu,

Lü

et al. 2023

Neural Comput & Applic

View full text Add to dashboard Cite

Investigation of PCA as a compression pre-processing tool for X-ray image classification

Cited by 4 publications

References 35 publications

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Pmir: an efficient privacy-preserving medical images search in cloud-assisted scenario

Contact Info

Product

Resources

About