Medical image compression using block-based PCA algorithm

In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF) allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected.

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“…The PCA algorithm has been described in [ 22 , 23 ]. Given a matrix X with dimension

, the first step consists in subtracting the mean of the elements in a particular row from each element of that row.…”

Section: Methodology and System Descriptionmentioning

confidence: 99%

A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

Moriano

Rodríguez

Martín

et al. 2016

Sensors

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“…In their research, a simple mean thresholding for blocks of pixels were used to segment the breast tissues. The resulting ROI were either oversegmented or undersegmented and the use of blockby-block PCA algorithm in their work, as proven by our previous research [16], produces very poor image quality. PCA was also employed in study performed in a region-based colour images compression [17] but it was used only to determine the spatio-chromatic information of a colour image so that the existing spatial correlations between the transform coefficients are removed.…”

Section: Anastassopoulos and Skodrasmentioning

confidence: 97%

A Region-based Compression Technique for Medical Image Compression using Principal Component Analysis (PCA)

Lim¹,

Manap²

2022

IJACSA

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Region-based compressiontechnique is particularly useful for radiological archiving system as it allows diagnostically important regions to be compressed with near lossless quality while the non-diagnostically important regions (NROI) to be compressed at lossy quality. In this paper, we present a region-based compression technique tailored for MRI brain scans. In the proposed technique termed as automated arbitrary PCA (AAPCA), an automatic segmentation based on brain symmetrical property is used to separate the ROI from the background. The arbitrary-shape ROI is then compressed by block-to-row PCA algorithm (BTRPCA) based on a factorization approach. The ROI is optimally compressed with lower compression rate while the NROI is compressed with higher compression rate. The proposed technique achieves satisfactory segmentation performance. The subjective and objective evaluation performed confirmed that the proposed technique achieves better performance metrics (PSNR and CoC) and higher overall compression rate. The experimental results also demonstrated that the proposed technique is more superior to various state-of-the-art compression methods.

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“…The Principal Component Analysis (PCA) technique is classified among the excellent unsupervised dimensionality reduction techniques. Its major aim is to transform image data or features from a higher dimensional space to a lower dimensional space [18][19][20][21][22].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Image compression of surface defects of the hot-rolled steel strip using Principal Component Analysis

Boudiaf

Boubendira

Harrar

et al. 2019

Matériaux & Techniques

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The quality control of steel products by human vision remains tedious, fatiguing, somewhat fast, rather robust, sketchy, dangerous or impossible. For these reasons, the use of the artificial vision in the world of quality control has become more than necessary. However, these images are often large in terms of quantity and size, which becomes a problem in quality control centers, where engineers are unable to store these images. For this, efficient compression techniques are necessary for archiving and transmitting the images. The reduction in file size allows more images to be stored in a disk or memory space. The present paper proposes an effective technique for redundancy extraction using the Principal Component Analysis (PCA) approach. Furthermore, it aims to study the effects of the number of eigenvectors employed in the PCA compression technique on the quality of the compressed image. The results revealed that using only 25% of the eigenvectors provide very similar compressed images compared to the original ones, in terms of quality. These images are characterized by high compression ratios and a small storage space.

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Medical image compression using block-based PCA algorithm

Cited by 19 publications

References 3 publications

A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

A Region-based Compression Technique for Medical Image Compression using Principal Component Analysis (PCA)

Image compression of surface defects of the hot-rolled steel strip using Principal Component Analysis

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