Image Standardization in PACS

Piętka, Ewa

doi:10.1016/b978-012077790-7/50056-4

Cited by 5 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the number of blocks per image ranged from one (for a grid length equal to 1) to a hundred (for a grid length equal to 10). A histogram, defined as the frequency of occurrence of each brightness value from 0 to 255 [ 25 ], was developed for every block. Next, to reduce computing time, every consecutive five values in the generated histogram were averaged.…”

Section: Methodsmentioning

confidence: 99%

“…Classic machine learning usually uses tabular data to predict a given end-point [ 23 , 24 ]. A tabular data set for an image could be its histogram, defined as the frequency of occurrence of each brightness value from 0 to 255 [ 25 ]. Next, a classic machine learning algorithm could use the histogram data to predict the image quality.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality

Nazar,

Daniłowicz-Szymanowicz

2024

Life

View full text Add to dashboard Cite

High-quality echocardiogram images are the cornerstone of accurate and reliable measurements of the heart. Therefore, this study aimed to develop, validate and compare machine learning and deep learning algorithms for accurate and automated assessment of transthoracic echocardiogram image quality. In total, 4090 single-frame two-dimensional transthoracic echocardiogram images were used from apical 4-chamber, apical 2-chamber and parasternal long-axis views sampled from 3530 adult patients. The data were extracted from CAMUS and Unity Imaging open-source datasets. For every raw image, additional grayscale block histograms were developed. For block histogram datasets, six classic machine learning algorithms were tested. Moreover, convolutional neural networks based on the pre-trained EfficientNetB4 architecture were developed for raw image datasets. Classic machine learning algorithms predicted image quality with 0.74 to 0.92 accuracy (AUC 0.81 to 0.96), whereas convolutional neural networks achieved between 0.74 and 0.89 prediction accuracy (AUC 0.79 to 0.95). Both approaches are accurate methods of echocardiogram image quality assessment. Moreover, this study is a proof of concept of a novel method of training classic machine learning algorithms on block histograms calculated from raw images. Automated echocardiogram image quality assessment methods may provide additional relevant information to the echocardiographer in daily clinical practice and improve reliability in clinical decision making.

show abstract

Section: Methodsmentioning

confidence: 99%