TY - GEN
T1 - Conversion of Pixel to Millimeter in Ultrasound Images
T2 - 20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB)
AU - Alzubaidi, Mahmood
AU - Shah, Uzair
AU - Shah, Hurmat
AU - Househ, Mowafa
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Ultrasound imaging is integral to prenatal care, facilitating the identification of fetal landmarks. However, the accuracy of fetal structure measurements largely hinges on the precise conversion of pixel values to millimeters (mm), an essential step in developing artificial intelligence (AI) applications capable of automating fetal measurements. This paper presents a novel pixel intensity-based technique for converting pixel values to millimeters in fetal ultrasound images. Utilizing a publicly available dataset, we have enhanced its value by adding new labels that provide pixel size in millimeters, thereby creating an augmented resource. Our augmented dataset, comprising 2835 fetal head images, represents the largest and most diverse collection of its kind in the literature. The mean pixel size label is 0.144 mm, ranging from a minimum of 0.06 mm to a maximum of 0.33 mm. The proposed technique proves to be accurate and efficient, thus serving as a substantial resource for future AI-based fetal measurement applications in clinical settings.
AB - Ultrasound imaging is integral to prenatal care, facilitating the identification of fetal landmarks. However, the accuracy of fetal structure measurements largely hinges on the precise conversion of pixel values to millimeters (mm), an essential step in developing artificial intelligence (AI) applications capable of automating fetal measurements. This paper presents a novel pixel intensity-based technique for converting pixel values to millimeters in fetal ultrasound images. Utilizing a publicly available dataset, we have enhanced its value by adding new labels that provide pixel size in millimeters, thereby creating an augmented resource. Our augmented dataset, comprising 2835 fetal head images, represents the largest and most diverse collection of its kind in the literature. The mean pixel size label is 0.144 mm, ranging from a minimum of 0.06 mm to a maximum of 0.33 mm. The proposed technique proves to be accurate and efficient, thus serving as a substantial resource for future AI-based fetal measurement applications in clinical settings.
KW - Fetal head
KW - Image processing
KW - Ultrasound images
KW - Pixel to millimeter
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=hbku_researchportal&SrcAuth=WosAPI&KeyUT=WOS:001090563700034&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1109/CIBCB56990.2023.10264909
DO - 10.1109/CIBCB56990.2023.10264909
M3 - Conference contribution
T3 - CIBCB 2023 - 20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
SP - 270
EP - 275
BT - 2023 Ieee Conference On Computational Intelligence In Bioinformatics And Computational Biology, Cibcb
PB - IEEE
Y2 - 29 August 2023 through 31 August 2023
ER -