TY - GEN
T1 - A COGNITIVE PERSPECTIVE ON SUBJECTIVE AND OBJECTIVE DIAGNOSTIC IMAGE QUALITY MODELS
AU - Caviedes, J. E.
AU - Patel, B. K.
AU - Gutzwiller, R.
AU - Li, B.
AU - Bhat, R.
AU - Chhabra, S.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We present a new perspective on diagnostic image quality (DIQ) for mammography (MG) aimed at future clinical decision support applications. We posit that DIQ is based on three interdependent criteria: adequacy, visual quality, and interpretability. Adequacy is addressed by imaging protocols and technique, while visual quality and interpretability ensures that human perception will be able to detect diagnostically relevant content, and both enable cognitive interpretation at a conceptual level. We have implemented a visual interface for radiologists to enter annotated scores for the three criteria and overall DIQ. Score annotations may be used to identify relevant computable image features by text mining. The image features allow building DIQ metrics potentially useful during image acquisition and reading. We have conducted preliminary research on a set of relevant image features and present initial results on their discriminative power and emerging challenges. We also discuss next steps and strategies for future research.
AB - We present a new perspective on diagnostic image quality (DIQ) for mammography (MG) aimed at future clinical decision support applications. We posit that DIQ is based on three interdependent criteria: adequacy, visual quality, and interpretability. Adequacy is addressed by imaging protocols and technique, while visual quality and interpretability ensures that human perception will be able to detect diagnostically relevant content, and both enable cognitive interpretation at a conceptual level. We have implemented a visual interface for radiologists to enter annotated scores for the three criteria and overall DIQ. Score annotations may be used to identify relevant computable image features by text mining. The image features allow building DIQ metrics potentially useful during image acquisition and reading. We have conducted preliminary research on a set of relevant image features and present initial results on their discriminative power and emerging challenges. We also discuss next steps and strategies for future research.
KW - Diagnostic image quality
KW - image quality model
KW - mammography image quality
UR - http://www.scopus.com/inward/record.url?scp=85146717026&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146717026&partnerID=8YFLogxK
U2 - 10.1109/ICIP46576.2022.9897481
DO - 10.1109/ICIP46576.2022.9897481
M3 - Conference contribution
AN - SCOPUS:85146717026
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 246
EP - 250
BT - 2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PB - IEEE Computer Society
T2 - 29th IEEE International Conference on Image Processing, ICIP 2022
Y2 - 16 October 2022 through 19 October 2022
ER -