Prediction-based coding with rate control for lossless region of interest in pathology imaging
Bartrina-Rapesta, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Hernández Cabronero, Miguel (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Sanchez, Victor (University of Warwick. Department of Computer Science)
Serra-Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Jamshidi, Pouya (Northwestern University. Department of Pathology)
Castellani, J. (Northwestern University. Department of Pathology)

Date:	2024
Abstract:	Online collaborative tools for medical diagnosis produced from digital pathology images have experimented an increase in demand in recent years. Due to the large sizes of pathology images, rate control (RC) techniques that allow an accurate control of compressed file sizes are critical to meet existing bandwidth restrictions while maximizing retrieved image quality. Recently, some RC contributions to Region of Interest (RoI) coding for pathology imaging have been presented. These encode the RoI without loss and the background with some loss, and focus on providing high RC accuracy for the background area. However, none of these RC contributions deal efficiently with arbitrary RoI shapes, which hinders the accuracy of background definition and rate control. This manuscript presents a novel coding system based on prediction with a novel RC algorithm for RoI coding that allows arbitrary RoIs shapes. Compared to other methods of the state of the art, our proposed algorithm significantly improves upon their RC accuracy, while reducing the compressed data rate for the RoI by 30%. Furthermore, it offers higher quality in the reconstructed background areas, which has been linked to better clinical performance by expert pathologists. Finally, the proposed method also allows lossless compression of both the RoI and the background, producing data volumes 14% lower than coding techniques included in DICOM, such as HEVC and JPEG-LS.
Grants:	Agencia Estatal de Investigación RTI2018-095287-B-I00 Agencia Estatal de Investigación PID2021-125258OB-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-00643 Agència de Gestió d'Ajuts Universitaris i de Recerca 2018/BP-00008 European Commission 801370
Note:	Altres ajuts: acords transformatius de la UAB
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Digital pathology images ; Region of interest coding ; Rate control
Published in:	Signal Processing: Image Communication, Vol. 123 (April 2024) , art. 117087, ISSN 0923-5965

DOI: 10.1016/j.image.2023.117087

13 p, 7.5 MB

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