Adaptive Laguerre-Gauss channels for detection and search of irregularly shaped lesions

Miguel A. Lago; Aldo Badano

doi:10.1117/12.2655056

3 April 2023 Adaptive Laguerre-Gauss channels for detection and search of irregularly shaped lesions

Miguel A. Lago, Aldo Badano

Proceedings Volume 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment; 124670V (2023) https://doi.org/10.1117/12.2655056
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Model observers are one of the most common tools to evaluate signal detectability on medical images. There is a large literature of model observers applied to simple signals such as disks or Gaussian blobs. However, these signals do not represent realistic scenarios where lesion shapes are irregular and lesion location is unknown. For instance, in breast imaging, masses can be elongated, spiculated or bumpy. We study how different model observers perform on irregularly shaped signals including the Non-Pre-Whitening (NPW) and two variations of the Channelized Hotelling Observer (CHO), one with symmetrical Laguerre-Gauss channels and one with adaptive Laguerre-Gauss channels. The novel adaptive Laguerre-Gauss channels are built taking into account the shape of the lesion by modifying the distance matrix used to build channels that adopt the shape of the signal. We embedded four different signal shapes in 2D realistic backgrounds in a digital mammography image generated using the VICTRE in silico breast imaging system and studied the models’ performance for detection and search. Our results show that the adaptive channels perform better than symmetrical channels with the most irregular signal (a star and an elongated mass).

Conference Presentation

Citation Download Citation

Miguel A. Lago and Aldo Badano "Adaptive Laguerre-Gauss channels for detection and search of irregularly shaped lesions", Proc. SPIE 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment, 124670V (3 April 2023); https://doi.org/10.1117/12.2655056

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