The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer

Gezheng Wen; Mia K. Markey

doi:10.1117/12.2081956

17 March 2015 The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer

Gezheng Wen, Mia K. Markey

Author Affiliations +

Proceedings Volume 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment; 94160Y (2015) https://doi.org/10.1117/12.2081956
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

It is resource-intensive to conduct human studies for task-based assessment of medical image quality and system optimization. Thus, numerical model observers have been developed as a surrogate for human observers. The Hotelling observer (HO) is the optimal linear observer for signal-detection tasks, but the high dimensionality of imaging data results in a heavy computational burden. Channelization is often used to approximate the HO through a dimensionality reduction step, but how to produce channelized images without losing significant image information remains a key challenge. Kernel local Fisher discriminant analysis (KLFDA) uses kernel techniques to perform supervised dimensionality reduction, which finds an embedding transformation that maximizes betweenclass separability and preserves within-class local structure in the low-dimensional manifold. It is powerful for classification tasks, especially when the distribution of a class is multimodal. Such multimodality could be observed in many practical clinical tasks. For example, primary and metastatic lesions may both appear in medical imaging studies, but the distributions of their typical characteristics (e.g., size) may be very different. In this study, we propose to use KLFDA as a novel channelization method. The dimension of the embedded manifold (i.e., the result of KLFDA) is a counterpart to the number of channels in the state-of-art linear channelization. We present a simulation study to demonstrate the potential usefulness of KLFDA for building the channelized HOs (CHOs) and generating reliable decision statistics for clinical tasks. We show that the performance of the CHO with KLFDA channels is comparable to that of the benchmark CHOs.

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Gezheng Wen and Mia K. Markey "The use of kernel local Fisher discriminant analysis for the channelization of the Hotelling model observer", Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94160Y (17 March 2015); https://doi.org/10.1117/12.2081956

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KEYWORDS

Medical imaging

Statistical analysis

Computer simulations

Data modeling

Performance modeling

Signal detection

Chemical elements

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