Intensity non-standardness affects computer recognition of anatomical structures

Ulas Bagci; Jayaram K. Udupa; Xinjian Chen

doi:10.1117/12.877779

1 March 2011 Intensity non-standardness affects computer recognition of anatomical structures

Ulas Bagci, Jayaram K. Udupa, Xinjian Chen

Proceedings Volume 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling; 79642M (2011) https://doi.org/10.1117/12.877779
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Since MR image intensities do not possess a tissue specific numeric meaning, even in images acquired for the same subject, on the same scanner, for the same body region, by using the same pulse sequence, it is important to transform the image scale into a standard intensity scale so that, for the same body region, intensities are similar. The lack of a standard image intensity scale in MRI leads to many difficulties in tissue characterizability, image display, and analysis, including image segmentation and registration. The influence of standardization on these tasks has been documented well; however, how intensity non-standardness may affect the automatic recognition of anatomical structures for image segmentation has not been studied. Motivated from the study that we previously presented in SPIE Medical Imaging Conference 2010,^{1, 2} in this study, we analyze the effects of intensity standardization on anatomical object recognition. A set of 31 scenarios of multiple objects from the ankle complex included in the model, plus seven different realistic levels of non-standardness introduced are considered for evaluation. The experimental results imply that, intensity variation among scenes in an ensemble - a particular characteristic of the behavior of non-standardness - degrades object recognition performance.

Citation Download Citation

Ulas Bagci, Jayaram K. Udupa, and Xinjian Chen "Intensity non-standardness affects computer recognition of anatomical structures", Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 79642M (1 March 2011); https://doi.org/10.1117/12.877779

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