Normalization of T2W-MRI prostate images using Rician a priori

Guillaume Lemaître; Mojdeh Rastgoo; Joan Massich; Joan C. Vilanova; Paul M. Walker; Jordi Freixenet; Anke Meyer-Baese; Fabrice Mériaudeau; Robert Martí

doi:10.1117/12.2216072

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24 March 2016 Normalization of T2W-MRI prostate images using Rician a priori

Guillaume Lemaître, Mojdeh Rastgoo , Joan Massich, Joan C. Vilanova, Paul M. Walker, Jordi Freixenet, Anke Meyer-Baese, Fabrice Mériaudeau, Robert Martí

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Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 978529 (2016) https://doi.org/10.1117/12.2216072
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Prostate cancer is reported to be the second most frequently diagnosed cancer of men in the world. In practise, diagnosis can be affected by multiple factors which reduces the chance to detect the potential lesions. In the last decades, new imaging techniques mainly based on MRI are developed in conjunction with Computer-Aided Diagnosis (CAD) systems to help radiologists for such diagnosis. CAD systems are usually designed as a sequential process consisting of four stages: pre-processing, segmentation, registration and classification. As a pre-processing, image normalization is a critical and important step of the chain in order to design a robust classifier and overcome the inter-patients intensity variations. However, little attention has been dedicated to the normalization of T2W-Magnetic Resonance Imaging (MRI) prostate images. In this paper, we propose two methods to normalize T2W-MRI prostate images: (i) based on a Rician a priori and (ii) based on a Square-Root Slope Function (SRSF) representation which does not make any assumption regarding the Probability Density Function (PDF) of the data. A comparison with the state-of-the-art methods is also provided. The normalization of the data is assessed by comparing the alignment of the patient PDFs in both qualitative and quantitative manners. In both evaluation, the normalization using Rician a priori outperforms the other state-of-the-art methods.

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Guillaume Lemaître, Mojdeh Rastgoo , Joan Massich, Joan C. Vilanova, Paul M. Walker, Jordi Freixenet, Anke Meyer-Baese, Fabrice Mériaudeau, and Robert Martí "Normalization of T2W-MRI prostate images using Rician a priori", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978529 (24 March 2016); https://doi.org/10.1117/12.2216072

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