Normalization of T2W-MRI Prostate Images using Rician a priori

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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Communication dans un congrès
SPIE Medical Imaging, Feb 2016, San Diego, United States
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01265774
Contributeur : Guillaume Lemaitre <>
Soumis le : vendredi 5 février 2016 - 18:04:20
Dernière modification le : mardi 9 février 2016 - 01:02:20
Document(s) archivé(s) le : samedi 12 novembre 2016 - 00:01:38

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  • HAL Id : hal-01265774, version 1

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Guillaume Lemaitre, Mojdeh Rastgoo, Joan Massich, Joan Vilanova, Paul Walker, et al.. Normalization of T2W-MRI Prostate Images using Rician a priori. SPIE Medical Imaging, Feb 2016, San Diego, United States. <hal-01265774>

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