Ms. Bianca Maan Profile

Bianca Maan

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Proceedings Article | 14 February 2012 Paper

A new prostate segmentation approach using multispectral magnetic resonance imaging and a statistical pattern classifier

Bianca Maan, Ferdi van der Heijden, Jurgen Fütterer

Proceedings Volume 8314, 83142Q (2012) https://doi.org/10.1117/12.911194

KEYWORDS: Prostate, Image segmentation, Magnetic resonance imaging, Tissues, Feature selection, Multispectral imaging, Bladder, Image fusion, Error analysis, Prostate cancer

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Prostate segmentation is essential for calculating prostate volume, creating patient-specific prostate anatomical models and image fusion. Automatic segmentation methods are preferable because manual segmentation is timeconsuming and highly subjective. Most of the currently available segmentation methods use a priori knowledge of the prostate shape. However, there is a large variation in prostate shape between patients. Our approach uses multispectral magnetic resonance imaging (MRI) data, containing T1, T2 and proton density (PD) weighted images and the distance from the voxel to the centroid of the prostate, together with statistical pattern classifiers. We investigated the performance of a parametric and a non-parametric classification approach by applying a Baysian-quadratic and a k-nearest-neighbor classifier respectively. An annotated data set is made by manual labeling of the image. Using this data set, the classifiers are trained and evaluated. sThe following results are obtained after three experiments. Firstly, using feature selection we showed that the average segmentation error rates are lowest when combining all three images and the distance with the k-nearest-neighbor classifier. Secondly, the confusion matrix showed that the k-nearest-neighbor classifier has the sensitivity. Finally, the prostate is segmented using both classifier. The segmentation boundaries approach the prostate boundaries for most slices. However, in some slices the segmentation result contained errors near the borders of the prostate. The current results showed that segmenting the prostate using multispectral MRI data combined with a statistical classifier is a promising method.

Proceedings Article | 16 March 2011 Paper

Comparison of dynamic susceptibility contrast-MRI perfusion quantification methods in the presence of delay and dispersion

Bianca Maan, Rita Lopes Simões, Frederick J. Meijer, W. Klaas Jan Renema, Cornelis Slump

Proceedings Volume 7965, 79652C (2011) https://doi.org/10.1117/12.878177

KEYWORDS: Tissues, Signal to noise ratio, Brain, Deconvolution, Magnetic resonance imaging, Blood circulation, Neuroimaging, Blood, Error analysis, Ischemic stroke

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The perfusion of the brain is essential to maintain brain function. Stroke is an example of a decrease in blood flow and reduced perfusion. During ischemic stroke the blood flow to tissue is hampered due to a clot inside a vessel. To investigate the recovery of stroke patients, follow up studies are necessary. MRI is the preferred imaging modality for follow up because of the absence of radiation dose concerns, contrary to CT. Dynamic Susceptibility Contrast (DSC) MRI is an imaging technique used for measuring perfusion of the brain, however, is not standard applied in the clinical routine due to lack of immediate patient benefit. Several post processing algorithms are described in the literature to obtain cerebral blood flow (CBF). The quantification of CBF relies on the deconvolution of a tracer concentration-time curve in an arterial and a tissue voxel. There are several methods to obtain this deconvolution based on singular-value decomposition (SVD). This contribution describes a comparison between the different approaches as currently there is no best practice for (all) clinical relevant situations. We investigate the influence of tracer delay, dispersion and recirculation on the performance of the methods. In the presence of negative delays, the truncated SVD approach overestimates the CBF. Block-circulant and reformulated SVD are delay-independent. Due to its delay dependent behavior, the truncated SVD approach performs worse in the presence of dispersion as well. However all SVD approaches are dependent on the amount of dispersion. Moreover, we observe that the optimal truncation parameter varies when recirculation is added to noisy data, suggesting that, in practice, these methods are not immune to tracer recirculation. Finally, applying the methods to clinical data resulted in a large variability of the CBF estimates. Block-circulant SVD will work in all situations and is the method with the highest potential.

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