Use of multispectral magnetic resonance imaging has received a great interest for prostate cancer localization in research and clinical studies. Manual extraction of prostate tumors from multispectral magnetic resonance imaging is inefficient and subjective, while automated segmentation is objective and reproducible. For supervised, automated segmentation approaches, learning is essential to obtain the information from training dataset. However, in this procedure, all patients are assumed to have similar properties for the tumor and normal tissues, and the segmentation performance suffers since the variations across patients are ignored. To conquer this difficulty, we propose a new iterative normalization method based on relative intensity values of tumor and normal tissues to normalize multispectral magnetic resonance images and improve segmentation performance. The idea of relative intensity mimics the manual segmentation performed by human readers, who compare the contrast between regions without knowing the actual intensity values. We compare the segmentation performance of the proposed method with that of z-score normalization followed by support vector machine, local active contours, and fuzzy Markov random field. Our experimental results demonstrate that our method outperforms the three other state-of-the-art algorithms, and was found to have specificity of 0.73, sensitivity of 0.69, and accuracy of 0.79, significantly better than alternative methods.
Phosphorescence lifetime measurement based on a frequency domain approach is used to estimate oxygen tension in
large retinal blood vessels. The classical least squares (LS) estimation was initially used to determine oxygen tension
indirectly from intermediate variables. A spatial regularized least squares (RLS) method was later proposed to reduce the
high variance of oxygen tension estimated by LS method. In this paper, we provide a solution using a modified RLS
(MRLS) approach that utilizes prior knowledge about retinal vessels oxygenation based on expected oxygen tension
values in retinal arteries and veins. The performance of MRLS method was evaluated in simulated and experimental
data by determining the bias, variance, and mean absolute error (MAE) of oxygen tension measurements and comparing
these parameters with those derived with the use of LS and RLS methods.
KEYWORDS: Signal to noise ratio, Principal component analysis, Independent component analysis, Computer simulations, Monte Carlo methods, Data processing, Reconstruction algorithms, Neuroimaging, Functional magnetic resonance imaging, Brain
We propose a new method for analyzing fMRI (functional magnetic resonance imaging) data based on locally
linear embeddings (LLE). The LLE method is useful for analyzing data when there is a local structure
intrinsic to the measurements allowing for reconstruction of measurements from its neighboring points only.
We develop a method to extract the underlying temporal signal in fMRI experiments based on LLE.
Simulations show that improved results can be obtained under certain conditions when compared to
traditional methods such as the principal component analysis (PCA) and independent component analysis