Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 4 Articles
This paper presents a novel method for Glioblastoma (GBM) feature extraction based on Gaussian mixture model (GMM) features\nusing MRI. We addressed the task of the new features to identify GBM using T1 and T2 weighted images (T1-WI, T2-WI) and\nFluid-Attenuated Inversion Recovery (FLAIR) MR images. A pathologic area was detected using multithresholding segmentation\nwith morphological operations of MR images.Multi classifier techniques were considered to evaluate the performance of the feature\nbased scheme in terms of its capability to discriminate GBM and normal tissue.GMM features demonstrated the best performance\nby the comparative study using principal component analysis (PCA) and wavelet based features. For the T1-WI, the accuracy\nperformance was 97.05% (AUC = 92.73%) with 0.00% missed detection and 2.95% false alarm. In the T2-WI, the same accuracy\n(97.05%, AUC = 91.70%) value was achieved with 2.95% missed detection and 0.00% false alarm. In FLAIR mode the accuracy\ndecreased to 94.11% (AUC = 95.85%) with 0.00% missed detection and 5.89% false alarm. These experimental results are promising\nto enhance the characteristics of heterogeneity and hence early treatment of GBM....
We propose an algorithm for vessel extraction in retinal images. The first step consists of applying anisotropic diffusion filtering\nin the initial vessel network in order to restore disconnected vessel lines and eliminate noisy lines. In the second step, a multiscale\nline-tracking procedure allows detecting all vessels having similar dimensions at a chosen scale. Computing the individual image\nmaps requires different steps. First, a number of points are preselected using the eigenvalues of the Hessian matrix. These points are\nexpected to be near to a vessel axis. Then, for each preselected point, the response map is computed from gradient information of\nthe image at the current scale. Finally, the multiscale image map is derived after combining the individual image maps at different\nscales (sizes). Two publicly available datasets have been used to test the performance of the suggested method. The main dataset is\nthe STARE project�s dataset and the second one is the DRIVE dataset.The experimental results, applied on the STARE dataset, show\na maximum accuracy average of around 94.02%. Also, when performed on the DRIVE database, the maximum accuracy average\nreaches 91.55%....
In ultrasound imaging, clutter artifacts degrade images and may cause inaccurate diagnosis. In this paper, we apply a method called\nMorphological Component Analysis (MCA) for sparse signal separation with the objective of reducing such clutter artifacts. The\nMCA approach assumes that the two signals in the additive mix have each a sparse representation under some dictionary of atoms\n(a matrix), and separation is achieved by finding these sparse representations. In our work, an adaptive approach is used for learning\nthe dictionary from the echo data. MCA is compared to Singular Value Filtering (SVF), a Principal Component Analysis- (PCA-)\nbased filtering technique, and to a high-pass Finite Impulse Response (FIR) filter. Each filter is applied to a simulated hypoechoic\nlesion sequence, as well as experimental cardiac ultrasound data. MCA is demonstrated in both cases to outperform the FIR filter\nand obtain results comparable to the SVF method in terms of contrast-to-noise ratio (CNR). Furthermore, MCA shows a lower\nimpact on tissue sections while removing the clutter artifacts. In experimental heart data,MCA obtains in our experiments clutter\nmitigation with an average CNR improvement of 1.33 dB....
Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising technique to\ncharacterize pathology and evaluate treatment response. However, analysis of DCE-MRI data is complex and benefits\nfrom concurrent analysis of multiple kinetic models and parameters. Few software tools are currently available that\nspecifically focuses on DCE-MRI analysis with multiple kinetic models. Here, we developed ROCKETSHIP, an open-source,\nflexible and modular software for DCE-MRI analysis. ROCKETSHIP incorporates analyses with multiple kinetic models,\nincluding data-driven nested model analysis.\nResults: ROCKETSHIP was implemented using the MATLAB programming language. Robustness of the software to provide\nreliable fits using multiple kinetic models is demonstrated using simulated data. Simulations also demonstrate the utility\nof the data-driven nested model analysis. Applicability of ROCKETSHIP for both preclinical and clinical studies is shown\nusing DCE-MRI studies of the human brain and a murine tumor model.\nConclusion: A DCE-MRI software suite was implemented and tested using simulations. Its applicability to both preclinical\nand clinical datasets is shown. ROCKETSHIP was designed to be easily accessible for the beginner, but flexible enough for\nchanges or additions to be made by the advanced user as well. The availability of a flexible analysis tool will aid future\nstudies using DCE-MRI.\nA public release of ROCKETSHIP is available at https://github.com/petmri/ROCKETSHIP....
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