The constant increase in the volume of data generated by various medical modalities has generated discussions regarding the space needed for storage. Although the storage and network bandwidth costs are decreasing, medical data production grows faster, thus forcing an increase in spending. With the application of image compression and decompression techniques, such expectations and challenges overcoming can preserve all clinically relevant information. This research proposes and evaluates a method that combines an adaptive normalization for each DICOM slice and volume compression using a video CODEC. Similarity metrics results show that the best result achieved for these tests was the method combines the normalization function and the H264 using as parameters FPS 60, bitrate 120, with an image in PNG format, where SSIM and CC have the maximum value, PSNR has a high value and CR higher than competitors in the literature.
Purpose: Brain image volumetric measurements (BVM) methods have been used to quantify brain tissue volumes using magnetic resonance imaging (MRI) when investigating abnormalities. Although BVM methods are widely used, they need to be evaluated to quantify their reliability. Currently, the gold-standard reference to evaluate a BVM is usually manual labeling measurement. Manual volume labeling is a time-consuming and expensive task, but the confidence level ascribed to this method is not absolute. We describe and evaluate a biomimetic brain phantom as an alternative for the manual validation of BVM.
Methods: We printed a three-dimensional (3D) brain mold using an MRI of a three-year-old boy diagnosed with Sturge-Weber syndrome. Then we prepared three different mixtures of styrene-ethylene/butylene-styrene gel and paraffin to mimic white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The mold was filled by these three mixtures with known volumes. We scanned the brain phantom using two MRI scanners, 1.5 and 3.0 Tesla. Our suggestion is a new challenging model to evaluate the BVM which includes the measured volumes of the phantom compartments and its MRI. We investigated the performance of an automatic BVM, i.e., the expectation–maximization (EM) method, to estimate its accuracy in BVM.
Results: The automatic BVM results using the EM method showed a relative error (regarding the phantom volume) of 0.08, 0.03, and 0.13 (±0.03 uncertainty) percentages of the GM, CSF, and WM volume, respectively, which was in good agreement with the results reported using manual segmentation.
Conclusions: The phantom can be a potential quantifier for a wide range of segmentation methods.
Spatial filtering is a ubiquitously used image processing approach to reduce noise, and frequently part of image processing pipelines. The most commonly used function is the Gaussian. Recently, a generalization of the Gaussian function consistent with nonadditive statistics was proposed. Although generalized Gaussian has been used for image filtering, no study assessed its performance for medical images. Here, we present two classes of Q-Gaussian filters as noise reduction methods. We evaluated filter performance for magnetic resonance images (MRI) in cerebral, thoracic and abdominal regions. Fractal dimension estimations from images were paired with filter effectiveness. Results showed that Q-Gaussian filters have improved filtering effective gain, when compared to classical Gaussian filtering. Furthermore, it is observed filter gain dependence with fractal dimension. The obtained results suggest that the Q-Gaussian filters are better for noise reduction than classic Gaussian filter when dealing with fractal MRI or fractal noise.
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