Adaptive Threshold Masking An Extended Bias Correction Algorithm for Heavily Biased MR Images
نویسندگان
چکیده
Image bias is a usual phenomenon in MR imaging when using surface coils. It complicates the interpretation as well as the algorithmic postprocessing of such data. We introduce a bias correction algorithm based on homomorphic unsharp masking (HUM) that is applicable on a broad range of image types (as long as foreand background is separable), simple, fast and requires only minimal user interaction. The results of this new algorithm are superior to HUM, especially with regards to feature separability.
منابع مشابه
روشی نوین در کاهش نوفه رایسین از مقدار بزرگی سیگنال دیفیوژن در تصویربرداری تشدید مغناطیسی (MRI)
The true MR signal intensity extracted from noisy MR magnitude images is biased with the Rician noise caused by noise rectification in the magnitude calculation for low intensity pixels. This noise is more problematic when a quantitative analysis is performed based on the magnitude images with low SNR(<3.0). In such cases, the received signal for both the real and imaginary components will fluc...
متن کاملQuantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation
Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), ...
متن کاملIllumination Correction on Biomedical Images
RF-Inhomogeneity Correction (aka bias) artifact is an important research field in Magnetic Resonance Imaging (MRI). Bias corrupts MR images altering their illumination even though they are acquired with the most recent scanners. Homomorphic Unsharp Masking (HUM) is a filtering technique aimed at correcting illumination inhomogeneity, but it produces a halo around the edges as a side effect. In ...
متن کاملOptimization of Brain Tumor MR Image Classification Accuracy Using Optimal Threshold, PCA and Training ANFIS with Different Repetitions
Background: One of the leading causes of death is brain tumors. Accurate tumor classification leads to appropriate decision making and providing the most efficient treatment to the patients. This study aims to optimize brain tumor MR images classification accuracy using optimal threshold, PCA and training Adaptive Neuro Fuzzy Inference System (ANFIS) with different repetitions.Material and Meth...
متن کاملOptimization of the brain tumor MR images classification accuracy using the optimal threshold, PCA and training ANFIS with different repetitions
Introduction: One of the leading causes of death among people is brain tumors. Accurate tumor classification leads to appropriate decision-making and providing the most efficient treatment to the patients. This study aims to optimize of the brain tumor MR images classification accuracy using the optimal threshold, PCA and training Adaptive Neuro Fuzzy Inference System (ANFIS) w...
متن کامل