Despite others have reported differences, we did not find significant variations between both registration methods for the target registration error, although application accuracy was slightly better after surface face registration. Superficial registration errors, but not the target registration error, can be routinely evaluated in the operating room. Since both errors were uncorrelated, surgeons may neglect the achievable accuracy of the procedure. The described method is recommended to assess application accuracy in the operating room.
In this paper, we propose the local complexity estimation based filtering method in wavelet domain for MRI (magnetic resonance imaging) denoising. A threshold selection methodology is proposed in which the edge and detail preservation properties for each pixel are determined by the local complexity of the input image. In the proposed filtering method, the current wavelet kernel is compared with a threshold to identify the signal- or noise-dominant pixels in a scale providing a good visual quality avoiding blurred and over smoothened processed images. We present a comparative performance analysis with different wavelets to find the optimal wavelet for MRI denoising. Numerical experiments and visual results in simulated MR images degraded with Rician noise demonstrate that the proposed algorithm consistently outperforms other denoising methods by balancing the tradeoff between noise suppression and fine detail preservation. The proposed algorithm can enhance the contrast between regions allowing the delineation of the regions of interest between different textures or tissues in the processed images. The proposed approach produces a satisfactory result in the case of real MRI denoising by balancing the detail preservation and noise removal, by enhancing the contrast between the regions of the image. Additionally, the proposed algorithm is compared with other approaches in the case of Additive White Gaussian Noise (AWGN) using standard images to demonstrate that the proposed approach does not need to be adapted specifically to Rician or AWGN noise; it is an advantage of the proposed approach in comparison with other methods. Finally, the proposed scheme is simple, efficient and feasible for MRI denoising.
Abstract. Magnetic resonance imaging is a technique for the diagnosis and classification of brain tumors. Discrete compactness is a morphological feature of two-dimensional and three-dimensional objects. This measure determines the compactness of a discretized object depending on the sum of the areas of the connected voxels and has been used for understanding the morphology of nonbrain tumors. We hypothesized that regarding brain tumors, we may improve the malignancy grade classification. We analyzed the values in 20 patients with different subtypes of primary brain tumors: astrocytoma, oligodendroglioma, and glioblastoma multiforme subdivided into the contrast-enhanced and the necrotic tumor regions. The preliminary results show an inverse relationship between the compactness value and the malignancy grade of gliomas. Astrocytomas exhibit a mean of 973 AE 14, whereas oligodendrogliomas exhibit a mean of 942 AE 21. In contrast, the contrast-enhanced region of the glioblastoma presented a mean of 919 AE 43, and the necrotic region presented a mean of 869 AE 66. However, the volume and area of the enclosing surface did not show a relationship with the malignancy grade of the gliomas. Discrete compactness appears to be a stable characteristic between primary brain tumors of different malignancy grades, because similar values were obtained from different patients with the same type of tumor.
La cirugía con paciente despierto se ha considerado el estándar de oro para tratamiento de lesiones tumorales en áreas elocuentes; y debe ser realizada mediante un manejo multidisciplinario, que involucre neurocirujanos, neuroanestesiólogos, neurolingüistas y neuropsicólogos, permitiendo realizar una resección máxima segura respetando los límites funcionales de cada paciente. Esta guía fue hecha por expertos del Instituto Nacional de Neurología y Neurocirugía y otros centros de referencia en México. Contiene criterios para selección de pacientes, pruebas auxiliares, estudios de imagen, entre otras recomendaciones para todo el personal involucrado en el proceso.
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