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This paper presents a method for automatic 3D segmentation of brain tumors in MRI using optical scanning holography. Automatic segmentation of tumors from 2D slices (coronal, sagittal and axial) enables efficient 3D reconstruction of the region of interest, eliminating the human errors of manual methods. The method uses enhanced optical scanning holography with a cylindrical…

Over the past ten years, deep learning models have considerably advanced research in artificial intelligence, particularly in the segmentation of medical images. One of the key benefits of medical picture segmentation is that it allows for a more accurate analysis of anatomical data by separating only pertinent areas. Numerous studies revealed that these models could…

It is difficult to accurately segment brain MRI in the complex structures of brain tumors, blurred borders, and external variables such as noise. Much research in developing as well as developed countries show that the number of individuals suffering tumor of the brain has died as a result of the inaccurate diagnosis. The proposed article,…

Brain tumor is the abnormal growth of cancerous cells in Brain. The development of automated methods for segmenting brain tumors remains one of the most difficult tasks in medical data processing. Accurate segmentation can improve diagnosis, such as evaluating tumor volume. However, manual segmentation in magnetic resonance data is a laborious task. The main problem…

Brain tumor is such an abnormality of brain tissue that causes brain hemorrhage. Therefore, apposite detections of brain tumor, its size, and position are the foremost condition for the remedy. To obtain better performance in brain tumor and its stages detection as well as its position in MRI images, this research work proposes an advanced…

True Random Number Generators (TRNG) find applications in various fields, especially hardware security. We suggest a TRNG that exploits the intrinsic static stochasticity of Resistive Switching Random Access Memories (ReRAMs) to generate random bits. Other suggested designs use stochasticity in the switching mechanism, which requires high precision over input voltage and time. In the proposed…

Being known as an irreversible neurodegenerative disease which has no cure to date, detection and classification of Alzheimer’s disease (AD) in its early stages is significant so that the deterioration process can be slowed down. Generally, AD can be classified into three major stages, ranging from the “normal control” stage with no symptoms shown, the…

High Intensity Focused Ultrasound (HIFU) was widely used for treating tumors non-invasively. In this treatment, ultrasound is focused on the target volume inside the human body to ablate cancerous tissues and Magnetic Resonance Imaging (MRI) is mainly used to grasp the target position and to measure the temperature distributions around the target. However, MRI is…

A functional near-infrared spectroscopy (fNIRS) can be employed to investigate brain activation by measuring the absorption of near-infrared light through the intact skull. The general linear model (GLM) as a standard model for fMRI analysis has been applied to functional near-infrared spectroscopic (fNIRS) imaging analysis as well. The GLM has drawback of failure in fNIRS…

Diffusion MRI-based tractography, which is built by connecting the principal components of the estimated water diffusion pattern, is used to elucidate neuronal connectivity. However, the anatomical accuracy of the method is affected by factors such as noise and imaging misalignments. In this manuscript, we present a method to clean diffusion datasets by rotating the diffusion…

Small animal modeling is an exciting research field where human pathogenic frameworks can be replicated in a controlled environment. Accurate Imaging is in high demand when modeling abnormalities and, magnetic resonance imaging plays a vital role due to its demonstrated lowest intrusion when compared with other imaging methods. However, the required high-resolution yields low-quality images…