Parameter Optimization for Local Polynomial Approximation based Intersection Confidence Interval Filter Using Genetic Algorithm: An Application for Brain MRI Image De-Noising

Abstract

Magnetic resonance imaging (MRI) is extensively exploited for more accurate pathological changes as well as diagnosis. Conversely, MRI suffers from various shortcomings such as ambient noise from the environment, acquisition noise from the equipment, the presence of background tissue, breathing motion, body fat, etc. Consequently, noise reduction is critical as diverse types of the generated noise limit the

Meta-KANSEI Modeling with Valence-Arousal fMRI Dataset of Brain

“…The results showed the model feasibility and the performed effectiveness for the experimental methods. By comparing with our previous studies [44][45][46], it can be concluded that:…”

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

“…Brain abnormality evaluation involves in the implementation of dedicated hardware systems to acquire essential brain information based on brain-signals and brain-images in a controlled environment. Earlier research confirms that imaging based activities offer more apparent information on brain irregularity compared to the signal supported assessment [1][2][3]. Mapping of brain signal along with the brain image is also a flourishing research field [4].…”

“…The existing works on brain MRI confirms the availability of traditional machine learning and deep learning based approaches implemented and evaluated on user defined and benchmark datasets [11,12]. The aim of this research work was to develop a Computerized Disease inspection Tool (CDT) using a Hybrid Image Processing (HIP) procedure recently discussed in the literature [1][2][3]. HIP was developed by integrating a pre-processing practice based on the Social-Group-Optimization (SGO) assisted Shannon's thresholding and a post-processing based on Active-Contour, Marker-Controlled-Watershed and Seed-Region-Growing procedures to segment the suspicious region.…”

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

“…Chaddad (2015) implemented Gaussian mixture model technique to support automated feature extraction in brain tumor extracted from MRI [17]. Dey et al (2015) applied genetic algorithm tuned interval filter to remove noise in brain MRI [6]. implemented a detailed evaluation of the brain MRI with various segmentation approaches [9].…”

“…Normally, the condition of the brain can be evaluated based on a single-channel and multichannel EEG signals recorded using the external electrodes or the brain images recorded with MRI and CT imaging approaches [4][5][6][7]. Previous study confirms that information existing in brain image is essential to categorize and localize the abnormality compared to the brain signals [8].…”