scholarly journals MRI Images Enhancement Using Genetic Programming Based Hybrid Noise Removal Filter Approach

2018 ◽  
Vol 14 (6) ◽  
pp. 867-873 ◽  
Author(s):  
Sajid Ullah Khan ◽  
Najeeb Ullah ◽  
Imran Ahmed ◽  
Wang Yin Chai ◽  
Amjad Khan
Keyword(s):  
Genetic Programming ◽  
Noise Removal ◽  
Filter Approach

DE-Based RBFNs for Classification With Special Attention to Noise Removal and Irrelevant Features

2018 ◽  
pp. 218-243
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Sarat Chandra Nayak
Keyword(s):  
Feature Selection ◽  
Radial Basis Function ◽  
Basis Function ◽  
Missing Values ◽  
Information Gain ◽  
Noise Removal ◽  
Network Efficiency ◽  
Novel Approach ◽  
Radial Basis ◽  
Filter Approach

This chapter presents a novel approach for classification of dataset by suitably tuning the parameters of radial basis function networks with an additional cost of feature selection. Inputting optimal and relevant set of features to a radial basis function may greatly enhance the network efficiency (in terms of accuracy) at the same time compact its size. In this chapter, the authors use information gain theory (a kind of filter approach) for reducing the features and differential evolution for tuning center and spread of radial basis functions. Different feature selection methods, handling missing values and removal of inconsistency to improve the classification accuracy of the proposed model are emphasized. The proposed approach is validated with a few benchmarking highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository. The experimental study is encouraging to pursue further extensive research in highly skewed data.

An Improved Denoising of Medical Images Based on Hybrid Filter Approach and Assess Quality Metrics

2020 ◽  
Vol 44 ◽  
pp. 27-36
Author(s):  
Mopidevi Suneetha ◽  
Mopidevi Subbarao
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Visual Quality ◽  
Noise Removal ◽  
Main Task ◽  
Large Numbers ◽  
Filter Approach ◽  
Assess Quality ◽  
Structural Preservation ◽  
Image Quality Index

Degradation of images and segmentation are the two most demanding fields for medical image processing, particularly when explicitly applied. The involvement of noise not only deteriorates the visual quality but also the precision of the segmentation which is vital to the medical diagnosis process of development. The complicated and monotonous main task is to manually denoise medical images such as CT, ultrasound and large numbers of clinical routine MRI images. The medical image must be denoised automatically. The proposed approach is associated with less complexity, this follows from the fact that, the design of system and time for optimization. Results show their efficacy for noise removal in medical ultrasound and MRI images .The final results of the proposed scheme in terms of noise reduction and structural preservation are excellent. However the proposed scheme is compared with existing methods and the performance of the proposed method in terms of visual quality, image quality index, peak SNR and PSNR is shown to be superior to existing methods.

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