Nonparametric de‐noising filter optimization using structure‐based microscopic image classification

Abstract

The Local Polynomial Approximation (LPA) is a nonparametric filter that performs pixel-wise polynomial fit on a certain neighborhood. This filter can be supported by the Intersection of Confidence Interval rule (ICI) as an adaptation algorithm to identify the most suited neighborhood at which the polynomial assumptions provide superior fit for the observations. However, the LPA-ICI is considered to be a near-optimal de-noising filter. Moreover, the ICI rule has several parameters that affect its performance. The current study applied an optimization algorithm, namely the Particle swarm optimization (PSO) to determine the optimal ICI parameter values for microscopic images de-noising. As the ICI parameters are image's structure based, bag-of-features classifier is used to classify the images based on their structure into different classes. Afterward, a generated optimal ICI parameters' table was created using the LPA-ICI-PSO for further direct use without optimization. This table included the optimal ICI parameters based on the image structure. Based on the image category, the generated table can be used to attain the suitable ICI optimal parameters without using PSO. This guarantees less computational time along with the optimal de-noising compared to the LPA-ICI as established by the performance metrics. The experimental results established the superiority of the proposed LPA-ICI-PSO over the classical LPA-ICI filter.

A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains

“…With noise added, it showcases good noise resistance and stability, but whether it can be applied to non-microscope images is still unknown. To improve the denoising ability of the algorithm, Samsad Beagum et al [ 14 ] proposed an automation technology LPA-ICI-PSO by studying the local polynomial approximation (LPA) filter supported by the intersection confidence interval (ICI) rule (LPA-ICI) and combining it with particle swarm optimization (PSO). This guarantees less computational time along with optimal denoising compared to the LPA-ICI as established by the performance metrics.…”

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

“…where I GT is GT, I S is the ROI, T N , T P , F N and F P are the related measures [51][52][53][54].…”

Histopathological Image Segmentation Using Modified Kernel-Based Fuzzy C-Means and Edge Bridge and Fill Technique

“…For experimental simulation, MATLAB (version 2017a) was employed on a PC with 3.2 GHz with i5 processor [2]. In order to estimate the efficiency of the proposed algorithm, the performance of the proposed method was compared with the diffusion-weighted approach [22] and DCNN [13] on a database: TMAD.…”