Fuzzy random impulse noise reduction method

A new framework for reducing impulse noise from digital color images is presented, in which a fuzzy detection phase is followed by an iterative fuzzy filtering technique. We call this filter the fuzzy two-step color filter. The fuzzy detection method is mainly based on the calculation of fuzzy gradient values and on fuzzy reasoning. This phase determines three separate membership functions that are passed to the filtering step. These membership functions will be used as a representation of the fuzzy set impulse noise (one function for each color component). Our proposed new fuzzy method is especially developed for reducing impulse noise from color images while preserving details and texture. Experiments show that the proposed filter can be used for efficient removal of impulse noise from color images without distorting the useful information in the image

Fuzzy Two-Step Filter for Impulse Noise Reduction From Color Images

A new fuzzy filter is presented for the reduction of additive noise for digital color images. The filter consists of two subfilters. The first subfilter computes fuzzy distances between the color components of the central pixel and its neighborhood. These distances determine in what degree each component should be corrected. All performed corrections preserve the color component distances. The goal of the second subfilter is to correct the pixels where the color components differences are corrupted so much that they appear as outliers in comparison to their environment. Experimental results show the feasibility of the proposed approach. We compare with other noise reduction methods by numerical measures and visual observations. We also illustrate the performance of the proposed method as preprocessing step for edge detection

A Fuzzy Noise Reduction Method for Color Images

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

/pdf/automatic-screening-and-classification-of-diabetic-rdy6hen1ek.pdf

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing

Deep learning techniques such as convolutional neural network and capsule network have attained good results in iris recognition. However, due to the influence of eyelashes, skin, and background noises, the model often needs many iterations to retrieve informative iris patterns. Also because of some nonideal situations, such as reflection of glasses and facula on the eyeball, it is hard to detect the boundary of pupil and iris perfectly. Under such a circumstance, discarding the rest parts beyond the boundary may cause losing useful information. Hence, we use Gaussian, triangular fuzzy average, and triangular fuzzy median smoothing filters to preprocess the image by fuzzifying the region beyond the boundary to improve the signal-to-noise ratios. We applied the enhanced images through fuzzy operations to train deep learning methods, which speeds up the process of convergence and also increases the recognition accuracy rate. The saliency maps show that fuzzified image filters make the images more informative for deep learning. The proposed fuzzy operation of images may be a robust technique in many other deep-learning applications of image processing, analysis, and prediction.

Fuzzified Image Enhancement for Deep Learning in Iris Recognition

In this paper, four fuzzy filters for filtering images contaminated with random, impulse, and sum of random and impulse noises are introduced In each of these four fuzzy filters, the output pixel of a filtered image at the center of a moving window area is defined as a normalized sum of weighted input pixels within the window Simulation results indicate that some of these fuzzy filters show improvement over the standard median and moving average filters in reducing these three noises

Fuzzy filters for image filtering

Two nonlinear filters, called fuzzy median filters are introduced. Preliminary simulation results based on a periodic rectangular signal contaminated with random noise, impulse noise, and addition of random and impulse noises indicate that the two fuzzy median filters are suitable for the edge preservation of a signal. >

http://ieeexplore.ieee.org/iel2/1096/8013/00343196.pdf

Median filtering using fuzzy concept

Coronary heart disease (CHD) is the leading cause of death and disease burden in China and world-wide. The accurate screening for CHD can be of great value to guiding and facilitating the treatment. Traditional methods, such as computed tomography (CT) and coronary computed tomography angiography (CCTA), are costly and harmful to humans. To address these issues, we proposed a multi-modal data fusion method of ultrasonic (US) images and electronic medical records (EMRs) named M-US-EMRs model to automatically screen CHD. Comparing to traditional methods, this model features cheap, harmless to humans, easy to implement and independent of doctors. The experiment result shows that, M-US-EMRs model reached an overall classification AUC of 79.19 $$\%$$ . Furthermore, the interpretable models that we proposed lead to an increased trust from the people who use them. Our project developed effective tools with good performance for CHD screening among a Chinese population that will help to improve the primary prevention and management of cardiovascular disease. Our method lays the groundwork for using automated interpretation to support serial patient tracking and scalable analysis of millions of ultrasonic images and EMRs archived within healthcare systems. 

Y. Cai

Papers

Fuzzy filters for image filtering

Median filtering using fuzzy concept

M-US-EMRs: A Multi-modal Data Fusion Method of Ultrasonic Images and Electronic Medical Records Used for Screening of Coronary Heart Disease