scholarly journals Enhancement of medical images using fuzzy logic

2021 ◽  
Vol 23 (3) ◽  
pp. 1478
Author(s):  
Yousra Ahmed Fadil ◽  
Baidaa Al-Bander ◽  
Hussein Y. Radhi
Keyword(s):  
Image Enhancement ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Visual Appearance ◽  
Fuzzy C Means Clustering ◽  
Reasonable Limit ◽  
Image Pixels ◽  
The Difference ◽  
Two Stages ◽  
Average Information

Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy c-means clustering (FCM) technique is utilized to enhance the medical images. The method of enhancement consists of two stages. The proposed algorithm conducts a cluster test on the image pixels. It then increases the difference of gray level between the diverse objects to accomplish the enhancement purpose of the medical images. The experimental results have been tested using various images. The algorithm enhanced the small target of the image to a reasonable limit and revealed favorable performance. The results of image enhancement techniques were evaluated by using terms of different criteria such as peak signal to noise ratio (PSNR), mean square error (MSE) and average information contents (AIC), showing promising performance.

scholarly journals Gradient, Texture Driven Based Dynamic-Histogram Equalization For Medical Image Enhancement

2021 ◽  
Vol 15 ◽  
pp. 303-310
Author(s):  
H. N. Vidyasaraswathi ◽  
M. C. Hanumantharaju
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Images ◽  
Similarity Index ◽  
Mapping Function ◽  
Structural Similarity ◽  
Histogram Equalization ◽  
Visual Appearance ◽  
Index Measurement ◽  
Medical Image Enhancement

In many clinical diagnostic measurements, medical images play some significant role but often suffer from various types of noise and low-luminance, which causes some notable changes in overall system accuracy with misdiagnosis rate. To improve the visual appearance of object regions in medical images, image enhancement techniques are used as potential pre-processing techniques. Due to its simplicity and easiness of implementation, histogram equalization is widely preferred in many applications. But due to its mapping function based image transformation during enhancement process affect the biomedical patterns which are essential for diagnosis. To mitigate these issues in medical images, a new method based on gradient computations and Texture Driven based Dynamic histogram equalization (GTDDHE) is accomplished to increase the visual perception. The spatial texture pattern is also included to ensure the texture retention and associated control over its variations during histogram modifications. Experimental results on MRI, CT images, eyes images from medical image datasets and quantitative analysis by PSNR, structural similarity index measurement (SSIM), information entropy (IE) and validated that the proposed method offers improved quality with maximum retention of biomedical patterns across all types of medical images.

A Fusion of Cuckoo Search and Multiscale Adaptive Smoothing Based Unsharp Masking for Image Enhancement

2019 ◽  
Vol 10 (3) ◽  
pp. 151-174 ◽  
Author(s):  
Lalit Maurya ◽  
Prasant Kumar Mahapatra ◽  
Amod Kumar
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Cuckoo Search ◽  
Histogram Equalization ◽  
Adaptive Smoothing ◽  
Visual Appearance ◽  
Search Optimization ◽  
Fusion Images ◽  
Contrast Stretching ◽  
Linear Contrast

Image enhancement means to improve the visual appearance of an image by increasing its contrast and sharpening the features. This article presents a fusion of cuckoo search optimization-based image enhancement (CS-IE) and multiscale adaptive smoothing based unsharping method (MAS-UM) for image enhancement. The fusion strategy is introduced to improve the deficiency of enhanced image that suppresses the saturation and over-sharpness artefacts in order to obtain a visually pleasing result. The ideology behind the selection of fusion images (candidate) is that one image should have high sharpness or contrast with maximum entropy and other should be high Peak Signal-to-Noise Ratio (PSNR) sharp image, to provide a better trade-off between sharpness and noise. In this article, the CS-IE and MAS-UM results are fused to combine their complementary advantages. The proposed algorithms are applied to lathe tool images and some natural standard images to verify their effectiveness. The results are compared with conventional enhancement techniques such as Histogram equalization (HE), Linear contrast stretching (LCS), Contrast-limited adaptive histogram equalization (CLAHE), standard PSO image enhancement (PSO-IE), Differential evolution image enhancement (DE-IE) and Firefly algorithm-based image enhancement (FA-IE) techniques.

scholarly journals Optimal Image Based Information Hiding with One-dimensional Chaotic Systems and Dynamic Programming

2022 ◽  
Vol 19 (1) ◽  
Author(s):  
Yinglei Song ◽  
Jia Song ◽  
Junfeng Qu
Keyword(s):  
Dynamic Programming ◽  
Information Hiding ◽  
Signal To Noise Ratio ◽  
Cover Image ◽  
Dynamic Programming Method ◽  
Stego Image ◽  
One Dimensional ◽  
Other Information ◽  
The Difference ◽  
Two Stages

Information hiding is a technology aimed at the secure hiding of important information into digital documents or media. In this paper, a new approach is proposed for the secure hiding of information into gray scale images. The hiding is performed in two stages. In the first stage, the binary bits in the sequence of information are shuffled and encoded with a set of integer keys and a system of one-dimensional logistic mappings. In the second stage, the resulting sequence is embedded into the gray values of selected pixels in the given image. A dynamic programming method is utilized to select the pixels that minimize the difference between a cover image and the corresponding stego image. Experiments show that this approach outperforms other information hiding methods by 13.1% in Peak Signal to Noise Ratio (PSNR) on average and reduces the difference between a stego image and its cover image to 0 in some cases.

scholarly journals Performance Comparison of Firefly and Cuckoo Search Algorithms in Optimal Thresholding of Cancer Cell Images

2019 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Daniel Martomanggolo Wonohadidjojo
Keyword(s):  
Cancer Cells ◽  
Image Enhancement ◽  
Cancer Cell ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Cuckoo Search ◽  
Performance Comparison ◽  
Indexing Method ◽  
Two Stages

This research presented a performance comparison of the two methods in cancer cells image processing. Each method consisted of two stages. The first stage was image enhancement using fuzzy sets. The second stage was optimal fuzzy entropy based image thresholding. In the thresholding stage, the first method used Firefly Algorithm (FA) and the second used Cuckoo Search (CS). In both methods, four performance metrics (Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM), and Feature Similarity Indexing Method (FSIM)) and variance and entropy of the images were computed to validate the comparison. The image histograms of both methods show that the distribution of red, green, and blue channel is better than the histograms of original images. In terms of the four metrics, the method that uses FA shows higher performance than CS. In terms of image variance and entropy, the method using CS shows better results than FA. These results suggest that when the performance metrics used are MSE, PSNR, MSSIM, and FSIM, the method using FA is more suitable for cancer cells image enhancement and thresholding. However, when the variance and entropy of the images are used as the performance metrics, the method using CS is more suitable for cancer cells image enhancement and thresholding. Both methods will be useful to assist in the analysis of cancer cell images by the experts in the field.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

scholarly journals Multi-Feature Guided Low-Light Image Enhancement

2021 ◽  
Vol 11 (11) ◽  
pp. 5055
Author(s):  
Hong Liang ◽  
Ankang Yu ◽  
Mingwen Shao ◽  
Yuru Tian
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Low Light ◽  
Low Contrast ◽  
Light Image ◽  
Affine Model ◽  
The Neural Network ◽  
Low Visibility ◽  
Model Robustness ◽  
Traditional Image

Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed.

Diagnosis of infertility from adenomyosis and endometriosis through entroxon based intelligent water drop back propagation neural networks

2022 ◽  
pp. 1-9
Author(s):  
P. Vijayalakshmi ◽  
K. Muthumanickam ◽  
G. Karthik ◽  
S. Sakthivel
Keyword(s):  
Clustering Algorithm ◽  
Water Drop ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Uterine Wall ◽  
Health Issues ◽  
Testing Dataset ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
The Difference

Adenomyosis is an abnormality in the uterine wall of women that adversely affects their normal life style. If not treated properly, it may lead to severe health issues. The symptoms of adenomyosis are identified from MRI images. It is a gynaecological disease that may lead to infertility. The presence of red dots in the uterus is the major symptom of adenomyosis. The difference in the extent of these red dots extracted from MRI images shows how significant the deviation from normality is. Thus, we proposed an entroxon-based bio-inspired intelligent water drop back-propagation neural network (BIWDNN) model to discover the probability of infertility being caused by adenomyosis and endometriosis. First, vital features from the images are extracted and segmented, and then they are classified using the fuzzy C-means clustering algorithm. The extracted features are then attributed and compared with a normal person’s extracted attributes. The proposed BIWDNN model is evaluated using training and testing datasets and the predictions are estimated using the testing dataset. The proposed model produces an improved diagnostic precision rate on infertility.

Bias in estimates of mb at small magnitudes

1976 ◽  
Vol 66 (6) ◽  
pp. 1887-1904
Author(s):  
J. F. Evernden ◽  
W. M. Kohler
Keyword(s):  
Signal To Noise Ratio ◽  
High Signal ◽  
Systematic Bias ◽  
Station Correction ◽  
Signal To Noise ◽  
Identification Criterion ◽  
The Difference ◽  
Operational Networks ◽  
Worldwide Network ◽  
Simple Parameter

abstract A possibly significant factor in application of an identification criterion such as MS:mb is systematic bias in mb magnitude estimates at small magnitudes due to a variety of factors. Magnitude bias is the difference in magnitude value, positive or negative, between an observed network-based magnitude value and the expected magnitude value if all stations of the network had detected the event at high signal-to-noise ratio. This paper constitutes a partial study of the general problem; it evaluates the bias effects expected from both conceptual and operational networks when using parameters for noise and signal levels and standard deviations derived from observations, and when correcting observed station mb values solely via a simple parameter station correction factor. The analysis shows that any bias effects on mb inherent in any operational or potential worldwide network are so small as to have negligible effect on use of an MS:mb discriminant.

scholarly journals An Image Encryption Algorithm with Total Diffusion

2018 ◽  
Vol 11 (1) ◽  
pp. 15-25
Author(s):  
Jakub Oravec ◽  
Ján Turán ◽  
Ľuboš Ovseník
Keyword(s):  
Image Encryption ◽  
Logistic Map ◽  
Encryption Algorithm ◽  
Diffusion Stage ◽  
Suitable Combination ◽  
Image Pixels ◽  
Encrypted Images ◽  
Two Stages ◽  
Image Encryption Algorithm ◽  
Chaotic Logistic Map

Abstract This paper proposes an image encryption algorithm which uses four scans of an image during the diffusion stage in order to achieve total diffusion between intensities of image pixels. The condition of total diffusion is fulfilled by a suitable combination of techniques of ciphertext chaining and plaintext related diffusion. The proposed encryption algorithm uses two stages which utilize chaotic logistic map for generation of pseudo-random sequences. The paper also briefly analyzes approaches described by other researchers and evaluates experimental results of the proposed solution by means of commonly used measures. Properties of our proposal regarding modifications of plain images prior to encryption or modifications of encrypted images prior to decryption are illustrated by two additional experiments. The obtained numeric results are compared with those achieved by other proposals and briefly discussed.

scholarly journals Qualidade de frutos de mangabeira de diferentes estágios de maturação tratados com CaCl2

2021 ◽  
Vol 10 (3) ◽  
pp. e21010313340
Author(s):  
Alexandre Passos Oliveira ◽  
Pryanka Thuyra Nascimento Fontes ◽  
Luiz Fernando Ganassali de Oliveira Junior
Keyword(s):  
Storage Period ◽  
Titratable Acidity ◽  
Soluble Solids ◽  
Ambient Atmosphere ◽  
Climacteric Fruit ◽  
Organoleptic Characteristics ◽  
The Difference ◽  
Solids Content ◽  
Hancornia Speciosa ◽  
Two Stages

Hancornia speciosa is a fruit tree, popularly known as mangabeiras. The mangaba, fruits of this tree, are quite appreciated for their organoleptic characteristics. Because it is a climacteric fruit, this fruit has very high perishability. The use of products that extend the useful life is necessary. Calcium chloride (CaCl2) has been shown to be an alternative in post-harvest because it promotes few changes in fruit quality and increases the storage period. Thus, the objective of this work was to evaluate quality attributes of mangaba fruits in two stages of maturation, 'Immature' and 'Mature', submitted to CaCl2 application, in four storage times (0, 2, 4 and 6 days) under ambient atmosphere. During the experiment, the loss of fresh weight, color, pH, titratable acidity, soluble solids and SS/TA ratio were evaluated. It was verified that the 'mature' fruits showed a higher acidity and soluble solids content, even with the application of CaCl2, the difference that the loss with the application of CaCl2 was smaller. Unlike '‘Immature’ and ‘Immature’ fruits with CaCl2 in which these characteristics were acquired as the experiment was conducted, in addition to presenting lower values for weight loss, pH and color.

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