Differential Gray-levels Histogram Research Articles

Combining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID-19 classification.

In this article, COVID‐19 detection and classification framework based on anopheles search optimized AlexNet convolutional deep neural network for random forest classifier is implemented. Here, the COVID‐19 dataset is taken from Joseph Paul Cohen database. Then, the input images are preprocessed with the help of fuzzy gray level difference histogram equalization technique (FGLHE) and fuzzy stacking technique for color enhancement and noise elimination in the input images. The FGLHE technique and fuzzy stacking technique are combined together and forms into stacked dataset image. This stacked dataset are trained with AlexNet convolutional deep neural network model and the feature packages acquired via the models are processed by the anopheles search algorithm. Subsequently, the efficient features are combined and delivered to random forest (RF) classifier. The proposed approach is implemented in MATLAB. The proposed ADCNN‐ASA‐RFC provides 91.66%, 69.13%, 34.86%, and 70.13% higher accuracy, 79.13%, 60.33%, and 63.34% higher specificity and 77.13%, 58.45%, 25.86%, and 55.33%, higher sensitivity compared with existing algorithms. At last, the simulation outcomes demonstrate that the proposed system can be able to find the optimal solutions efficiently and accurately with COVID‐19 diagnosis.

Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement.

Contrast enhancement methods are used to reduce image noise and increase the contrast of structures of interest. In medical images where the distinction between normal and abnormal tissue is subtle, accurate interpretation may become difficult if noise levels are relatively high. To provide accurate interpretation and clearer image for the observer with reduced noise levels "a novel adaptive fuzzy gray level difference histogram equalization algorithm" is proposed. At first, gray level difference of an input image is calculated using the binary similar patterns. Then, the gray level differences are fuzzified in order to deal the uncertainties present in the input image. Following the fuzzification, fuzzy gray level difference clip limit is computed to control the insignificant contrast enhancement. Finally, a fuzzy clipped histogram is equalized to obtain the contrast-enhanced MR medical image. The proposed algorithm is analysed both visually and analytically to calculate its performance against the other existing algorithms. Visual and analytical results on various test images affirm that the proposed algorithm outperforms all other existing algorithms and provide a clear path to analyse the fine details and infected portions effectively.

Use of a novel set of features based on texture anisotropy for identification of liver steatosis from ultrasound images: a simple method

Detection of fatty liver disease (steatosis) from the ultra sound (US) images using pattern recognition techniques is attempted by many authors. Different pre-processing methods, feature extraction, feature selection and classification models are reported in the literature. The present work uses a hitherto unexplored property of liver texture. A careful visual observation reveals that the liver texture is anisotropic. A novel set of features exploiting this anisotropy is explicitly proposed. These anisotropy features are derived from gray level difference histogram (GLDH), pair correlation function (PCF), probabilistic local directionality statistics and randomness of texture (GLCM-χ8). For comparison with other features, the most popular gray level co-occurrence matrix (GLCM) derived features are also extracted. Accordingly, three alternative data sets are prepared to classify the images with five different classifiers –Bayesian, multilayer perceptron (MLP), probabilistic neural network (PNN), learning vector quantisation (LVQ) and support vector machine (SVM). A comparative evaluation in terms of specificity, sensitivity, discrimination score and accuracy has been made while classifying US images of human livers. On the basis of results this paper enumerates as to how the anisotropy feature provides better entity for classification purpose in the present context. It is also shown that the highest accuracy of 99% is obtained using anisotropy features with PNN. Anisotropy features leads to 100% sensitivity with PNN and SVM. The present classification system with anisotropy features outperforms the existing models available in the literature keeping in mind the simplicity of the algorithm.

Underwater Image Enhancement by Adaptive Gray World and Differential Gray-Levels Histogram Equalization

Most underwater images tend to be dominated by a single color cast. This paper presents a solution to remove the color cast and improve the contrast in underwater images. However, afte ...

A Novel Design Method of Gray-levels Transformation Function for Image Contrast Improvement Based on Histogram Equalization Technique

Histogram equalization (HE) is a simple and effective method for contrast enhancement as it can automatically define the gray-levels transformation function based on the distribution of gray-levels included in the image. HE fails to produce satisfactory results for a broad variety of low-contrast images because the HE does not use a spatial feature included in the input image. This paper proposes a novel contrast enhancement method using not only the conventional histogram but also the differential gray-levels histogram of the input image. The differential gray-levels histogram contains edge information of the input image. Edge information is one of the important spatial features of the image. We can extract the regions where are wanted to be emphasized by using both the conventional histogram and the differential gray-levels histogram. The input histogram is modified by the histogram of the extracted regions. The gray-levels transformation function is derived by the modified histogram.

The Space Division Type Image Contrast Improvement based on Differential Gray-Levels Histogram

The Space Division Type Image Contrast Improvement based on Differential Gray-Levels Histogram

濃度勾配ヒストグラムによる画像のコントラスト改善

When a captured image does not have good contrast, a method for improving its contrast that converts gray-levels based on the distribution of gray-levels included in the image is generally applied. A resultant image with good contrast does not always generated because the conventional method does not use a spatial feature included in an input image. This paper proposes a method for contrast improvement that can enhance differential gray-levels in an image in order to generate a resultant image with clearness and good contrast. The proposed method determines a gray-level of each pixel according to a Look-up-table generated from a histogram to represent a distribution of differential gray-levels. The experimental results show that the resultant images with natural and clear contrast were generated by the proposed method.

Partial discharge classification using principal component transformation

Texture analysis algorithms have been applied to generate different features for identifying partial discharge (PD) sources. The algorithms utilised are the spatial gray-level dependence method, gray-level difference histogram method, gray-level run-length method and the power spectrum method. To reduce the identification time, it is important to minimise the number of features used to describe the PD sources in the feature space. Principal component transformation has been applied as a feature reduction technique on the features obtained from the texture analysis algorithms. The classification accuracy of the principal components generated has been established using a minimum-distance classifier for six different types of PD sources created in the laboratory as well as for a practical case simulating two types of PD on an actual cable length.

An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters

The aim of this study was to investigate the statistical meaning of six GLCM (Gray Level Cooccurrence Matrix) parameters. This objective was mainly pursued by means of a selfcorrsistent, theoretical assessment in order to remain independent from test image. The six statistical parameters are energy, contrast, variance, correlation, entropy and inverse dzfference moment, which are considered the most relevant among the 14 originally proposed by Haralick et al.. The functional analysis supporting theoretical considerations was based on natural clustering in the feature space of segment texture values. The results show that among the six GLCM statistical parameters, five different sets can be identified, each set featuring a specific textural meaning. The first set contains energy and entropy, while the four remaining parameters can be regarded as belonging to four different sets. Two parameters, energy and contrust, are considered to be the most efficient for discriminating different textural patterns. A new GLCM statistical parameter, recursivity, is presented in order to replace energy which presents some degree of correlation with contrast. It is demonstrated that in some cases it may be reasonable to replace the computation of GLCM with that of GLDH (Gray Level Difference Histogram), in order to benefit by a better compromise between texture measurement accuracy, computer storage and computation time.