Image Processing Applications Based on Texture and Fractal Analysis

2013 ◽  
pp. 235-259
Author(s):  
Radu Dobrescu ◽  
Dan Popescu
Keyword(s):  
Fractal Dimension ◽  
Texture Classification ◽  
Malignant Tumour ◽  
Classification Performance ◽  
Textured Surfaces ◽  
Box Counting ◽  
Image Partition ◽  
Fractal Analyses ◽  
Occurrence Matrix

Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives from the mean co-occurrence matrices: contrast, energy, entropy, homogeneity, and variance. The second class is based on fractal dimension and is derived from a box-counting algorithm. For the purpose of increasing texture classification performance, the notions “mean co-occurrence matrix” and “effective fractal dimension” are introduced and utilized. Some applications of the texture and fractal analyses are presented: road analysis for moving objective, defect detection in textured surfaces, malignant tumour detection, remote land classification, and content based image retrieval. The results confirm the efficiency of the proposed methods and algorithms.

Image Processing Applications Based on Texture and Fractal Analysis

2011 ◽  
pp. 226-250 ◽  
Author(s):  
Radu Dobrescu ◽  
Dan Popescu
Keyword(s):  
Fractal Dimension ◽  
Texture Classification ◽  
Malignant Tumour ◽  
Classification Performance ◽  
Textured Surfaces ◽  
Box Counting ◽  
Image Partition ◽  
Fractal Analyses ◽  
Occurrence Matrix

Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives from the mean co-occurrence matrices: contrast, energy, entropy, homogeneity, and variance. The second class is based on fractal dimension and is derived from a box-counting algorithm. For the purpose of increasing texture classification performance, the notions “mean co-occurrence matrix” and “effective fractal dimension” are introduced and utilized. Some applications of the texture and fractal analyses are presented: road analysis for moving objective, defect detection in textured surfaces, malignant tumour detection, remote land classification, and content based image retrieval. The results confirm the efficiency of the proposed methods and algorithms.

scholarly journals Classification of texture using random box counting and binarization methods

2021 ◽  
Vol 10 (1) ◽  
pp. 533-540
Author(s):  
Wijdan Jaber AL-kubaisy ◽  
Maha Mahmood
Keyword(s):  
Image Processing ◽  
Fractal Dimension ◽  
Blood Cells ◽  
Multifractal Analysis ◽  
Vision System ◽  
White Blood Cells ◽  
Real Life ◽  
Self Similarity ◽  
Box Counting

The heterogeneous texture classifications with the complexity of structures provide variety of possibilities in image processing, as an example of the multifractal analysis features. The task of texture analysis is a highly significant field of study in the field of machine vision. Most of the real-life surfaces exhibit textures and an efficiently modelled vision system must have the ability to deal with this variety of surfaces. A considerable number of surfaces maintain a self-similarity quality as well as statistical roughness at different scales. Fractals could provide a great deal of advantages; also, they are popular in the process of modelling these properties in the tasks related to the field of image processing. With two distinct methods, this paper presents classification of texture using random box counting and binarization methods calculate the estimation measures of the fractal dimension BCM. There methods are the banalization and random selecting boxes. The classification of the white blood cells is presented in this paper based on the texture if it is normal or abnormal with the use of a number of various methods.

scholarly journals Identification of emphysema patterns in high resolution computed tomography images

2017 ◽  
Vol 4 (1) ◽  
pp. 16
Author(s):  
Musibau A. Ibrahim ◽  
Oladotun A. Ojo ◽  
Peter A. Oluwafisoye
Keyword(s):  
Computed Tomography ◽  
Fractal Dimension ◽  
High Resolution ◽  
Hölder Exponent ◽  
High Resolution Computed Tomography ◽  
Box Counting ◽  
Computed Tomography Images ◽  
Local Intensity ◽  
Box Counting Method

Fractal dimension (FD) is a very useful metric for the analysis of image structures with statistically self-similar properties. It has applications in areas such as texture segmentation, shape classification and analysis of medical images. Several approaches can be used for calculating the fractal dimension of digital images; the most popular method is the box-counting method. It is also very challenging and difficult to classify patterns in high resolution computed tomography images (HRCT) using this important descriptor. This paper applied the Holder exponent computation of the local intensity values for detecting the emphysema patterns in HRCT images. The absolute differences between the normal and the abnormal regions in the images are the key for a successful classification of emphysema patterns using the statistical analysis. The results obtained in this paper demonstrated the effectiveness of the predictive power of the features extracted from the Holder exponent in the analysis and classification of HRCT images. The overall classification accuracy achieved in lung tissue layers is greater than 90%, which is an evidence to prove the effectiveness of the methods investigated in this paper.

scholarly journals ROI-based features for classification of skin diseases using a multi-layer neural network

2021 ◽  
Vol 23 (1) ◽  
pp. 216
Author(s):  
Thanh-Hai Nguyen ◽  
Ba-Viet Ngo
Keyword(s):  
Skin Disease ◽  
Classification Accuracy ◽  
Skin Diseases ◽  
Human Life ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
High Classification Accuracy ◽  
Separate Region ◽  
Occurrence Matrix

<p>Skin diseases have a serious impact on human life and health. This article aims to represent the classification accuracy of skin diseases for supporting the physicians’ correct decision on patients for early treatment. In particular, 100 images in each type of five skin diseases from ISIC database are used for balanced datasets related to the classification accuracy. In addition, this paper focuses on processing images for extracting six optimal types of eleven features of skin disease image for higher classification performance and also this takes less time for training. Therefore, skin disease images are filtered and segmented for separating region of interests (ROIs) before extracting optimal features. First, the skin disease images are processed by normalizing sizes, removing noises, segmenting to separate region of interests (ROIs) showing skin disease signs. Next, a gray-level co-occurrence matrix (GLCM) method is applied for texture analysis to extract eleven features. With the optimal six features chosen, the high classification accuracy of skin diseases is about 92% evaluated using a matrix confusion. The result showed to illustrate the effectiveness of the proposed method. Furthermore, this method can be developed for other medical datasets for supporting in disease diagnosis.</p>

scholarly journals COMPARISON OF THE INFLUENCE OF STANDARDIZATION AND NORMALIZATION OF DATA ON THE EFFECTIVENESS OF SPONGY TISSUE TEXTURE CLASSIFICATION

2019 ◽  
Vol 9 (3) ◽  
pp. 66-69
Author(s):  
Róża Dzierżak
Keyword(s):  
Support Vector Machine ◽  
Texture Classification ◽  
High Accuracy ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Bayes Classifier ◽  
Spongy Tissue ◽  
Importance Ranking ◽  
Occurrence Matrix

The aim of this article was to compare the influence of the data pre-processing methods – normalization and standardization – on the results of the classification of spongy tissue images. Four hundred CT images of the spine (L1 vertebra) were used for the analysis. The images were obtained from fifty healthy patients and fifty patients with diagnosed with osteoporosis. The samples of tissue (50×50 pixels) were subjected to a texture analysis to obtain descriptors of features based on a histogram of grey levels, gradient, run length matrix, co-occurrence matrix, autoregressive model and wavelet transform. The obtained results were set in the importance ranking (from the most important to the least important), and the first fifty features were used for further experiments. These data were normalized and standardized and then classified using five different methods: naive Bayes classifier, support vector machine, multilayer perceptrons, random forest and classification via regression. The best results were obtained for standardized data and classified by using multilayer perceptrons. This algorithm allowed for obtaining high accuracy of classification at the level of 94.25%.

Feature Modeling and Classification of Halftone Image Based on Statistical Method

2012 ◽  
Vol 229-231 ◽  
pp. 1693-1696
Author(s):  
Zhi Qiang Wen ◽  
Wen Qiu Zhu ◽  
Yong Xiang Hu ◽  
Zhao Yi Peng
Keyword(s):  
Autocorrelation Function ◽  
Feature Vector ◽  
Classification Performance ◽  
Feature Modeling ◽  
Gray Level ◽  
Halftone Image ◽  
Occurrence Matrix ◽  
Halftone Images ◽  
Better Than

For problem of feature modeling on halftone image, three statistics methods, named gray-level co-occurrence matrix, autocorrelation function and spectrum statistics, are used to extract feature vector of various halftone images. Then, their classification performance is assessed by radial basis function neural network. A mass of experiments show the autocorrelation function is better than other two methods for classification on halftone image.

scholarly journals ENHANCING VOLUMETRIC BOULIGAND–MINKOWSKI FRACTAL DESCRIPTORS BY USING FUNCTIONAL DATA ANALYSIS

2011 ◽  
Vol 22 (09) ◽  
pp. 929-952 ◽  
Author(s):  
JOÃO BATISTA FLORINDO ◽  
MÁRIO DE CASTRO ◽  
ODEMIR MARTINEZ BRUNO
Keyword(s):  
Fractal Dimension ◽  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Texture Classification ◽  
Data Representation ◽  
Classification Problems ◽  
Minkowski Fractal

This work proposes and studies the concept of Functional Data Analysis transform, applying it to the performance improving of volumetric Bouligand–Minkowski fractal descriptors. The proposed transform consists essentially in changing the descriptors originally defined in the space of the calculus of fractal dimension into the space of coefficients used in the functional data representation of these descriptors. The transformed descriptors are used here in texture classification problems. The enhancement provided by the FDA transform is measured by comparing the transformed to the original descriptors in terms of the correctness rate in the classification of well known datasets.

scholarly journals Classification of Building Images using Fractal Features

2021 ◽  
Vol 9 (5) ◽  
pp. 183-185
Author(s):  
A. Sangeetha ◽  
R. Rajakumari
Keyword(s):  
Fractal Dimension ◽  
Region Of Interest ◽  
Machine Learning Algorithms ◽  
Complex Objects ◽  
Box Counting ◽  
Efficient Measure ◽  
Sobel Edge Detection ◽  
Edge Detection Method ◽  
Extent Of Damage

Cracks in concrete buildings may show the total extent of damage or problems of greater magnitude. Causes of cracks depend on the nature of the crack and the type of structure. Crack classification is an approach to using machine learning algorithms to find a particular type of crack. The image is preprocessed by image smoothening and removes noise using a Gaussian filter, whereas the Sobel edge detection method is used to detect the edges. By using k-means clustering, the image segmentation is carried out to identify the Region of Interest. Fractal dimension is an efficient measure for complex objects. Fractal features like fractal dimension, average, and lacunarity are calculated using a differential box-counting algorithm. The classification of the crack classifies the crack based on the characteristics derived from the crack area.

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