Fractal Dimension Co-occurrence Matrix Method for Texture Classification

2006 ◽  
Author(s):  
Ju Hyun Kim ◽  
Soo Chang Kim ◽  
Tae Jin Kang
Keyword(s):  
Fractal Dimension ◽  
Matrix Method ◽  
Texture Classification ◽  
Occurrence Matrix

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.

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.

scholarly journals Energy analysis of image descriptors in texture classification

2021 ◽  
Author(s):  
Faeze Kiani
Keyword(s):  
Surface Defect ◽  
Energy Analysis ◽  
Texture Classification ◽  
Medical Image Analysis ◽  
Image Description ◽  
Image Descriptors ◽  
Discriminative Feature ◽  
Surface Defect Detection ◽  
Occurrence Matrix

Texture play important role in image description process. Texture classification is one of the problems which have been paid much attention on by computer vision scientists in last decade. If texture classification is done accurately, it can be used in many problems such as skin detection, surface defect detection, medical image analysis, gender identification, human identification, etc. Since now, many approaches are proposed to perform it. Most of them have tried to extract discriminative features to separate different texture types accurately. This paper has proposed an approach based on energy analysis of some efficient image descriptors such as median binary pattern, Local binary pattern and Gray Level Co-occurrence matrix. Next, by concatenating extracted features, a discriminative feature vector is defined. Finally, classifier is used to classify texture types. Although, this approach is a general one and is could be used in different applications. In the result part the proposed approach has been evaluated on some benchmark dataset. Next, the results have been compared with some of state-of-the-art approaches to prove the quality of the proposed approach.

scholarly journals Deteksi Batik Parang Menggunakan Fitur Co-Occurence Matrix Dan Geometric Moment Invariant Dengan Klasifikasi KNN

2016 ◽  
pp. 40
Author(s):  
Ni Luh Wiwik Sri Rahayu Ginantra
Keyword(s):  
Matrix Method ◽  
Nearest Neighbor ◽  
Texture Features ◽  
Sufficient Information ◽  
K Nearest Neighbor ◽  
Moment Invariant ◽  
The Public ◽  
Geometric Moment ◽  
Visual Identification ◽  
Occurrence Matrix

Batik motifs are the base or the blueprint of batik patterns which serve as the core of the batik image design, and therefore the meaning of a sign, symbol or logo in a batik work can be revealed through its motifs. Visual identification requires visual skills and knowledge in classifying patterns formed in a batik image. Lack of media providing information on batik motifs makes the public unable to have sufficient information about batik motifs. Looking at this phenomenon, this study is conducted in order to perform visual identification using a computer that can assist and facilitate in identifying the types of batik. The methods used for batik image recognition are the Co-occurrence Matrix method to provide extraction of batik texture features, and the Geometric Moment Invariant method, while K Nearest Neighbor is used to classify batik images. The results on the accuracy values obtained reveal that the of 80%, compared to the accuracy value result using the Co-occurrence Matrix method that is 70%.  

Chromatin Fractal Organization, Textural Patterns, and Circularity of Nuclear Envelope in Adrenal Zona Fasciculata Cells

2016 ◽  
Vol 22 (6) ◽  
pp. 1120-1127 ◽  
Author(s):  
Igor Pantic ◽  
Dejan Nesic ◽  
Milos Basailovic ◽  
Mila Cetkovic ◽  
Sanja Mazic ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Nuclear Envelope ◽  
Current Knowledge ◽  
Significant Negative Correlation ◽  
Computer Assisted ◽  
Cell Physiology ◽  
Zona Fasciculata ◽  
Research Software ◽  
Occurrence Matrix ◽  
Adrenal Zona Fasciculata

AbstractDespite previous research efforts in the fields of histology and cell physiology, the relationship between chromatin structural organization and nuclear shape remains unclear. The aim of this research was to test the existence and strength of correlations between mathematical parameters of chromatin microarchitecture and roundness of the nuclear envelope. On a sample of 240 nuclei of adrenal zona fasciculata cells stained using the DNA-specific Feulgen method, we quantified fractal parameters such as fractal dimension and lacunarity, as well as textural parameters such as angular second moment (ASM), entropy, inverse difference moment, contrast, and variance. Circularity of the nuclear envelope was determined from the nuclear area and perimeter. The results indicate that there is a statistically significant negative correlation between chromatin ASM and circularity. Moreover, there was a statistically significant positive correlation between chromatin fractal dimension and envelope circularity. This is the first study to demonstrate these relationships in adrenal tissue, and also one of the first studies to test the connection between circularity and fractal and gray-level co-occurrence matrix parameters in DNA-specific Feulgen stain. The results could be useful both as an addition to the current knowledge on chromatin/nuclear envelope interactions, and for design of future computer-assisted research software for evaluation of nuclear morphology.

scholarly journals Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density

2019 ◽  
Vol 11 (3) ◽  
pp. 249 ◽  
Author(s):  
Pejman Rasti ◽  
Ali Ahmad ◽  
Salma Samiei ◽  
Etienne Belin ◽  
David Rousseau
Keyword(s):  
Texture Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Local Binary Patterns ◽  
High Density ◽  
Weed Detection ◽  
Data Set ◽  
Scattering Transform ◽  
Supervised Image Classification ◽  
Occurrence Matrix

In this article, we assess the interest of the recently introduced multiscale scattering transform for texture classification applied for the first time in plant science. Scattering transform is shown to outperform monoscale approaches (gray-level co-occurrence matrix, local binary patterns) but also multiscale approaches (wavelet decomposition) which do not include combinatory steps. The regime in which scatter transform also outperforms a standard CNN architecture in terms of data-set size is evaluated ( 10 4 instances). An approach on how to optimally design the scatter transform based on energy contrast is provided. This is illustrated on the hard and open problem of weed detection in culture crops of high density from the top view in intensity images. An annotated synthetic data-set available under the form of a data challenge and a simulator are proposed for reproducible science (https://uabox.univ-angers.fr/index.php/s/iuj0knyzOUgsUV9). Scatter transform only trained on synthetic data shows an accuracy of 85 % when tested on real data.

scholarly journals Application of Texture and Fractal Dimension Analysis to Estimate Effectiveness of Oral Leukoplakia Treatment Using an Er:YAG Laser—A Prospective Study

Materials ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 3614
Author(s):  
Kamil Jurczyszyn ◽  
Marcin Kozakiewicz
Keyword(s):  
Fractal Dimension ◽  
Er:Yag Laser ◽  
Treatment Method ◽  
Oral Leukoplakia ◽  
Discrete Wavelet ◽  
Dimension Analysis ◽  
Short Run ◽  
Fractal Dimension Analysis ◽  
Occurrence Matrix ◽  
A Prospective Study

Background: Oral leukoplakia (OL) is a potential neoplasmic lesion. The aim of this study was to apply texture analysis (TA) and fractal dimension analysis (FDA) to estimate the effectiveness of OL treatment using an Er:YAG laser. Methods: Eighteen patients with 32 lesions were treated. Laser procedures were conducted using the LiteTouch™ Er:YAG Dental Laser. The diameter of the operational tip was 1.3 mm, the power was 50 mJ, the frequency was 50 Hz, and the wavelength was 2940 nm. TA was based on long and short-run emphasis inverse moments, difference entropy, inverse difference moment, and wavelet decomposition for two-dimensional photography. FDA was measured using the box-counting method. Results: Total response was achieved in 50% of lesions, partial response was observed in 47%, and 3% of lesions did not respond to treatment. Recurrence occurred in 34% of lesions. TA features indicated pathological images depicting leukoplakia and complete reconstruction of the correct mucosal image after laser ablation. The discrete wavelet transformation feature detects much larger structures than the properties derived from the run-length matrix and co-occurrence matrix. Conclusions: The Er:YAG laser is an effective treatment method in cases of oral leukoplakia. Leukoplakia treatment by Er:YAG laser is an effective modality, as revealed by the oral mucosa microstructure. TA and FDA are promising methods to estimate the effectiveness of OL treatment.

Mapping directional variations in seismic character using gray-level co-occurrence matrix-based attributes

2015 ◽  
Vol 3 (1) ◽  
pp. T13-T23 ◽  
Author(s):  
Christoph Georg Eichkitz ◽  
Marcellus Gregor Schreilechner ◽  
Paul de Groot ◽  
Johannes Amtmann
Keyword(s):  
Seismic Anisotropy ◽  
Texture Classification ◽  
Spatial Arrangement ◽  
Training Image ◽  
Seismic Facies ◽  
Gray Level ◽  
Vienna Basin ◽  
Neighboring Pixel ◽  
Analysis Window ◽  
Occurrence Matrix

Texture attributes describe the spatial arrangement of neighboring amplitudes values within a given analysis window. We chose a statistical texture classification method, the gray-level co-occurrence matrix (GLCM), and its derived attributes, to produce a semiautomated description of the spatial arrangement of seismic facies. The GLCM is a measure of how often different combinations of neighboring pixel values occur. We tested the application of directional GLCM-based attributes for the detection of seismic variability within paleoriver features. Calculation of 3D GLCM-based attributes can be done in 13 space directions. The results of GLCM-based attribute calculation differed depending on the chosen GLCM parameters (number of gray levels, analysis window, and direction of calculation). We specifically focused on how the direction of calculation influenced the computation of attributes, while keeping other parameters constant. We first tested the workflow on a 2D training image and later ran on a real seismic amplitude volume from the Vienna Basin. Based on the GLCM-based attributes, we could map the channel features and extract them as geobodies. Additionally, we generated a new set of directional GLCM-based attributes to detect spatial changes in the seismic facies. By comparing these directional attributes, we could determine areas within the channel features having higher directional variability. Areas with higher tendency to directional variations might be associated with changes in lithology, seismic facies, or with seismic anisotropy.

Sign in / Sign up

Export Citation Format

Share Document