scholarly journals Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix

2017 ◽  
Vol 32 (1) ◽  
pp. e22175 ◽  
Author(s):  
Keigo Kono ◽  
Ruka Hayata ◽  
Satoru Murakami ◽  
Mai Yamamoto ◽  
Maiko Kuroki ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Morphological Characteristic ◽  
Precursor Cells ◽  
Gray Level ◽  
Occurrence Matrix

scholarly journals WRITER IDENTIFICATION BY TEXTURE ANALYSIS BASED ON KANNADA HANDWRITING

2012 ◽  
pp. 80-85
Author(s):  
B.V. DHANDRA ◽  
VIJAYALAXMI.M. B ◽  
GURURAJ MUKARAMBI ◽  
MALLIKARJUN. HANGARGE
Keyword(s):  
Texture Analysis ◽  
Identification Problem ◽  
Document Image ◽  
Independent Method ◽  
Gray Level ◽  
Writer Identification ◽  
Texture Image ◽  
Major Research ◽  
Gabor Filtering ◽  
Occurrence Matrix

Writer identification problem is one of the important area of research due to its various applications and is a challenging task. The major research on writer identification is based on handwritten English documents with text independent and dependent. However, there is no significant work on identification of writers based on Kannada document. Hence, in this paper, we propose a text-independent method for off-line writer identification based on Kannada handwritten scripts. By observing each individual’s handwriting as a different texture image, a set of features based on Discrete Cosine Transform, Gabor filtering and gray level co-occurrence matrix, are extracted from preprocessed document image blocks. Experimental results demonstrate that the Gabor energy features are more potential than the DCTs and GLCMs based features for writer identification from 20 people.

Application Of Texture Analysis In Echocardiography Images For Myocardial Infarction Tissue

2012 ◽  
Author(s):  
N. Agani ◽  
S. A. R. Abu–Bakar ◽  
S. H. Sheikh Salleh
Keyword(s):  
Myocardial Infarction ◽  
Texture Analysis ◽  
Mahalanobis Distance ◽  
Medical Images ◽  
Object Identification ◽  
Time Interval ◽  
Gray Level ◽  
Tissue Samples ◽  
Second Moment ◽  
Occurrence Matrix

Analisa tekstur adalah satu sifat penting untuk mengenal pasti permukaan dan objek daripada imej perubatan dan pelbagai imej lain. Penyelidikan ini telah membangunkan sebuah algoritma untuk menganalisa tekstur dengan menggunakan imej perubatan dari echocardiography untuk mengenal pasti jantung yang disyaki mengalami myocardial infarction. Di sini penggabungan daripada teknik wavelet extension transform dan teknik gray level co–occurrence matrix adalah dicadangkan. Di dalam penyelidikan ini wavelet extension transform digunakan untuk menghasilkan sebuah imej hampiran yang mempunyai resolusi yang lebih besar. Gray level co–occurrence matrix yang dihitung untuk setiap sub–band digunakan untuk mencirikan empat sifat vektor: entropy, contrast, energy (angular second moment) dan homogeneity (invers difference moment). Pengklasifikasian yang digunakan di dalam penyelidikan ini adalah pengklasifikasian Mahalanobis distance. Kaedah yang telah dicadangkan diuji dengan data klinikal dari imej echocardiography untuk 17 orang pesakit. Untuk setiap pesakit, contoh tisu diambil daripada kawasan yang disyaki infarcted dan kawasan non–infarcted (normal). Untuk setiap pesakit, 8 bingkai imej yang dipisahkan oleh sela waktu tertentu di mana 5 kawasan normal dan 5 kawasan disyaki myocardial infarction berukuran 16×16 piksel akan dianalisa. Hasil pengklasifikasian telah dicapai dengan ketepatan 91.32%. Kata kunci: Analisa tekstur, wavelet extension, co–occurrence matrix, myocardial infarction, sifat vektor Texture analysis is an important characteristic for surface and object identification from medical images and many other types of images. This research has developed an algorithm for texture analysis using medical images do trained from echocardiography in identifying heart with suspected myocardial infarction problem. A set of combination of wavelet extension transform with gray level co–occurrence matrix is proposed. In this work, wavelet extension transform is used to form an image approximation with higher resolution. The gray level co–occurrence matrices computed for each subband are used to extract four feature vectors: entropy, contrast, energy (angular second moment) and homogeneity (inverse difference moment). The classifier used in this work is the Mahalanobis distance classifier. The method is tested with clinical data from echocardiography images of 17 patients. For each patient, tissue samples are taken from suspected infarcted area as well as from non–infarcted (normal) area. For each patient, 8 frames separated by some time interval are used and for each frame, 5 normal regions and 5 suspected myocardial infarction regions of 16×16 pixel size are analyzed. The classification performance achieved 91.32% accuracy. Key words: Texture analysis, wavelet extension, co–occurrence matrix, myocardial infarction, feature vector

Quantitative Distinction of Morphological Characteristics of White Blood Cells in Peripheral Blood Through Texture Analysis Using Gray Level Co-Occurrence Matrix

2017 ◽  
Vol 63 (11+12/2017) ◽  
Author(s):  
Mai Yamamoto ◽  
Keigo Kono ◽  
Maiko Kuroki ◽  
Satoru Murakami ◽  
Ruka Hayata ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Peripheral Blood ◽  
Blood Cells ◽  
White Blood Cells ◽  
Morphological Characteristics ◽  
Gray Level ◽  
Occurrence Matrix

Co-Occurrence Matrix-Based Statistical Model for Texture Analysis from Images

2011 ◽  
Vol 103 ◽  
pp. 717-724
Author(s):  
Hossain Shahera ◽  
Serikawa Seiichi
Keyword(s):  
Texture Analysis ◽  
Vision System ◽  
Ground Truth ◽  
Image Features ◽  
Image Texture ◽  
Gray Level ◽  
Machine Vision System ◽  
Statistical Features ◽  
Texture Surface ◽  
Occurrence Matrix

Texture surface analysis is very important for machine vision system. We explore Gray Level Co-occurrence Matrix-based 2ndorder statistical features to understand image texture surface. We employed several features on our ground-truth dataset to understand its nature; and later employed it in a building dataset. Based on our experimental results, we can conclude that these image features can be useful for texture analysis and related fields.

scholarly journals Use of radiography images and gray level co-occurrence matrix to investigate gravitational granular flow

2018 ◽  
Vol 219 ◽  
pp. 03014
Author(s):  
Selam Waktola ◽  
Krzysztof Grudzień ◽  
Maciej Niedostatkiewicz ◽  
Laurent Babout
Keyword(s):  
Texture Analysis ◽  
Granular Flow ◽  
Gray Level ◽  
Material Distribution ◽  
Porosity Level ◽  
X Ray ◽  
Gravitational Flow ◽  
Model Geometry ◽  
Images Processing ◽  
Occurrence Matrix

The paper presents analysis of granular gravitational flow based on radiography images processing. The investigations were conducted for silo model geometry with concentric/eccentric discharging modes. The continuous X-ray radiography scans of granular material distribution, acquired during flow, were obtained by means of an especially designed model silo with rectangular bin and different settings of hopper angles. Image processing involved texture analysis methods, the Gray-Level Co-Occurrence Matrix (GLCM). The texture analysis of radiography images provides information about changes of granular porosity in different silo zones during silo discharging process. This technique allows to divide the silo space into a number homogenous regions with similar porosity level. The proposed methodology was applied to analyse the flow in silo model with various hopper angles.

scholarly journals The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

Animals ◽  
2022 ◽  
Vol 12 (2) ◽  
pp. 195
Author(s):  
Małgorzata Domino ◽  
Marta Borowska ◽  
Anna Trojakowska ◽  
Natalia Kozłowska ◽  
Łukasz Zdrojkowski ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Texture Features ◽  
Image Texture ◽  
Gray Level ◽  
Image Texture Analysis ◽  
Thermal Images ◽  
Thoracolumbar Region ◽  
Thermal Features ◽  
Occurrence Matrix ◽  
The Impact

Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses’ care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body’s surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.

scholarly journals Fractal and gray level co-occurrence matrix texture analysis of senescent and non-senescent deciduous teeth stem cells: A pilot study

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Ivan Zaletel ◽  
Nebojša T. Milošević ◽  
Vera Todorović ◽  
Milica Kovačević-Filipović ◽  
Nela Puškaš
Keyword(s):  
Stem Cells ◽  
Pilot Study ◽  
Texture Analysis ◽  
Deciduous Teeth ◽  
Gray Level ◽  
Occurrence Matrix
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