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

Gray Level Co-Occurrence Matrix Texture Analysis of Germinal Center Light Zone Lymphocyte Nuclei: Physiology Viewpoint with Focus on Apoptosis

2012 ◽  
Vol 18 (3) ◽  
pp. 470-475 ◽  
Author(s):  
Igor Pantic ◽  
Senka Pantic ◽  
Gordana Basta-Jovanovic
Keyword(s):  
Texture Analysis ◽  
Germinal Center ◽  
Nuclear Size ◽  
Gray Level ◽  
Nuclear Area ◽  
Second Moment ◽  
Size And Shape ◽  
Early Stages ◽  
Occurrence Matrix ◽  
Center Light

AbstractIn our study we investigated the relationship between conventional morphometric indicators of nuclear size and shape (area and circularity) and the parameters of gray level co-occurrence matrix texture analysis (entropy, homogeneity, and angular second moment) in cells committed to apoptosis. A total of 432 lymphocyte nuclei images from the spleen germinal center light zones (cells in early stages of apoptosis) were obtained from eight healthy male guinea pigs previously immunized with sheep red blood cells (antigen). For each nucleus, area, circularity, entropy, homogeneity, and angular second moment were determined. All measured parameters of gray level co-occurrence matrix (GLCM) were significantly correlated with morphometric indicators of nuclear size and shape. The strongest correlation was observed between GLCM homogeneity and nuclear area (p < 0.0001, rs = 0.61). Angular second moment values were also highly significantly correlated with nuclear area (rs = 0.39, p < 0.0001). These results indicate that the GLCM method may be a powerful tool in evaluation of ultrastructural nuclear changes during early stages of the apoptotic process.

scholarly journals Ekstraksi Ciri Sel Darah Putih Berdasarkan Gray Level Co-occurence Matrix

2020 ◽  
Vol 10 (2) ◽  
pp. 99
Author(s):  
Anwar Siswanto ◽  
Abdul Fadlil ◽  
Anton Yudhana
Keyword(s):  
Gray Level ◽  
Second Moment ◽  
Occurrence Matrix

Dalam tubuh manusia terkandung darah yang terdiri dari komponen selular dan non selulardimana salah satu komponen selular adalah sel darah putih. Darah didistribusikan melalui pembuluh darah dari jantung ke seluruh tubuh dan kembali lagi menuju jantung. Sistem ini berfungsi untuk memenuhi kebutuhan sel atau jaringan akan nutrien dan oksigen serta mentranspor sisa metabolisme sel atau jaringan keluar dari tubuh. Dalam berbagai penegakan diagnosis penyakit, sel darah putih merupakan indikator yang dibutuhkan. Pengenalan secara manual membutuhkan waktu yang lama dan cenderung subjektif tergantung dari pengalaman petugas. Sel darah putih diketahui dengan pemeriksaan Sediaan Apus Darah Tepi (SADT) dengan pewarnaan My Grundwald. Penelitian ini bertujuan untuk membantu pengenalan sel darah putih secara otomatis sehingga didapatkan hasil yang cepat dan akurat. Sel darah putih terdiri dari Eosinofil, Basofil, Neutrofil, Limfosit dan Monosit.Penelitian ini menggunakan citra dari apusan darah tepi menggunakan mikroskop digital. Sistem pengenalan sel darah putih ini berdasarkan ekstraksi fitur Gray Level Co-occurrence Matrix (GLCM) yaitu menggunakan fitur Contrast, Anguler Second Moment (ASM) serta Inverse Difference Moment (IDM) dan Correlation. Klasifikasi dengan menggunakan K-means Clustering dihasilkan plot berbeda-beda dan terlihat beberapa ciri yang mirip sesuai jenis sel darah putih. 

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.

scholarly journals Analysis of vascular architecture and parenchymal damage generated by reduced blood perfusion in decellularized porcine kidneys using a gray level co-occurrence matrix

2021 ◽  
Author(s):  
Igor V. Pantic ◽  
Adeeba Shakeel ◽  
Georg A Petroianu ◽  
Peter R Corridon
Keyword(s):  
Wavelet Coefficient ◽  
Blood Perfusion ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Kidney Medulla ◽  
Vascular Architecture ◽  
Second Moment ◽  
Arterial Perfusion ◽  
Occurrence Matrix ◽  
Parenchymal Damage

There is no cure for kidney failure, but a bioartificial kidney may help address this global problem. Decellularization provides a promising platform to generate transplantable organs. However, maintaining a viable vasculature is a significant challenge to this technology. Even though angiography offers a valuable way to assess scaffold structure/function, subtle changes are overlooked by specialists. In recent years, innovative image analysis methods in radiology have been suggested to detect and identify subtle changes in tissue architecture. The aim of our research was to apply one of these methods based on a gray level co-occurrence matrix (GLCM) computational algorithm in the analysis of vascular architecture and parenchymal damage generated by hypoperfusion in decellularized porcine. Perfusion decellularization of the whole porcine kidneys was performed using previously established protocols. We analyzed and compared angiograms of kidneys subjected to pathophysiological arterial perfusion of whole blood. For regions of interest (ROIs) covering kidney medulla and the main elements of the vascular network, five major GLCM features were calculated: angular second moment as an indicator of textural uniformity, inverse difference moment as an indicator of textural homogeneity, GLCM contrast, GLCM correlation, and sum variance of the co-occurrence matrix. In addition to GLCM, we also performed discrete wavelet transform analysis of angiogram ROIs by calculating the respective wavelet coefficient energies using high and low-pass filtering. We report statistically significant changes in GLCM and wavelet features, including the reduction of the angular second moment and inverse difference moment, indicating a substantial rise in angiogram textural heterogeneity. Our findings suggest that the GLCM method can be successfully used as an addition to conventional fluoroscopic angiography analyses of micro/macrovascular integrity following in vitro blood perfusion to investigate scaffold integrity. This approach is the first step toward developing an automated network that can detect changes in the decellularized vasculature.

RELATIONSHIP BETWEEN SOIL MOISTURE CONTENT IN PADDY FIELD AND ITS IMAGE TEXTURE

2016 ◽  
Vol 78 (1-2) ◽  
Author(s):  
Siti Khairunniza Bejo ◽  
Nor Hafizah Sumgap ◽  
Siti Nurul Afiah Mohd Johari
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Color Space ◽  
Texture Features ◽  
Image Texture ◽  
Gray Level ◽  
Second Moment ◽  
Variance Homogeneity ◽  
Occurrence Matrix

The aim of this study is to identify the relationship between soil moisture content and its image texture. Soil image was captured and converted into CIELUV color space. These images were later used to develop two dimensional gray level co-occurrence matrix. Eight texture features extracted from gray level co-occurrence matrix namely mean, variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation was used for the analysis. The results has shown that the image texture properties can be used to relate with soil moisture content, where variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation gave significant responds to the moisture content. The highest value of correlation was gathered from entropy with r = -0.522.

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.

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