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.

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

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 Color Laser Printer Identification through Discrete Wavelet Transform and Gray Level Co-occurrence Matrix

2010 ◽  
Vol 17B (3) ◽  
pp. 197-206 ◽  
Author(s):  
Ji-Yeoun Baek ◽  
Heung-Su Lee ◽  
Seung-Gyu Kong ◽  
Jung-Ho Choi ◽  
Yeon-Mo Yang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Laser Printer ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Occurrence Matrix

Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network

SIMULATION ◽  
2020 ◽  
Vol 96 (11) ◽  
pp. 867-879
Author(s):  
Li Xu ◽  
Qi Gao ◽  
Nasser Yousefi
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Cells ◽  
Deep Belief Network ◽  
Tumor Segmentation ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Belief Network ◽  
Occurrence Matrix ◽  
The Brain

Brain tumors are a group of cancers that originate from different cells of the central nervous system or cancers of other tissues in the brain. Excessive cell growth in the brain is called a tumor. Tumor cells need food and blood to survive. Growth and proliferation of tumor cells in the cranial space, cause strain inside the brain and thus disrupt vital human structures. Therefore, diagnosis in the early stages of brain tumors is crucial. This study introduces a new optimized method for early diagnosis of the brain tumor. The method has five main parts of noise reduction, tumor segmentation, morphology, feature extraction based on wavelet and gray-level co-occurrence matrix, and classification based on an optimized deep belief network. For optimizing the classifier network, an enhanced version of the moth search algorithm is utilized. Simulation results are applied to three different datasets, FLAIR, T1, and T2, and the accuracy results of the presented method are compared with two other metaheuristics, particle swarm optimization and Bat algorithms. The final results showed that the presented technique has good achievements toward the compared methods.

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

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.

Fractal analysis and Gray level co-occurrence matrix method for evaluation of reperfusion injury in kidney medulla

2016 ◽  
Vol 397 ◽  
pp. 61-67 ◽  
Author(s):  
Igor Pantic ◽  
Zorica Nesic ◽  
Jovana Paunovic Pantic ◽  
Sanja Radojević-Škodrić ◽  
Mila Cetkovic ◽  
...  
Keyword(s):  
Reperfusion Injury ◽  
Fractal Analysis ◽  
Matrix Method ◽  
Gray Level ◽  
Kidney Medulla ◽  
Occurrence Matrix

scholarly journals Perbandingan Tingkat Akurasi Pengenalan Kadar Semen Dan Pasir Pada Campuran Kering Berdasarkan Tingkat Resolusi Kamera Dengan Metode Pengenalan JST

2021 ◽  
Vol 1 (2) ◽  
pp. 168-183
Author(s):  
Erven Tanjungan ◽  
Gasim Gasim ◽  
Sudiadi Sudiadi
Keyword(s):  
Standard Deviation ◽  
Gray Level ◽  
Second Moment ◽  
Occurrence Matrix

Pasir dan semen merupakan salah satu material terbesar atau terpenting yang digunakan dalam proses pembangunan pada suatu bangunan atau gedung dan selalu digunakan oleh masyarakat. Masing-masing campuran memiliki takaran pasir dan semennya masing-masing, namun untuk orang biasa sulit untuk membedakan jenis-jenis campuran kering pada bangunan runtuh ataupun bangunan yang belum jadi. Penelitian ini membandingkan tingkat akurasi pengenalan kadar semen dan pasir pada campuran kering berdasarkan tingkat resolusi kamera dengan metode pengenalan Jaringan Saraf Tiruan. Jenis campuran yang digunakan antara lain dengan takaran 1semen 1pasir, 1semen 1,5pasir, 1semen 2pasir, 1semen 2,5pasir, 1semen 3pasir, dan 1semen 3,5pasir. Tingkat resolusi kamera yang digunakan ada 5 antara lain 3MP, 5MP, 8MP, 10MP, 12MP, dan menggunakan jarak pemotretan ±9cm. Metode pengenalan menggunakan Jaringan Syaraf Tiruan dan ekstrasi fitur menggunakan GLCM(Gray Level Co-Occurrence Matrix) yang terdiri dari Entropy, Standard Deviation, Contrast, Angular Second Moment(ASM)/ Homogeneity, Correlation, dan Inverse Different Moment(IDM)/ Energy. Hasil perhitungan tertinggi dalam pengenalan jenis campuran kering berdasarkan tingkat resolusi kamera ialah pada resolusi kamera 12MP dengan jumlah pengenalan sebanyak 105 dari 120 data uji, sehingga menghasilkan tingkat akurasi sebesar 87,5%.

Sign in / Sign up

Export Citation Format

Share Document