Use of Local Binary Pattern in Texture Classification of Carbonate Rock Micro-CT Images

2017 ◽  
Author(s):  
Khurshed Rahimov ◽  
Ali M. AlSumaiti ◽  
Hasan AlMarzouqi ◽  
Mohamed Soufiane Jouini
Keyword(s):  
Carbonate Rock ◽  
Local Binary Pattern ◽  
Texture Classification ◽  
Ct Images ◽  
Micro Ct

Deep learning for lithological classification of carbonate rock micro-CT images

2021 ◽  
Author(s):  
Carlos E. M. dos Anjos ◽  
Manuel R. V. Avila ◽  
Adna G. P. Vasconcelos ◽  
Aurea M. Pereira Neta ◽  
Lizianne C. Medeiros ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carbonate Rock ◽  
Ct Images ◽  
Micro Ct

Shape-Based Clustering and Classification of Breast Microcalcifications in Micro-CT Images

2015 ◽  
pp. 160-163
Author(s):  
Evgenia Papavasileiou ◽  
Frederik Temmermans ◽  
Bart Jansen ◽  
Inneke Willekens ◽  
Elke Van de Casteele ◽  
...  
Keyword(s):  
Ct Images ◽  
Micro Ct ◽  
Clustering And Classification

scholarly journals Classification of micro-CT images using 3D characterization of bone canal patterns in human osteogenesis imperfecta

2017 ◽  
Author(s):  
Anas Z. Abidin ◽  
John Jameson ◽  
Robert Molthen ◽  
Axel Wismüller
Keyword(s):  
Osteogenesis Imperfecta ◽  
Ct Images ◽  
Micro Ct

DELINEATION AND CLASSIFICATION OF LIVER CANCER USING LEVEL SET METHOD IN CT IMAGES

2017 ◽  
Vol 29 (06) ◽  
pp. 1750047
Author(s):  
Amita Das ◽  
S. S. Panda ◽  
Sukanta Sabut
Keyword(s):  
Machine Learning ◽  
Level Set ◽  
Texture Classification ◽  
Ct Images ◽  
Experimental Result ◽  
Support Vector ◽  
Svm Classifier ◽  
Correct Prediction ◽  
Liver Cancers

The paper proposes a modified approach of delineation and classification of two different types of liver cancers viz. Hepatocellular Carcinoma (HCC) and Metastatic Carcinoma (MET) from different slices of computed tomography (CT) scans images. A combined framework of reorganization and extraction of region of interest (ROI), texture feature extraction followed by texture classification by different machine learning approaches has been presented. Initially, adaptive thresholding has been applied to segment the liver region from CT images. Level set algorithm has been used for detecting the region of cancer tissues. In the classification stage, the delineated output lesions have been extracted with 38 features to build up the dataset. Two machine learning classifiers, support vector machine (SVM) and random forest (RF), have been used to train the dataset for correct prediction of cancer classes. Ten-fold cross-validation has been used to evaluate the performance of two classifiers. The efficiency of the proposed algorithm is tested in terms of accuracy, where the RF classifier achieved a higher accuracy of 95% compared to SVM classifier of 87%. The experimental result proves the superiority of RF classifier compared to SVM classifier with level-set features.

Key controlling factors of permeability estimated from micro-CT images of Brazilian analogous pre-salt carbonate rock

2014 ◽  
Author(s):  
Nathaly L. Archilha* ◽  
Roseane M. Missagia ◽  
Cathy Hollis ◽  
Marco A. R. Ceia Irineu ◽  
A. L. Neto ◽  
...  
Keyword(s):  
Carbonate Rock ◽  
Ct Images ◽  
Controlling Factors ◽  
Micro Ct

Automatic classification of squamosal abnormality in micro-CT images for the evaluation of rabbit fetal skull defects using active shape models

2014 ◽  
Author(s):  
Antong Chen ◽  
Belma Dogdas ◽  
Saurin Mehta ◽  
Ansuman Bagchi ◽  
L. David Wise ◽  
...  
Keyword(s):  
Automatic Classification ◽  
Ct Images ◽  
Micro Ct ◽  
Active Shape Models ◽  
Skull Defects ◽  
Shape Models ◽  
Active Shape

scholarly journals Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

2019 ◽  
Vol 9 (9) ◽  
pp. 1930 ◽  
Author(s):  
Sibel Arslan ◽  
Celal Ozturk
Keyword(s):  
Artificial Bee Colony ◽  
Local Binary Pattern ◽  
Texture Classification ◽  
Classification Problem ◽  
Automatic Programming ◽  
Abc Algorithm ◽  
Machine Learning Methods ◽  
Bee Colony ◽  
High Level

Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.

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