Grey Level Co-Occurrence Matrix (GLCM) and Multi-Scale Non-Negative Sparse Coding For Classification of Medical Images

2019 ◽  
Vol 11 (10-SPECIAL ISSUE) ◽  
pp. 481-493
Author(s):  
P. Dhanalakshmi ◽  
Dr.G. Satyavathy
Keyword(s):  
Sparse Coding ◽  
Medical Images ◽  
Grey Level ◽  
Multi Scale ◽  
Occurrence Matrix

scholarly journals Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images

2020 ◽  
Vol 6 (6) ◽  
pp. 51
Author(s):  
Marcos A. M. Almeida ◽  
Iury A. X. Santos
Keyword(s):  
Early Diagnosis ◽  
Medical Images ◽  
Skin Tumor ◽  
Classification Models ◽  
Color Channel ◽  
Second Order Statistics ◽  
New Strategy ◽  
Channel Information ◽  
Occurrence Matrix

Medical images have made a great contribution to early diagnosis. In this study, a new strategy is presented for analyzing medical images of skin with melanoma and nevus to model, classify and identify lesions on the skin. Machine learning applied to the data generated by first and second order statistics features, Gray Level Co-occurrence Matrix (GLCM), keypoints and color channel information—Red, Green, Blue and grayscale images of the skin were used to characterize decisive information for the classification of the images. This work proposes a strategy for the analysis of skin images, aiming to choose the best mathematical classifier model, for the identification of melanoma, with the objective of assisting the dermatologist in the identification of melanomas, especially towards an early diagnosis.

GENETIC ALGORITHM OPTIMIZATION OF ADAPTIVE MULTI-SCALE GLCM FEATURES

2003 ◽  
Vol 17 (01) ◽  
pp. 17-39 ◽  
Author(s):  
ROSS F. WALKER ◽  
PAUL T. JACKWAY ◽  
DENNIS LONGSTAFF
Keyword(s):  
Genetic Algorithm ◽  
Weighting Function ◽  
Classification Performance ◽  
Discriminatory Power ◽  
Matrix Elements ◽  
Grey Level ◽  
Algorithm Optimization ◽  
Multi Scale ◽  
Classification Of Images ◽  
Occurrence Matrix

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification "worth" is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

scholarly journals CLASSIFICATION OF URBAN FEATURE FROM UNMANNED AERIAL VEHICLE IMAGES USING GASVM INTEGRATION AND MULTI-SCALE SEGMENTATION

2015 ◽  
Vol XL-1-W5 ◽  
pp. 479-485 ◽  
Author(s):  
M. Modiri ◽  
A. Salehabadi ◽  
M. Mohebbi ◽  
A. M. Hashemi ◽  
M. Masumi
Keyword(s):  
Spectral Composition ◽  
Simultaneous Occurrence ◽  
Average Height ◽  
Impervious Surfaces ◽  
Image Stack ◽  
Multi Scale ◽  
The Difference ◽  
Occurrence Matrix ◽  
Paved Road

The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

scholarly journals Multi-scale characterizations of colon polyps via computed tomographic colonography

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Weiguo Cao ◽  
Marc J. Pomeroy ◽  
Yongfeng Gao ◽  
Matthew A. Barish ◽  
Almas F. Abbasi ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Texture Features ◽  
Learning Model ◽  
Colon Polyps ◽  
Texture Descriptor ◽  
Operating Characteristics ◽  
Computed Tomographic ◽  
Multi Scale ◽  
Definition Of ◽  
Occurrence Matrix

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

scholarly journals A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET

2021 ◽  
Vol 11 (2) ◽  
pp. 535
Author(s):  
Mahbubunnabi Tamal
Keyword(s):  
Textural Features ◽  
Strongly Correlated ◽  
Segmentation Method ◽  
Diagnosis And Prognosis ◽  
Radiotracer Uptake ◽  
Positron Emission ◽  
Occurrence Matrix ◽  
The Impact ◽  
Robust To Noise

Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.

Sign in / Sign up

Export Citation Format

Share Document