This paper investigates on extending and comparing the Gray level co-occurrence matrices (GLCM) and 3D Gabor filters in volumetric texture analysis of brain tumor tissue classification. The extracted features are sub-selected by genetic algorithm for dimensionality reduction and fed into Extreme Learning Machine Classifier. The organizational prototype of image voxels distinctive to the underly...

Keyword Down syndrome, Trisomy, Nuchal Translucency, Chromosomal Abnormalities, Gray Level Cooccurrence Matrix(GLCM), Support Vector Machine (SVM) Down syndrome or Trisomy 21 is a genetic disorder which causes mental disability to the baby during the gestation period. Ultrasound scan, a noninvasive test which includes ultrasound fetal image scan for the Nuchal Translucency measurement (NT). Thi...

The primary factors in determining beef quality grades are the amount and distribution of intramuscular fat percentage (IMFAT). Texture analysis was applied to ultrasound B-mode images from ribeye muscle of live beef cattle to predict its IMFAT. We used wavelet transform (WT) for multiresolutional texture analysis and second-order statistics using a gray-level co-occurrence matrix (GLCM) techni...

The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, sampled directly without need for interpolation due to algorithm’s application of GLCM polar co-ordinate system, which reduces inaccuracy caused by transformation. An additional process then merge convergence method w...

SUMMARY The grey level co-occurrence matrix (GLCM) is a measure of the texture of an image. It describes how often different combinations of pixel brightness values occur in an image. Based on this, several textural attributes can be calculated. These directional attributes can be used to determine isotropic and anisotropic areas. In anisotropic areas the information of directional GLCM-based a...

Recently proposed texture descriptors extracted from the co-occurrence matrix across several datasets is surveyed and validated in this paper; moreover, two new methods for extracting features from the Gray Level Co-occurrence Matrix (GLCM) are proposed. The descriptors are extracted not only from the entire GLCM but also from subwindows. These texture descriptors are used to train a support ve...

PURPOSE To identify the impact of reconstruction algorithms on CT radiomic features of pulmonary tumors and to reveal and compare the intra- and inter-reader and inter-reconstruction algorithm variability of each feature. METHODS Forty-two patients (M:F = 19:23; mean age, 60.43±10.56 years) with 42 pulmonary tumors (22.56±8.51mm) underwent contrast-enhanced CT scans, which were reconstructed ...

Texture is literally defined as consistency of a substance or a surface. Technically, it is the pattern of information or arrangement of structure found in an image. Texture is a crucial characteristic of many image type and textural features have a plethora of application viz., image processing, remote sensing, content-based imaged retrieval and so on. There are various ways of extracting thes...

BACKGROUND Gliomas are the most common primary brain neoplasms. Misdiagnosis occurs in glioma grading due to an overlap in conventional MRI manifestations. The aim of the present study was to evaluate the power of radiomic features based on multiple MRI sequences - T2-Weighted-Imaging-FLAIR (FLAIR), T1-Weighted-Imaging-Contrast-Enhanced (T1-CE), and Apparent Diffusion Coefficient (ADC) map - in...

The majority of the available rigid registration measures are based on a 2-dimensional histogram of corresponding grey-values in the registered images. This paper shows that these features are similar to a family of texture measures based on Grey Level Cooccurrence Matrices (GLCM). Features from the GLCM literature are compared to the current range of measures using images from the visible huma...