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...

Gray level Co occurrence matrix (GLCM) texture analysis has been aggressively researched for decade for multiple applications. Co occurrence matrix retains the spatial and frequency information of the image while compresses the image into a fraction of size enabling the application of classifier engines for analysis. Haralick features are secondary features derived from GLCM. There have been co...

OBJECTIVE To evaluate texture data of the torn supraspinatus tendon (SST) on preoperative T2-weighted magnetic resonance arthrography (MRA) using the gray-level co-occurrence matrix (GLCM) for prediction of post-operative tendon state. MATERIALS AND METHODS Fifty patients who underwent arthroscopic rotator cuff repair for full-thickness tears of the SST were included in this retrospective stu...

Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationa...

In recent years, brain tumors become the leading cause of death in the world. Detection and rapid classification of this tumor are very important and may indicate the likely diagnosis and treatment strategy. In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.e., meningioma, glioma, and pituitary tumo...

The textural and spatial information extracted from very high resolution (VHR) remote sensing imagery provides complementary information for applications in which the spectral information is not sufficient for identification of spectrally similar landscape features. In this study grey-level co-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from...

Recently, a rapid decline in the quality of Indian tea production has been observed due to the old age of the plantations, disease and pests infestations and frequent application of pesticides and insecticides. This paper shows an application of remote sensing and GIS technologies for monitoring tea plantations. We developed an approach for monitoring and assessing tea bush health using texture...