Mass abnormality segmentation is a vital step for the medical diagnostic process and is attracting more and more the interest of many research groups. Currently, most of the works achieved in this area have used the Gray Level Co-occurrence Matrix (GLCM) as texture features with a region-based approach. These features come in previous phase for segmentation stage or are using as inputs to class...

Predicting the epileptic seizure is challenging biomedical problem. EEG signal includes enormous information. Few relevant parameters are expected in the field of recognition and diagnostic purposes. Seizure detection and classification system has been designed and developed. The system uses computer based procedures to detect seizure and classified normal and abnormal subjects. Intelligent com...

In this paper rotation invariant Local Binary Patterns (LBP) texture based descriptors are evaluated experimentally in the context of land-use and land-cover object-based classification. The texture descriptors were employed in the classification of an Ikonos-2 and a Quickbird-2 image. The experiments have shown that texture characterization approaches perform well when combined with the graysc...

Textures are one of the basic features in visual searching, computational vision and also a general property of any surface having ambiguity. This paper presents a novel texture classification system which has a high tolerance against illumination variation. A Gray Level Co-occurrence Matrix (GLCM) and binary pattern based automated similarity identification and defect detection model is presen...

Automatic Fingerprint Identification System (AFIS) uses fingerprint segmentation as its pre-processing step. A fingerprint segmentation step divides the fingerprint image into foreground and background. An AFIS that uses a feature extraction algorithm for person identification will tend to fail if it extracts spurious features from the noisy background area. So fingerprint image segmentation pl...

Texture is an important perceptual property of images based on which image content can be characterized and searched for in a Content Based Search and Retrieval (CBSR) system. This paper investigates techniques for improving texture recognition accuracy by using a set of Wavelet Decomposition Matrices (WDM) in conjunction with Grey Level Co-occurrence Matrices (GLCM). The texture image is decom...

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...

Glioblastoma multiforme (GBM) is the most common malignant primary tumor of the central nervous system, characterized among other traits by rapid metastatis. Three tissue phenotypes closely associated with GBMs, namely, necrosis (N), contrast enhancement (CE), and edema/invasion (E), exhibit characteristic patterns of texture heterogeneity in magnetic resonance images (MRI). In this study, we p...

Microscopic images of leaves, collected from Mona Island dry forest (which is located between Puerto Rico and the Dominican Republic), were analyzed. For each leaf side an image was obtained at two magnifications (200x and 400x). This resulted in four samples of images showing a wide variety of textures and stomata patterns. For each group of images we used the gray-level co-occurrence method t...

In this paper, a new fusion texture feature with orthogonal polynomials based multiresolution subband and the Gray Level Co-occurrence Matrix (GLCM) is presented. The proposed orthogonal polynomials based multiresolution subband coefficients posses the localized frequency information and the GLCM matrices capture the structural and statistical properties from the subband coefficients for charac...