Brain tumor is a very harmful disease for human being. A tumor is a mass of tissue that's formed by an accumulation of abnormal cells. The location of tumors in the brain is one of the factors that determine how a brain tumor effects an individual's functioning. Image processing is an active research area in which medical image processing is a highly challenging field. For medical diagnosis of ...

In this method we propose the method to detect the forensic in the photography. For that here we use the svm classifier for the forensic detection. Initially we identify the illuminant map in the image. We find the face from the photography. For the face detect here we use the violo john method. After face detection After that we identify the GLCM (Gray Level Co-Occurance Matrix). In GLCM is th...

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...

Brain tumor detection in Magnetic Resonance Imaging (MRI) is important in medical diagnosis because it provides information associated to anatomical structures as well as potential abnormal tissues necessary for treatment planning and patient follow-up. In this paper a brain tumour Detection and Classification System is developed. The image processing techniques such as preprocessing, image enh...

This paper proposes to use ETM+ multispectral data and panchromatic band as well as texture features derived from the panchromatic band for land cover classification. Four texture features including one ‘internal texture’ and three GLCM based textures namely correlation, entropy, and inverse different moment were used in combination with ETM+ multispectral data. Two data sets involving combinat...

We present a fast, efficient, and automatic method for extracting vessels from retinal images. The proposed method is based on the second local entropy and on the gray-level co-occurrence matrix (GLCM). The algorithm is designed to have flexibility in the definition of the blood vessel contours. Using information from the GLCM, a statistic feature is calculated to act as a threshold value. The ...

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 study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated w...

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...