Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different thresholds were computed using GLCM-energy information. An experimental evaluation on DRIV...

In this paper we propose an instruction to accelerate software caches. While DMAs are very efficient for predictable data sets that can be fetched before they are needed, they introduce a large latency overhead for computations with unpredictable access behavior. Software caches are advantageous when the data set is not predictable but exhibits locality. However, software caches also incur a la...

In this paper we propose a technique for classifying images by modeling features extracted at diierent scales. Speciically, we use texture measures derived from Pap Smear cell nuclei images using a Grey Level Co-occurrence Matrix (GLCM). For a texture feature extracted from the GLCM at a number of distances we hypothesise that by modeling the feature as a continuous function of scale we can obt...

Steganalysis is a technique to detect the hidden embedded information in the provided data. This study proposes a novel steganalytic algorithm which distinguishes between the normal and the stego image. III level contourlet is exploited in this study. Contourlet is known for its ability to capture the intrinsic geometrical structure of an image. Here, the lowest frequency component of each leve...

In this paper we propose a technique for classifying images by modeling features extracted at di erent scales. Speci cally, we use texture measures derived from Pap smear cell nuclei images using a Grey Level Co-occurrence Matrix (GLCM). For a texture feature extracted from the GLCM at a number of distances we hypothesise that by modeling the feature as a continuous function of scale we can obt...

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...