Texture Features from Gray level Gap Length Matrix
نویسندگان
چکیده
sever& texture features are introduced from a proposed higher-order statistical matrix, the gray level gap length matrix (GLGLM). The GLGLM measures the gray level variations in an image. It complements the gray level run length matrix (GLRLM) and is more superior when the number of gray level is large. Features extracted from the weighted GLGLM can be used to estimate the size distribution of the subpatterns. It works much faster than the commonly used K statistic based on gray level cooccurrence matrices, and provides additional quasi-periodicities. The GLGLM and its features seem very useful in texture segmentation and classification.
منابع مشابه
Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis
PURPOSE To elucidate differences between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) with MR image-based texture features. METHODS This was an Institutional Review Board (IRB)-approved retrospective study. Consecutive, pathologically proven, initially treated 44 patients with GBM and 16 patients with PCNSL were enrolled. We calculated a total of 67 image texture fea...
متن کاملFeature Fusion Technique for Colour Texture Classification System Based on Gray Level Co-occurrence Matrix
In this study, an efficient feature fusion based technique for the classification of colour texture images in VisTex album is presented. Gray Level Co-occurrence Matrix (GLCM) and its associated texture features contrast, correlation, energy and homogeneity are used in the proposed approach. The proposed GLCM texture features are obtained from the original colour texture as well as the first no...
متن کاملFault condition recognition based on multi-scale co-occurrence matrix for copper flotation process
Image processing technology has been successfully applied to fault detection of copper flotation processes, and the key to realize image processing based fault condition recognition is accurately extracting froth image features closely related to key production indices. To extract texture features of froth images in real-time, a multi-scale gray level co-occurrence matrix (M-GLCM) method is pro...
متن کاملStatistical texture feature set based classification of histopathological images of stomach adenocarcinoma
Inspection of the biopsy samples microscopically plays a vital role in the definitive diagnosis of cancer. To overcome the subjectivity in pathologists’ decision, objective analysis of the stomach biopsy samples is carried out in this work. At the tissue level, malignancy leads to distortions in glandular structure and nearby supporting tissue namely, stroma. These pathological alterations are ...
متن کاملAutomatic classification of Non-alcoholic fatty liver using texture features from ultrasound images
Background: Accurate and early detection of non-alcoholic fatty liver, which is a major cause of chronic diseases is very important and is vital to prevent the complications associated with this disease. Ultrasound of the liver is the most common and widely performed method of diagnosing fatty liver. However, due to the low quality of ultrasound images, the need for an automatic and intelligent...
متن کامل