Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geostatistical classification for remote sensing: an introduction
Computers & Geosciences
Computing geostatistical image texture for remotely sensed data classification
Computers & Geosciences
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
FPGA architecture for fast parallel computation of co-occurrence matrices
Microprocessors & Microsystems
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
MaZda-A software package for image texture analysis
Computer Methods and Programs in Biomedicine
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
GPU-based cone beam computed tomography
Computer Methods and Programs in Biomedicine
Fuzzy posterior-probabilistic fusion
Pattern Recognition
Supervised restoration of degraded medical images using multiple-point geostatistics
Computer Methods and Programs in Biomedicine
Capsule endoscopy image analysis using texture information from various colour models
Computer Methods and Programs in Biomedicine
Hybrid retinal image registration
IEEE Transactions on Information Technology in Biomedicine
3D image texture analysis of simulated and real-world vascular trees
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.