Implementation of mathematical morphological operations for spatial data processing
Computers & Geosciences
Automated diagnosis of sewer pipe defects based on machine learning approaches
Expert Systems with Applications: An International Journal
Segmenting ideal morphologies of sewer pipe defects on CCTV images for automated diagnosis
Expert Systems with Applications: An International Journal
Gray-scale edge detection for gastric tumor pathologic cell images by morphological analysis
Computers in Biology and Medicine
A robust approach for automatic detection and segmentation of cracks in underground pipeline images
Image and Vision Computing
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
IEEE Transactions on Image Processing
Hi-index | 12.05 |
The essential work of sewer rehabilitation is a sewer inspection through diagnoses of sewer pipe defects. At present, image processing and artificial intelligence techniques have been used to develop diagnostic systems to assist engineers in interpreting sewer pipe defects on CCTV images to overcome human's fatigue and subjectivity, and time-consumption. Based on the segmented morphologies on images, the diagnostic systems were proposed to diagnose sewer pipe defects. However, the environmental influence and image noise hamper the efficiency of automatic diagnosis. For example, the central area of a CCTV image, where is always darker than the surrounding due to the vanishing light and slight reflectance, causes a difficulty to segment correct morphologies. In this paper, a novel approach of morphological segmentation based on edge detection (MSED) is presented and applied to identify the morphology representatives for the sewer pipe defects on CCTV images. Compared with the performances of the opening top-hat operation, which is a popular morphological segmentation approach, MSED can generate better segmentation results. As long as the proper morphologies of sewer pipe defects on CCTV images can be segmented, the morphological features, including area, ratio of major axis length to minor axis length, and eccentricity, can be measured to effectively diagnose sewer pipe defects.