Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

2006 ◽  
Vol 25 (4) ◽  
pp. 406-416 ◽  
Author(s):  
K. Suzuki ◽  
H. Abe ◽  
H. MacMahon ◽  
K. Doi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Processing Technique ◽  
Chest Radiographs ◽  
Image Processing Technique ◽  
Artificial Neural

scholarly journals Artificial Neural Network and Gray Level Co-Occurrence Matrix Based Automated Corrosion Detection

2019 ◽  
Vol 8 (6) ◽  
pp. 4499-4502
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Crack Detection ◽  
Gaussian Mixture ◽  
Image Processing Technique ◽  
Gray Level ◽  
Corrosion Detection ◽  
Artificial Neural ◽  
Occurrence Matrix

In this paper, we show an image processing algorithm with its capabilities in detecting the corrosion. This algorithm is programmed and requires no parameter modification and no previous knowledge of image acquisition process because function evaluates their parameters. Digital image processing technique proposed to avoid such incident occurrences. Combining Poisson-Gaussian- Mixture distribution with a Fuzzy segmentation framework an algorithm is developed to clutch image information. Artificial neural network and gray level co-occurrence matrix (GLCM) utilized to recognize the corrosion. The developed algorithm can be used in the ROV to detect the corrosion spots. The algorithm results exhibit the sufficiency in perceives corroded spots. Using image processing the corrosion detection process can be automated with a monitoring software setup which can generate an alert based on corrosion severity. Using image processing the infrastructure’s corrosion evaluation effort will be minimized, and presenting the result statistics is easier. In application point of view, we can extend the algorithm capabilities to the fatigue crack detection.

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