Fuzzy c-means clustering for noise reduction, enhancement and reconstruction of 3D ultrasonic images

This paper reports the application of artificial intelligence in the reconstruction of images from data acquired via ultrasonic sensors. These elements, placed to form an array of emitters-receivers, take data sequentially from different sections of a piece in movement on a conveyor belt. Taking into account the fuzziness (uncertainty) in the measured information, the use of fuzzy clustering algorithms, such as fuzzy c-means, should be of interest. As a comparison, non-fuzzy techniques, such as k-means are also applied, proving to be not as appropriate as the fuzzy alternatives. Another technique related to clustering, the chained distance algorithm, is implemented in order to define the number of regularities or classes in the image to be reconstructed, previous to the clustering. Finally, it is concluded that the use of fuzzy c-means clustering offers excellent results, giving noise-reduced, enhanced images, which are close to the real objects