Hardware implementation of fast bilateral filter and canny edge detector using Raspberry Pi for telemedicine applications

The role of preprocessing and segmentation are vital in image processing and computer vision. The medical images are prone to noise and the filtering algorithms are used for noise removal. In this paper, the fast bilateral filter is employed for noise removal and it has good edge preservation capacity. The segmentation algorithms are used to extract the region of interest and edge detection is a classical algorithm for tracing the contours of objects in an image. The canny edge detector is efficient when compared with the conventional edge detectors. The fast bilateral filter is proposed in this paper has the computation complexity of O(1) per pixel, while the classical bilateral filter has the computation complexity of O(W) operations per pixel, where W is the kernel size. The algorithms were implemented in Raspberry Pi using Open CV software package. The algorithms were tested on real time medical images.

Automated summary

This paper highlights the importance of preprocessing and segmentation in medical image processing, as well as the application of the fast bilateral filter and edge detection algorithms. The fast bilateral filter has good noise removal and edge preservation capabilities, while the Canny edge detector is more efficient than traditional edge detectors.