Based on Canny operator and mathematical morphology, Interpretationof image contents which is one of the objectives in computer vision specifically in image processinghas received much awareness of researchers. In image the partition of the image into object and background is a severe step. Segmentation separatesanimage in to itscomponent regions or objects. Image segmentation needs to segment the object from the background to read the image properly and identify the content of the imagecarefully has beendiscussed in thispaper. The image edge is estimated by the Canny algorithm and then mathematical morphology is usedfor enlargement which filled the fractureof the edge, withthat theinternalpore of theimage is filled. The two significant features of this method are introduction of NMS (Non-Maximum Suppression) anddouble thresholding of the gradient image. Due to poor illumination, the region boundaries in an image may become vague, creating uncertainties in the gradient image. In this paper, we have proposed an algorithm based on the concept of fuzzy sets to handle uncertainties that automatically selects the threshold values needed to segment the gradient image using classical Canny's edge detection algorithm. The results show that our algorithm works significantly well on different benchmark images aswell asmedical images. Finally, smoothing isoperated by mathematical morphology.
Edge detection, Canny algorithm, differential operator, mathematical Morphology, computer vision, Image segmentation
