An Efficient Edge Detection Approach Based On Pollination Based Optimization

Edge detection of pictures is a vital task in computer vision and image processing. Edge detection is always study focus in the field of medical image processing and analysis. It is necessary step in medical image processing. Edge detection of noise free pictures is comparatively less complicated, however in most sensible cases the photographs area unit degraded by noise. Edges in photos provide low-level cues, which could be utilized in higher level processes, like object detection, recognition, and classification, furthermore as motion detection, image matching, and trailing. Edges and textures in image are typical samples of high-frequency information. High-pass filters deduct low-frequency image information and therefore enhance high-frequency information like edges. Many approaches to image interpretation measure supported edges. This paper proposed an enhanced edge detection using Pollination based optimization (PBO) algorithm. In this, The samples of medical images (MRI) with resolution 128×128 is given as input and output as edges of image is produced. All images are gray scaled and we converted all samples to same size (128×128). In this firstly add speckle noise then filter this image by using bilateral filter to make image noise free. A bilateral filter preserves sharp edges by systematically looping through each pixel and adjusting weights to the adjacent pixels accordingly. It extends the concept of Gaussian smoothing by weighting the filter coefficients with their corresponding relative pixel intensities. Then we use PBO for edge detection. PBO based edge detection is a new technique and it perform as well in medical field also and we used MRI images in our work. Keywords— Edge Detection, Medical field, MRI images, PBO, Bilateral Filter.