An Adept Edge Detection Algorithm for Human Knee Osteoarthritis Images

Abstract: 
In this paper, it is discussed the Sobel edge detection operator and its enhanced algorithm.
It is implemented a competent execution time for this new enhanced algorithm to detect edges for human knee osteoarthritis images in different critical situations.
The proposed method is able to exhibit discernible view of salient features of most osteoarthritis images with approximately 50% better execution time compare to classical Sobel method.
Also, it is shown that the algorithm is very effective in case of noisy and blurred images. 
Existing System:
The traditional edge detection methods are such as
. Log operator
. Sobel operator
. Canny operator
. Gradient operator.
But a large number of digital image processing results show that these pairs of directional edge detection operators are more sensitive, anti-noise ability is poor and generally difficult to obtain satisfactory test results.
Proposed system:
In our method, it has been shown edge detection method for knee osteoarthritis images using classical Sobel and proposed an improved modified Sobel algorithm.
The proposed method is able to exhibit discernible view of salient features of most osteoarthritis images with approximately 50% better execution time compare to classical Sobel method.
SOBEL OPERATOR:
The Sobel operator is widely used in image processing particularly within edge detection algorithms.
The Sobel operator consists of a pair of 3x3 convolution kernels.
It is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function.
SOBEL EDGE DETECTION OPERATOR:
Classic Sobel Edge Detection Operator:
The Sobel operator is widely used in image processing, particularly within edge detection algorithms.
Technically, it is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function.
At each point in the image, the result of the Sobel operator is either the corresponding gradient vector or the normal of this vector.
Sobel operator is the partial derivative of f (x, y) as the central computing 3x3 neighbourhood at x, y direction.
In order to suppress noise, a certain weight is correspondingly increased on the centre point.
Improved Sobel Edge Detection Operator:
The Sobel operator is based on convolving the image with a small, separable, and integer valued filter in horizontal and vertical direction.
It is relatively inexpensive in terms of computations.
On the other hand, the gradient approximation which it produces is relatively crude, in particular for high frequency variations in the image.