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Added detailed mathematical equations for the Laplacian operator and its application in the detection of isolated points in image segmentation. Also included a function for determining isolated points based on response magnitude and threshold value. And added the section on the application of isolated point detection in X-ray image processing. |
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Segmentation methods can also be applied to edges obtained from edge detectors. Lindeberg and Li<ref>{{cite journal | last1 = Lindeberg | first1 = T. | last2 = Li | first2 = M.-X. | year = 1997 | title = Segmentation and classification of edges using minimum description length approximation and complementary junction cues | url = http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A473385&dswid=-8029 | journal = Computer Vision and Image Understanding | volume = 67 | issue = 1| pages = 88–98 | doi=10.1006/cviu.1996.0510}}</ref> developed an integrated method that segments edges into straight and curved edge segments for parts-based object recognition, based on a minimum description length (M<sub>DL</sub>) criterion that was optimized by a split-and-merge-like method with candidate breakpoints obtained from complementary junction cues to obtain more likely points at which to consider partitions into different segments.
== Isolated Point Detection ==
The detection of isolated points in an image is a fundamental part of image segmentation. This process primarily depends on the second derivative, indicating the use of the Laplacian operator. The Laplacian of a function <math>f(x, y)</math> is given by:
:<math> \nabla^2 f(x,y) = \frac{{\partial^2 f}}{{\partial x^2}} + \frac{{\partial^2 f}}{{\partial y^2}} </math>
The Laplacian operator is employed such that the partial derivatives are derived from a specific equation. The second partial derivative of <math>f(x, y)</math> with respect to <math>x</math> and <math>y</math> are given by:
:<math> \frac{{\partial^2 f(x,y)}}{{\partial x^2}} = f(x+1,y) + f(x-1,y) - 2f(x,y) </math>
:<math> \frac{{\partial^2 f(x,y)}}{{\partial y^2}} = f(x,y+1) + f(x,y-1) - 2f(x,y) </math>
These partial derivatives are then used to compute the Laplacian as:
:<math> \nabla^2 f(x,y) = f(x+1,y) + f(x-1,y) + f(x,y+1) + f(x,y-1) - 4f(x,y) </math>
This mathematical expression can be implemented by convolving with an appropriate mask. If we extend this equation to three dimensions (x,y,z), the intensity at each pixel ___location around a central pixel at (x, y, z) is replaced by their corresponding values. This equation becomes particularly useful when we assume that all pixels have unit spacing along each axis.
A sphere mask has been developed for use with three-dimensional datasets. The sphere mask is designed to use only integer arithmetic during calculations, thereby eliminating the need for floating-point hardware or software.
When applying these concepts to actual images represented as arrays of numbers, we need to consider what happens when we reach an edge or border region. The function <math>g(x, y)</math> is defined as:
:<math> g(x, y) = \begin{cases} 1 & \text{if } |R(x, y)| \geq T \\ 0 & \text{otherwise} \end{cases} </math>
This above equation is used to determine whether a point in the image is an isolated point based on the response magnitude <math>|R(x, y)|</math> and a threshold value <math>T</math>. If the response magnitude is greater than or equal to the threshold, the function returns 1, indicating the presence of an isolated point; otherwise, it returns 0. This helps in the effective detection and segmentation of isolated points in the image.<ref>Digital Image Processing (2007, Pearson) by Rafael C. Gonzalez, Richard E. Woods</ref>
== Application of Isolated Point Detection in X-ray Image Processing ==
The detection of isolated points has significant applications in various fields, including X-ray image processing. For instance, an original X-ray image of a turbine blade can be examined pixel-by-pixel to detect porosity in the upper-right quadrant of the blade. The result of applying an edge detector’s response to this X-ray image can be approximated. This demonstrates the segmentation of isolated points in an image with the aid of single-pixel probes.<ref>Digital Image Processing (2007, Pearson) by Rafael C. Gonzalez, Richard E. Woods</ref>
== Dual clustering method ==
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