Edge Detection - Medical Image Processing: Advanced Fuzzy Set Theoretic Techniques - page 169

8
Edge Detect ion
8.1 Introduction
Edge detection is a fundamental low-level image processing. An edge is
a property of an individual pixel, which is calculated from the functional
behaviour of an image in its neighbouring pixel. Edge serves in simplify-
ing the analysis of images by drastically reducing the data to be processed
and preserving the useful structural information about object boundaries.
Edges in an image are the areas with strong intensity contrasts - a jump
in intensity from one pixel to the next pixel. There are many ways to per-
form edge detection, but the majority of methods may be grouped into two
categories: (a) gradient and (b) Laplacian. Gradient-based methods detect
the edges by looking at the minimum and maximum of the first deriva-
tive of the image. Laplacian method searches for the zero crossings in the
second derivative of the image in order to find edges. A number of edge
detection techniques may be found, but there is not a single method that
can detect the edges efficiently. Traditional edge detection methods such as
Robert, Sobel and Prewitt operator and Laplacian of Gaussian operator are
widely used. Canny proposed an optimal operator for edge detection. Marr
and Hildreth's [17] method is based on the zero crossing of the Laplacian
operator, which is applied to the Gaussian smoothed image. But in the
detection of the zero crossings in the second derivative, the maxima of the
gradient are also captured and give false edges. Most of the existing tech-
niques either are very sensitive to noise or do not give satisfactory results
in low-contrast areas.
The problem in general edge detectors is that they behave very poorly on
the medical image. The quality of edge detection is highly dependent on
lighting conditions, density of edges in the scene and noise. Each of them can
be handled by adjusting certain values of the edge detector to find threshold
values, but these become very tedious when dealing with medical images.
The edge of the medical image contains rich information about the image
boundary and background. This information is widely used in locating
important tissues, the study of anatomical features, etc. Also, an image may
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