Image Processing Reference
In-Depth Information
Analysis of Performance
Consider Figure 3.12 that shows box plots (Tukey, 1977) which graphically depict the LCE
values corresponding to the segmentation achieved by bilevel thresholding using algorithms
(i) to (vii) mentioned earlier in this Section. A box plot, which in Figure 3.12 summarizes
the LCE values obtained corresponding to all the segmentation ground truths available for
an image, is given for all the 100 images considered. We find from the box plots that the
LCE values corresponding to the algorithms (i), (ii), (v) and (vii) are in general smaller
compared to that corresponding to the other algorithms. It is also evident that algorithms
(ii) and (vii) perform almost equally well, with algorithm (ii) doing slightly better.
From all the box plots in Figure 3.12, we find that segmentation results achieved by
algorithms (i) and (v) are equally good and they give the best performance among the
algorithms considered. The average of all the LCE values obtained using an algorithm is
minimum when algorithm (v) is used. However, maximum number of zero LCE values
are obtained when algorithm (i) is used. Hence, we say from quantitative analysis that
algorithms (i) and (v) are equally good and they give the best segmentation results.
3.6
Conclusion
In this chapter, image thresholding operations using rough set theory and its certain general-
izations have been introduced. Classes of entropy measures based on generalized rough sets
have been proposed and their properties have been discussed. A novel image thresholding
methodology based on grayness ambiguity in images has then been presented. For bilevel
thresholding, every element of the graylevel histogram of an image has been associated with
one of the two regions by comparing the corresponding errors of association. The errors of
association have been based on the grayness ambiguity measures of the underlying regions
and the grayness ambiguity measures have been calculated using the proposed entropy mea-
sures. Multilevel thresholding has been carried out using the proposed bilevel thresholding
method in a binary tree structured algorithm. Segmentation and edge extraction have been
performed using the proposed image thresholding methodology. Qualitative and quantita-
tive experimental results have been given to demonstrate the utility of the proposed entropy
measures and the effectiveness of the proposed image thresholding methodology.
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