Amr R. Abdel-Dayem and Mahmoud R. El-Sakka, "Coarse
Segmentation of Suspicious Tissues in Digital Mammogram Images
using Bayesian-Based Threshold Estimation", International
Journal for Computational Vision and Biomechanics, Vol. 3,
No. 1, pp. 41-59, 2010.
In this paper, we propose a coarse segmentation scheme for
highlighting suspicious lesions in digital mammogram images.
The proposed scheme is intended to be used in a multi-stage
segmentation paradigm for accurate localization of suspicious
masses. The major objective of the proposed scheme is to
reduce the search space when further stages search for
abnormalities. The proposed scheme uses the image histogram to
estimate the Bayes threshold that can segment suspicious
lesions from normal breast tissues with minimum probability
of classification error. We also present a block-based measure
that can objectively assess the computer-segmented images,
compared with the clinician-segmented ones. Experimental
results over a set of sample images (consists of 50 normal and
50 abnormal cases) showed that the proposed scheme produces
accurate highlighting results, compared with the manual
results produced by clinicians. It achieves a true positive
fraction, a precision and an overlap ratio of 1.0 for the
entire fifty abnormal cases (when used in the screening mode).
Meanwhile, the 95% and the 99% confidence intervals for the
false positive fraction, calculated over the fifty normal
cases, are [0.017, 0.183] and [0, 0.209], respectively (when
used in the screening mode).
When the proposed scheme is used in diagnosis or follow up
mode, we used our block-based measure with 32×32 block size to
report the performance of the system. The results shows that
the 95% and 99% confidence intervals (calculated over the
fifty abnormal images) for the true positive fraction are
[0.842, 0.938] and [0.827, 0.953], for the false positive
fraction are [0.101, 0.203] and [0.084, 0.219], for the
precision are [0.538, 0.691] and [0.514, 0.715], and for the
overlap ratio are [0.483, 0.623] and [0.461, 0.645],
respectively. Meanwhile, the 95% and 99% confidence intervals
for the false positive fraction (calculated over the fifty
normal images) are [0.002, 0.078] and [0, 0.09], respectively.
However, if we consider all hundred images together, the 95%
and 99% confidence intervals for the false positive fraction
are [0.062, 0.130] and [0.052, 0.140], respectively.
It is worth mentioning that, the output produced by the
proposed scheme represents preliminary estimates that will be
fine-tuned using more advanced stages that employ both pattern
classification and artificial intelligence techniques