2001
@phdthesis{Lee2001,
vgclass = {thesis},
author = {Tim Kam Lee},
title = {Measuring Border Irregularity and Shape of Cutaneous
Melanocytic Lesions},
school = {School of Computing Science, Simon Fraser University},
address = {Burnaby BC Canada V5A 1S6},
month = {January},
year = {2001},
abstract = {Cutaneous melanocytic lesions, commonly known as moles,
are mostly benign; however, some of them are malignant melanomas, the
most fatal form of skin cancer. Because the survival rate of melanoma
is inversely proportional to the thickness of the tumor, early
detection is vital to the treatment process. Many dermatologists have
advocated the development of computer-aided diagnosis systems for early
detection of melanoma.
One of the important clinical features differentiating benign nevi from
malignant melanomas is the lesion border irregularity. There are two
types of border irregularity: texture and structure irregularities.
Texture irregularities are the small variations along the border, while
structure irregularities are the global indentations and protrusions
that may suggest either the unstable growth in a lesion or regression
of a melanoma. An accurate measurement of structure irregularities is
essential to detect the malignancy of melanoma.
This thesis extends the classic curvature scale-space filtering
technique to locate all structure irregular segments along a
melanocytic lesion border. An area-based index, called irregularity
index, is then computed for each segment. From the individual
irregularity index, two important new measures, the most significant
irregularity index and the overall irregularity index, are derived.
These two indices describe the degree of irregularity along the lesion
border.
A double-blind user study is performed to compare the new measures with
twenty experienced dermatologists' evaluations. Forty melanocytic
lesion images were selected and their borders were extracted
automatically after dark thick hairs were removed by a preprocessor
called DullRazor. The overall irregularity index and the most
significant irregularity index were calculated together with three
other common shape descriptors. All computed measures and the
dermatologists' evaluations were analysed statistically. The results
showed that the overall irregularity index was the best predictor for
the clinical evaluation, and both the overall irregularity index and
the most significant irregularity index outperformed the other shape
descriptors. The new method has great potential for computer-aided
diagnosis systems.},
}