Abstract:
This thesis is motivated by the potential gains that can be achieved by the use of
computer assisted decision systems (CAD) for diagnosis of melanoma in the skin
using dermoscopy. A CAD system provides quantitative and objective evaluation
of the skin lesion versus the subjective clinical assessment. It automates the skin
lesion analysis, and reduces the amount of repetitive and tedious tasks to be done
by physicians. This research is mainly focused on the computer vision perspective
to design a CAD system which will facilitate the physicians. A complete pattern
recognition system that includes three vital stages to conform the analysis of skin
lesions by the clinicians: segmentation, feature extraction and classification. The
data-set contains images and annotations provided by physicians.
Segmentation is an imperative preprocessing step for CAD system of skin lesions.
Segmentation is performed using active contours with creasness features. Feature
extraction of segmented skin lesions is a pivotal step for implementing accurate decision
support systems. Physicians are interested in examining a specific clinically
significant region in a lesion. Such a region is expected to have more information
in the form of texture that can be relevant for detection. In case of detection of
melanoma various local features for example pigment network and streaks usually
occur in peripheral region of the lesion. This led to the extraction of peripheral part
for feature extraction instead of whole lesion processing. We propose novel techniques
for feature extraction on peripheral part of the lesion using joint histogram of
multiresolution Local Binary Pattern along with the contrast of the patterns. Classification
results obtained from the proposed feature matrix were compared with
some other texture descriptors, showing the superiority of our proposed descriptor.
