Dermoscopy is a noninvasive skin imaging technique that uses
optical magnification and either liquid immersion or
cross-polarized lighting to make subsurface structures more easily
visible when compared to conventional clinical images. It allows
for the identification of dozens of morphological features that are
particularly important in identifying malignant melanoma.
Dermoscopy Image Analysis summarizes the state of the art of the
computerized analysis of dermoscopy images. The book begins by
discussing the influence of color normalization on classification
accuracy and then: Investigates gray-world, max-RGB, and
shades-of-gray color constancy algorithms, showing significant
gains in sensitivity and specificity on a heterogeneous set of
images Proposes a new color space that highlights the distribution
of underlying melanin and hemoglobin color pigments, leading to
more accurate classification and border detection results
Determines that the latest border detection algorithms can achieve
a level of agreement that is only slightly lower than the level of
agreement among experienced dermatologists Provides a comprehensive
review of various methods for border detection, pigment network
extraction, global pattern extraction, streak detection, and
perceptually significant color detection Details a computer-aided
diagnosis (CAD) system for melanomas that features an inexpensive
acquisition tool, clinically meaningful features, and interpretable
classification feedback Presents a highly scalable CAD system
implemented in the MapReduce framework, a novel CAD system for
melanomas, and an overview of dermatological image databases
Describes projects that made use of a publicly available database
of dermoscopy images, which contains 200 high-quality images along
with their medical annotations Dermoscopy Image Analysis not only
showcases recent advances but also explores future directions for
this exciting subfield of medical image analysis, covering
dermoscopy image analysis from preprocessing to classification.
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