@mastersthesis {626, title = {High-Level Intuitive Features (HLIFs) for Melanoma Detection}, year = {2013}, pages = {85}, type = {M.A.Sc. Thesis}, abstract = {

Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Existing feature sets combine many ad-hoc calculations and are unable to easily provide intuitive diagnostic reasoning. This thesis presents the design and evaluation of a set of features for objectively detecting melanoma in an intuitive and accurate manner. We call these {\textquoteleft}{\textquoteleft}high-level intuitive features\&$\#$39;\&$\#$39; (HLIFs).

The current clinical standard for detecting melanoma, the deadliest form of skin cancer, is visual inspection of the skin\&$\#$39;s surface. A widely adopted rule for detecting melanoma is the {\textquoteleft}{\textquoteleft}ABCD\&$\#$39;\&$\#$39; rule, whereby the doctor identifies the presence of {\bf A}symmetry, {\bf B}order irregularity, {\bf C}olour patterns, and {\bf D}iameter. The adoption of specialized medical devices for this cause is extremely slow due to the added temporal and financial burden. Therefore, recent research efforts have focused on detection support systems that analyse images acquired with standard consumer-grade camera images of skin lesions. The central benefit of these systems is the provision of technology with low barriers to adoption. Recently proposed skin lesion feature sets have been large sets of low-level features attempting to model the widely adopted ABCD criteria of melanoma. These result in high-dimensional feature spaces, which are computationally expensive and sparse due to the lack of available clinical data. It is difficult to convey diagnostic rationale using these feature sets due to their inherent ad-hoc mathematical nature.

This thesis presents and applies a generic framework for designing HLIFs for decision support systems relying on intuitive observations. By definition, a HLIF is designed explicitly to model a human-observable characteristic such that the feature score can be intuited by the user. Thus, along with the classification label, visual rationale can be provided to further support the prediction. This thesis applies the HLIF framework to design 10 HLIFs for skin cancer detection, following the ABCD rule. That is, HLIFs modeling asymmetry, border irregularity, and colour patterns are presented.

}, author = {R Amelard} }