Skin Lesion Classification - Biomedical Signal Analysis: Contemporary Methods and Applications - page 325

12

Skin Lesion Classification

This chapter describes an application of biomedical image analysis: the
detection of malignant and benign skin lesions by employing local in-
formation rather than global features. For this we will build a neural
network model in order to classify these different skin lesions by means
of ALA-induced fluorescence images. After various image preprocess-
ing steps, eigenimages and independent base images are extracted using
PCA and ICA. In order to use local information in the images rather
than global features, we first add self-organizing maps (SOM) to clus-
ter patches of the images and then extract local features by means of
ICA (local ICA). These components are used to distinguish skin cancer
from benign lesions. An average classification rate of 70% is achieved,
which considerably exceeds the rate obtained by an experienced physi-
cian. These PCA- and ICA-based tumor classification ideas have been
published in [21] and extend previous work presented in [19].

12.1 Biomedical Image Analysis

Many kinds of biomedical data, such as fMRI, EEG, and optical imaging
data, form a challenge to any data-processing software due to their high
dimensionality. Low-dimensional representations of these signals are key
to solving many of the computational problems. Therefore, principal
component analysis (PCA) commonly was used in the past to provide
practically useful and compact representations. Furthermore, PCA was
successfully applied to the classification of images [272]. One major de-
ficiency of PCA is its global, orthogonal representation, which often
cannot extract the intrinsic information of high-dimensional data.

Independent component analysis (ICA) is a generalization of prin-
cipal component analysis which decorrelates the higher-order moments
of the input in addition to the second-order moments. In a task such
as image recognition, much of the important information is contained
in the higher-order statistics of the image. Hence ICA should be able
to extract local feature like structures of objects, such as fluorescence
images of skin lesions. Bartlett demonstrated that ICA outperformed
the face recognition performance of PCA [18]. Finally, local ICA was