New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...

2.2. AN EXAMPLE 21
successes. We call the percentage of successes the quality (e.g. 30 correct
labels out of 50 pictures yields a quality of 60%).
Step 5: Compile a fingerprint
Out of the list of features from the previous step take all features having a
quality value above 95%. We call this resulting list of features a fingerprint. To
determine the type of a pile using such a fingerprint, we perform the decision
as described in step 4 for each feature of this fingerprint one after another.
Each decision is treated as a vote for opera-A type or opera-B type. The type
with the most votes wins and is taken as the final result.
Remarks
Of course this example and the recipe are quite simple and sketchy. We omitted
many details, background information and occurring problems - for example,
how do we handle pictures with missing areas ? And, admittedly, it is not
very common that one needs to tell apart two piles of Lego bricks on the basis
of fuzzy pictures.
However, in the next chapters we will see how similar these problems,
questions and eventually concepts and solutions are in the actual field of this
thesis: the analysis of groups of mass spectra images resulting from human
blood samples. We will show how to use, extend and improve these concepts.
2.2.1 Term Comparison
This table shows a comparison of terms used in the Lego example and the
according terms used in later chapters.
Term in Lego example Term used later
Pile picture Mass spectrum
Storage building Hard disk drive
Lego brick producing machine Mass spectrometer
Camera Time-of-flight measurement
Opera type (A and B) Patient group (e.g. healthy or diseased )
Brick Peak
Brick position Peak center
Brick color Peak area and shape
Brick border Peak shape
Feature Feature
Fingerprint Fingerprint