Measuring Biological Matter_2012

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What is biological matter? It is group of atoms (matter) that has (or had) the property of being alive.

We don't go in the definition of wich mean "being alive", but is a common notion, learned by examples.

Some examples are

A tree

A cat

A cell

A tissue

Christian Lovato

Measuring biological mattersabato 24 aprile 2010

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Info-Med.one (On 19-01-2011) Page 1


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Measure:

The quantity,size,weight,distance or capacity of a substance compared to a designated standard.

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we can say "give a number (or a set of numbers) indicating something"

Information theoryInformation theory recognizes that all

data are inexact and statistical in

nature. Thus the definition of

measurement is: "A set of observationsthat reduce uncertainty where the

result is expressed as a quantity."[3].

This definition is implied in what

scientists actually do when they

measure something and report both the

mean and statistics of the

measurements. In practical terms, one

begins with an initial guess as to the

value of a quantity, and then, using

various methods and instruments,

reduces the uncertainty in the value.

Note that in this view, unlike the

positivist representational theory, all

measurements are uncertain, so instead

of assigning one value, a range of values

is assigned to a measurement. This also

implies that there is a continuum

between estimation and measurement.

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Measure of an artifact

With some instruments

We can define a diameter, an height, without ambiguities.

After, we can answer some questions like:

how many mugs can enter in this box?

A mug

We obtain a set of

Informative (useful)

measures

A human

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in the biological domain the situation is usually different.

We can measure as we do with artifacts, but we have a lot of problems, for example in defining what

and how to measure. In most cases biological systems cannot "fit" into a determined number of

measures.

For example one of the common measures of the body is stature. Measuring height is simple. A little bitmore complicated than measuring the height of a mug but we can obtain a number that gives the

stature of a person. However there are some problems once we want to use this number.

For instance, there is a problem with this number's precision. We know that the stature of a person

changes during life, so that the temporal validity of this number is limited. And if we want to be more

precise, the height of a person decrease of about 1% during the daytime and increase during the sleep.

[http://www.informaworld.com/smpp/content~db=all~content=a785169438 ]

Another order of problems that emerges is about the definition of the measure, i.e. how to deal with

hairs during the measurement: if we compress them we obtain a certain number, if we don't we have

another, and if we cut them and measure on the scalp we have yet another measurement.

None of them is the "real" height, every measure has a different biological meaning, if not a clinic one.

All we have said about stature is valid for almost all the measures we can think to perform.

These problems derive, as last resort, from the fact that biological systems (and thereby shapes) are

complex, and we don't have a well defined geometrical, or mathematical, model of what a "human

body" is.

While we can have few ambiguities in measure and use the measures of the mug, we really have a lot of

ambiguities in measuring, and in using the measures of biological systems, because of their complexity.

How to approach this complexity? Or, more simply, how to get objective measurements of biological

systems?

Firstly, we need a model of the human body. Every measure is

related to a model that express some of the relationships of the

subject.

While for the measures of a mug we can simply define one (the

measure of "diameter" is strongly connected with the notion ofthe "cylinder" that forms the geometrical model of the mug) in

the biological case, this model is more complex and should be

explicitly defined, for every set of measures we want to obtain.

Secondly, we can manage the complexity of the "human body", and of the model that should be

derived from it, by means of successive refinements. We can start from simple general models, and

"deepen" them into more complex and precise models that use the information obtained from the

simpler ones, in a process can be compared to the successive approximation of an optimization

algorithm.

These considerations lead to a general framework in wich a such model can be constructed, and in wich

The model is usually in the mind of

the operator, and must be shared

with the agent that uses the

measurement.

Different people have usually similar

(not equal) models of the same

subject.

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the notion of successive approximation is implemented.

We can expand this framework by including new data sources. We can also decide to set up interactive

measures for those blocks for which automatic measures are not readily avaiable, or the implemented

algorithms fail for some specific data.

Other interesting features and properties of the method can be:

Substitution of algorithms when better (faster, or more robust) algorithms become avaiable, alterating

only a block, with eventual minor changes in neighbours blocks

Measures on request: if an automatic pipe is heavy to process as a whole, only the needed blocks can be

activated (backpropagation of request)

Associate to every measure some indicative label (from medical domain )

Reusability of well-behaving algorithms within the system at different levels/scales, where previous

stages provide the context to deeper ones. This frame fits naturally in a multirisolution approach, i.e.

different levels could use data at different resolutions.

In this very general frame we take row data as input, preprocess them in order to achieve a good

precision against acquisition errors, and process them at different stages, or levels.

At every stage we refine the explicit information about the particular examined dataset .

The key is that we can use information extracted at early levels to simplify (make more efficient, faster,

simpler to implement) the tasks of the deeper levels.



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In general, strenght of this approach is the good coupling it can give between "medical knowledge" and

the implemented model, because the measures are guided from the medical needings, and the

representation can give useful medical informations at all levels of frame, from the simplest ones (that

give general information) to the deeper levels, that extract more specific or contextualized informations.

This feature is necessary in order to obtain immediatly useful and informative measures of the subject.

What's already done at level 1

Example (our anthropometric application)



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Improvements:

allows analysis of fine structures; i.e. hands, that in the original mesh has motion artifacts

Advanced hole closing -> refinement in volume measurement

Better mesh preprocessing ->

Add algorithms for interesting points on mesh surface, in order to make segmentation more robust, or

make it work in presence of topological variations due to different subject poses

Planning to improve the stick fitting algorithm.

Measuring Biological Matter_2012

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