\chapter{Microscopic analysis of biological particles}	\label{sec:microana}
\thispagestyle{empty}

Microscopic analysis is used in many fields of technology and physics, for
example in aerobiology, geology, biomedical analysis, industrial product inspection.\\
Basically, the input image has a resolution of about 1024x1024 pixels while objects of interest have resolution of about 50x50 pixels. In all these applications detection and classification are difficult, because of the poor resolution and maybe strong variability of objects of interest, and because the background can also be very noisy and highly variable. In this work, we focus on recognition of biological particles and especially on recognition of airborne pollen and on recognition of particles that can be found in urine. Since our approach is general and our performance is very good on many categories of different kinds of corpuscles, we can claim that this research and its results are naturally applicable to other many kinds of biological particles found in microscopic analysis. In this research, we develop a recognition system that is not a specific case study, but it is segmentation free and it can be easily updated in order to deal with new classes.\\
Focusing in the next sections on airborne pollen recognition and urinalysis, we will explain why and how these analyses are performed, what the problems of manual analysis are, and at the end, we will assess the need to automate this
process.

\section{Measuring airborne pollen level in air} \label{sec:airbonepollana}

Estimates from a skin test survey suggest that allergies affect as many as 40 to 50 million people in the United States of America. Allergic
diseases affect more than 20\% of the U.S. population and are the sixth leading cause of chronic disease. In this country, the estimated
overall costs for allergic rhinitis in 1996 totaled 6 billion dollars and two years later, the increased absenteeism and reduced productivity due to
allergies cost to companies more than 250 million dollars \cite{sito:aaaai}. Moreover, from 1982 to 1996, the prevalence of asthma increased to 97\%
among women and 22\% among men. These statistics are an example that perfectly corresponds to the situation and trend in all other
countries in the world. \\
An allergy is an abnormal reaction to an ordinarily harmless substance called \emph{allergen}, \cite{sito:aaaai} \cite{libro:pollini}.
 When an allergen is
absorbed into the body of an allergic person, the person's immune system views the allergen as an invader and a chain of abnormal reactions begins.
The effects of this response are runny nose, watery eyes, itching and sneezing. People with these symptoms are unable to work and even to
sleep. The most common allergens are: pollen particles, molds, dust
mites, animal dander, foods, medications, insect stings. Pollen grains are clinically the most important outdoor allergens and the most allergenic
species are: \emph{American elm}, \emph{paper birch}, \emph{red alder}, \emph{white oak}, \emph{white ash}, \emph{olive}, \emph{mulberry},
\emph{pecan}, \emph{black walnut}, \emph{sycamore}, \emph{grass}, \emph{chenopod}, \emph{rumex}, \emph{plantago}.
 Note that asthma and allergies can affect anyone, regardless to age, gender, race.

In order to make forecasts and to aid in the diagnosis, treatment and management of allergic diseases, many
stations with air sampling equipment are spread in the territory to collect airborne pollen particles.\\
The most popular device is the \emph{volumetric spore trap}, see \figref{burkard2}, which draws air into the sampler at a given rate through a little opening. The
particles in the air land on an adhesive coated microscope slide attached to a slowly rotating wheel. 
After a period of sampling, for example one day but most usually one week,  
the pollen grains caught on the sticky slides are counted under the microscope by simply cutting the
long slide into several pieces. The analysis at the microscope is performed by trained observers (aerobiologists) who count and 
classify pollen grains.
This method is slow, expensive, and inaccurate. First of all: the response time
 is inadequate for many applications. 
In a typical installation pollen particles are collected during the week on sticky tape. The tape is analyzed only once a week. 
The results of such analysis are therefore sometimes available one week after the fact, rendering them useless for preparations of medical
 response in hospitals.\\
   Second, the analysis of one weekly tape takes up to 8 hours of work by a skilled professional, thus, the yearly cost of measuring pollen 
   contents in the air at one location could approach \$30,000, too expensive for many institutions 
and too expensive to allow fine spatial sampling of air pollen contents.\\ 
For example, the National Allergy Bureau, section of the American Academy of Allergy, Asthma and Immunology's Aeroallergen Network, is the
institution responsible for reporting current pollen count to the media and it has \emph{only} 75 counting stations throughout the US and its
members are all \emph{volunteers}; if you check the website of AAAAI \cite{sito:aaaai} the pollen counts are never updated to the last
week!\\
Most importantly: relying on humans produces inaccurate 
measurements. This for two reasons: first, the process is tedious, it is well documented that the attention of a human operator tends to 
flag after 30 minutes on a demanding repetitive job. Second,  in order to accomplish the task at all, human operators sample coarsely the 
collected tapes. Measurements are thus accurate for high pollen counts, but inaccurate for low pollen counts, an even more inaccurate 
as to estimating the concentration of pollen grains over time. It is entirely possible to miss the presence of a given pollen in the air if the 
critical time when it was released is not sampled by the operator. 
Moreover, it is difficult to provide accurate pollen levels for areas not near to a counting station and so the actual counts are useless
for most of the physicians.

\begin{figure}[tbp]
\centering
\includegraphics[width=0.8\textwidth, height=0.6\textwidth]{./figure/burkard.eps}
\caption{Burkard volumetric spore trap is used to collect airborne pollen grains and fungi.}
\label{figure:burkard2}
\end{figure}


To summarize, there are many reasons to justify the recent strong interest and the need to automate the count and identification of 
airborne pollen particles.
The manual collection and analysis is not adequate because it is too slow, too expensive, not precise and not able to cover all the territory. 

\section{Urinalysis}

Urinalysis can reveal diseases that have gone unnoticed because they do not produce striking signs or symptoms; examples are diabetes
mellitus, various forms of glomerulonephritis and chronic urinary tract infections \cite{sito:urine}. There are three kinds of urinalysis:
macroscopic, chemical and microscopic. The first is direct to visual observation to assess the color and cloudiness. The second one, uses a
dipstick to determine pH, specific gravity, content of proteins, glucose, etc. and it is based on the color change of the strip and on its
comparison to a color chart. The third analysis, the one of our interest, requires a light microscope and is the most complex. A sample of
well-mixed urine is centrifugated in a test tube and then, a drop of sediment is poured onto a glass slide for the examination. Using low
magnification, a well-trained expert identifies crystals, casts, squamous cells and other ``large'' objects. After another adjustment of the
microscope to get a higher magnification, the expert can count the number of smaller objects like bacteria and red blood cells. 

Particles in urine can be classified into 12 categories, they are: red blood cells, white blood cells,
bacteria, hyaline casts, pathological casts, crystals, squamous epithelial cells, non squamous epithelial cells, yeasts, white blood cell
clumps, sperm and mucus. The microscopic analysis is very useful and generally required because it is non-invasive and it gives 
several indications about disease progress and therapeutic efficacy, \cite{depliant:IRIS}. 

However, this kind of analysis if manually done is intrinsically not precise, time consuming and expensive.
There is no standardization in the process of taking a volume of fluid, there is no reliability of the result because the experts may have a
different training and experience and last but not least, this work is annoying because repetitive and difficult. 
The difficulty relies on the strong similarity
among some categories of particles and in the variability existing also among corpuscles belonging to the same family.\\
Moreover, this process is slow and expensive for hospitals. \\

For these reasons, an automatic recognition system is required in several situations above all when many specimens have to be analyzed. Some
systems for automatic recognition of particles in urinalysis are currently sold. An interesting machine using computer vision approach is made
by IRIS, a company in the field of biomedical analysis. This company also provided us the urine database. 

Our interest in urinalysis is justified because the manual analysis is not efficient in terms of precision, cost and time and because the
automatic recognition can be still improved. In the next chapter, we will describe IRIS system and its very good
performance but we will highlight also its main flaws.\\
Other systems using different techniques, such as analysis of particle refraction when these particles are hit by a laser beam, have also drawbacks
because of their suboptimal performance and the difficulty to verify analysis outcomes.

\section{Conclusion}

Both aerobiology and urinalysis need automation. \\
First, manual analysis is slow. For instance, the pollen level in the air is usually given in the following week and so, physicians have available this information when by this time it is too late. 
Second, manual analysis requires
very skilled experts with a high cost for the community and the institutions. Third, because this kind of work is repetitive and difficult,
results may be not accurate.  
Experts' results depend on their experience, their subjective judgment, their way to set up the system. There is a need of standardization in the measurements that can be achieved only with an automatic system.

On the other hand, today there is no completely automatic system to detect and identify pollen particles and the machines for urinalysis have a lot of
margin of improvement.

Besides, visual recognition of particles in microscopic images and more generally, recognition of small object categories in images with
poor resolution and contrast is a field of research which has never been studied deeply but nonetheless, has many challenging problems to
tackle and can find several applications.  



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