Cytopathological cancer diagnoses can be obtained less invasive than histopathological investigations. Cells
containing specimens can be obtained without pain or discomfort, bloody biopsies are avoided, and the diagnosis
can, in some cases, even be made earlier. Since no tissue biopsies are necessary these methods can also be used
in screening applications, e.g., for cervical cancer. Among the cytopathological methods a diagnosis based on
the analysis of the amount of DNA in individual cells achieves high sensitivity and specificity. Yet this analysis
is time consuming, which is prohibitive for a screening application. Hence, it will be advantageous to retain, by
a preceding selection step, only a subset of suspicious specimens. This can be achieved using highly sensitive
immunocytochemical markers like p16ink4a for preselection of suspicious cells and specimens.
We present a method to fully automatically acquire images at distinct positions at cytological specimens
using a conventional computer controlled microscope and an autofocus algorithm. Based on the thus obtained
images we automatically detect p16ink4a-positive objects. This detection in turn is based on an analysis of the
color distribution of the p16ink4a marker in the Lab-colorspace. A Gaussian-mixture-model is used to describe
this distribution and the method described in this paper so far achieves a sensitivity of up to 90%.
Compared to histopathological methods cancer can be detected earlier, specimens can be obtained easier and
with less discomfort for the patient by cytopathological methods. Their downside is the time needed by an expert
to find and select the cells to be analyzed on a specimen. To increase the use of cytopathological diagnostics,
the cytopathologist has to be supported in this task.
DNA image cytometry (DNA-ICM) is one important cytopathological method that measures the DNA content
of cells based on the absorption of light within Feulgen stained cells. The decision whether or not the patient has
cancer is based on the histogram of the DNA values. To support the cytopathologist it is desirable to replace
manual screening of the specimens by an automatic selection of relevant cells for DNA-ICM. This includes
automated acquisition and segmentation of focused cells, a recognition of cell types, and a selection of cells to
be measured. As a step towards automated cell type detection we show the discrimination of cell types in serous
effusions on a selection of about 3, 100 manually classified cells. We present a set of 112 features and the results
of feature selection with ranking and a floating-search method combined with different objective functions. The
validation of the best feature sets with a k-nearest neighbor and a fuzzy k-nearest neighbor classifier on a disjoint
set of cells resulted in classification rates of 96% for lymphocytes and 96.8% for the diagnostically relevant cells
(mesothelial+ cells), which includes benign and malign mesothelial cells and metastatic cancer cells.
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