International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B5. Istanbul 2004
Fig. 3: GIT
[Immune fluorescence imaging the probes in a confocal
microscope, the red channel was used to image the protein, and
the green channel to image the reference organelles, i.e. the cell
nucleus membrane and the golgi apparatus. Fig. 4 shows an
example of the colocalizations becoming visible.
Fig. 4: Images of a cell prepared to show Huntingtin: the
protein (or its marker HDI) in red, the reference
organelles (markers Lamin and Golgin97) in green
and the color image showing colocalizations (from
left to right).
For each protein to be investigated ten images of different cells
were acquired. In order to explore the statistic behaviour of the
visual appearance, ie. the feature vector of a protein, two
visually similar proteins, Huntingtin and GIT (Figs. 2 and 3)
were imaged 100 times. Half of the images were used as
training data, the other half as test data.
The features to be used for classification are for instance
statistical measures describing protein localisation inside of the
nucleus, e.g. variance and entropy, edge segments appearing
inside and outside of the nucleus, and the visibility of the golgi
apparatus being attached to the nucleus. If these “features” are
no numerical values directly, they have to be transformed into
numeric measures such as edge length or strength. As the
occurrence of the protein inside of a cell is a natural event more
or less varying statistically, the statistic behaviour of the
extracted features is of major importance for the performance of
the method and, therefore, has to be taken into account by the
algorithm, e.g., by using the probability density distributions of
the features for classification.
The feature vector actually used includes the following features;
c.f. (STECKLING & KLÓTZER, 2003; STECKLING et al., 2003):
I. White pixels: number of pixels whose grey value is
greater than the average of all grey values of the
image.
2. White segments: number of image segments fulfilling
the same condition. À segment is defined as a four-
connected neighbourhood (BOLAND & MURPHY,
2001).
J
four-connected pixels with grey values lower or equal
than the average of all grey values of the image
(BOLAND & MuRPHY, 2001).
4. Expectation value:
] K
m=E(x)=— > x, (1)
= 2x
k=l
(BOLAND & MURPHY, 2001).
5. Energy: second angular moment
N,
> Ps (f (2)
i=0
where x and y are the coordinates (row and
column) of an entry in the co-occurrence matrix,
and p,(i) is the probability of co-occurrence
matrix coordinates summing to x+y
(BOLAND & MURPHY, 2001; HARALICK ct al., 1973).
6. Difference entropy:
S a
- VY p, G)log[p, .()] G3)
i=0
(BOIAND & MURPHY, 2001; HARALICK ct al., 1973).
7 Lines: number of segments extracted with a line
extraction method.
4. CLASSIFICATION
A maximum likelihood classification was used. To illustrate the
separability of the clusters of two visually similar proteins
based on the feature vector defined in the previous section, Fig.
5 shows the sub-space of the three most informative features.
3. Black segments: number of segments consisting of
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