Engineering, 2013, 5, 404-408 Published Online October 2013 (
Copyright © 2013 SciRes. ENG
Research of Segmentation Algorithm s for Overlapping
Wenzhong Yan, Lei Bai
Department of Computer, North China Institute of Science and Technology, Beijing, China
Received 2013
Chromosome segmentation is the most important step in the automatic chromosome analysis system. Since in almost
every metaphase image partial touching and overlapping of chromosomes are a common phenomenon, how to separate
these chromosomes correctly is a difficult yet vital problem. A number of attempts have been made to deal with this
problem. This paper is focused on these attempts. Some algorithms are investigated. The principle and the realization of
these algorithms are analyzed. Results of these algorithms are compared and discussed.
Keywords: Chromosome; Segmentation; Overlapping; M-FISH
1. Introduction
Human chromosome analysis is an essential task in cy-
togenetics, especially in prenatal screening and genetic
syndrome diagnosis, cancer pathology research and en-
vironmentally induced mutagen dosimetry [1]. A com-
puter-assisted system usually includes four processing
steps: 1) image enhancement, 2) chromosome segmenta-
tion (detection) and alignment, 3) feature computation
and selection and 4) chromosome classification [2]. In
these steps, chromosome segmentation is the most im-
portant one because this step affects the performance of
these systems.
Since in almost every metaphase image partial touch-
ing and overlapping of chromosomes are a common phe-
nomenon (Figure 1 shows an example of overlapping
chromosomes), finding solutions for automated separa-
tion of overlapping chromosomes is difficult yet vital.
Early studies found that a number of automated classifi-
cation systems were somewhat successful in karyotyping
the chromosomes under favorable imaging conditions.
The typical case error rate was approximately 20% [3]. If
the chromosomes were touching, overlapping or deformed,
the classification error rate was substantially increased
In order to solve the problems above, a number of at-
tempts have been made to deal with segmentation of
overlapping chromosomes. This paper is focused on these
attempts. Some segmentation algorithms are investigated.
The principle and the realization of these algorithms are
analyzed. Results of these algorithms are compared and
2. Segmentation Algorithms
A novel recursive searching algorithm was developed
and implemented in an automated karyotyping system
(AKS). The goal of this system is to label the chromo-
somes from a metaphase image with minimal human
intervention. It is unique in that it is designed to auto-
matically process cells containing overlapping chromo-
some. It also has four novel algorithmic features: the use
of cross section sequence graphs, an over-segmentation-
based segmentation strategy, the use of the “pale path”
concept for banded chromosomes and the application of
mathematical programming for cell-level chromosome
classification in cells with overlapping chromosomes.
The algorithm was tested using 29 cells (including 397
chromosomes) and achieved 82% accuracy. Three types
of skeleton errors were found, which were missing frag-
ments in the skeleton, extr a -fragments in the skeleton and
hole information [5].
Figure 1. Example of overlapping chromosomes.
This research is sup ported by the Funda mental Research Fund s for the
Central Universities (2011A010).
Copyright © 2013 SciRes. ENG
A new technique to separate overlapping chromosomes
based on computational geometry is developed. This
technique requires the identification of all possible cut
points from the contour line of overlapping chromosomes,
using Voronoi diagrams and Delaunay triangulations to
select the four target cut points and cut overlapping
chromosomes into two chromosomes. This algorithm is
tested on 35 overlapping chromosome images and find
that 28 out of 35 overlapping chromosomes images can
be separated correctly (i.e. 80.0%).Three out of the 35
images are separated incorrectly (i.e. 8.6%) and four out
of 35 images are not separable by our algorithm (i.e.
11.4%). Figure 2 shows some results of the separation
One research presented an algorithm that is able to
automatically identify chromosomes in metaphase im-
ages. Researchers take care of a first segmentation step
and then of the disentanglement of chromosome clusters
by resolving separately adjacencies and overlaps with a
greedy approach, which ensures that at each step only the
best split of a blob is performed. The performance of this
method is better or comparable to the best of other me-
thods reported in the literature. This method to simulta-
neously tackle segmentation, overlaps and adjacencies
with performance on each task higher than 90%, hence
providing a tool able to automatically analyze an image
and can be handed over wit minimal human intervention
to a classifier for automatic karyotyping [7].
Another research is aimed at designing a classifier that
its input is an image and its output is decision of being
(a) (b)
(c) (d)
Figuer 2. Separation overlapping chromosome images. (a)
Original image of two overlapping chromosomes; (b) a
Bounded Voronoi diagram of possible cut points; (c) chro-
mosome 1; and (d) chromosome 2.
mul ti-chromosome or one-chromosome image. Since the
gray-scale level of image has no effect on decision of
human, researchers convert the images to binary. The
output of classifier has two conditions that can be showed
with one bit. Therefore, if the classifier output is “zero”,
the image is one-chromosome and if it is “one”, the im-
age is multi-chromosome image. If one wants to use a
classifier except neural network classifier, he should de-
fine a procedure for feature extraction. But if one uses
neural network classifier, he doesn’t need to define fea-
tures for neural network. When a neural network is learn-
ing, in fact it learns the features. In this condition, deci-
sion space is divided in two regions. Decision boundary
is specified during learning process. The best experiment
result is obtained in second simulation with 73% true
separation. Figure 3 shows that the best result is 73%
and obtained with 8, 16, 20 and 23 neurons in hidden
layer. The specifications of this neural network are: mul-
ti-layer feedforward perceptron (MLP) neural network
with one hidden layer that has 9 neurons in input layer, 8
neurons in hidden layer and one neuron in input layer.
Input vector of neural network includes: surface of image,
surface of chromosome, number of pixels of boundary of
chromosomes, and six momentums [8].
One group proposed a novel approach to image com-
pletion for overlapping chromosomes. In their system,
only given missing regions, the task can be performed
automatically without human intervention. They address
the problem of image completion for overlapping chro-
mosomes in the context of a discrete global optimization
problem. In order to reconstruct the original chromosome
image as faithfully as possible, the objective cost func-
tion of this problem is defined under constraint condi-
tions of band patterns in chromosomes image, and cor-
Figure 3. The results of neural network simulation with 9
features at input. Horizontal axis is the number of neurons
in hidden layer and vertical axis represnts the average per-
centage of true classification.
Copyright © 2013 SciRes. ENG
responds to the energy of a discrete Markov random
fields. For efficiently optimizing this MRF, a loopy Be-
lief Propagation algorithm is utilized to perform it. By
using an efficient optimization algorithm, this approach
integrates the high-level knowledge of chromosomes im-
age into image completion process to yield good results
in comparison to conventional methods. Moreover, this
approach can automatically perform without human in-
tervention. Figure 4 shows some results obtained by this
approach to image completion for overlapping chromo-
somes [9].
3. Algorithms for M-FISH
1) Basic Theory
In the mid-1990s, a new technique for staining chro-
mosomes was introduced. It produced an image in which
each chromosome type appeared as a distinct color [10].
This multispectral staining technique is called multiplex
fluorescence in-situ hybridization, or MFISH, which
made analysis of chromosome images easier, not only for
visual inspection of the images by humans, but also for
computer analysis of the images. M-FISH uses five color
dyes that attach to various chromosomes differently to
produce a multispectral image, and a sixth dye that at-
taches to all chromosomes to produce a grayscale image.
(a) (b) (c)
Figure 4. Some results of image completion for overlapping
chromosomes. (a) Images of overlapping chromosomes; (b)
Background chromosome with occluded regions (in green)
after segmentation process; (c) Image completion based on
the approac h.
Thus, it is possible to envision new and improved me-
thods for the location, segmentation and classification of
chromosome images by exploiting the color information
in M-FISH images.
2) Segmentation Algorithms
One study introduces entropy as a criterion for select-
ing cut lines to decompose groups of chromosomes that
touch and overlap each other. This algorithm uses mul-
ti-spectral information in chromosome images for more
accurate segmentation. They locate the chromosome ma-
terial of a sixth dye, DAPI (4,6-diamidino-2-phenylin-
dole nucleic acid stain), which binds to all chromosomes,
and then find the connected components. Next, they label
every connected component as a separate object and cal-
culate its entropy. If its entropy is above a given thre-
shold, then they examine that object for possible cut lines.
They consider only points whose cut line was contained
within the chromosome clusters and had an 8-connected
neighbor whose class differed from theirs. Candidate cut
lines are made by straight line s between all combination s
of candidate cut points. Once all entropy-reducing divi-
sions are made, they recombine objects that may have
been labeled as separate objects due to overlap. Experi-
ment results show that this algorithm is able to decom-
pose clusters of touching and overlapping chromosomes
An ML method to segment and classify M-FISH chro-
moso me s i ma ge s using multispectr al data was introduced.
This method is developed for automatic chromosome
identification by exploiting the multispectral information
in M-FISH chromosome images and by jointly perform-
ing chromosome segmentation and classification. Re-
searchers develop a maximum-likelihood hypothesis test
that uses multispectral information, together with con-
ventional criteria, to select the best segmentation possi-
bility. Then this likelihood function is used to combine
chromosome segmentation and classification into a ro-
bust chromosome identification system. Experiment re-
sults show that the proposed likelihood function can also
be used as a reliable indicator of errors in segmentation,
errors in classification, and chromosome anomalies, which
can be indicators of radiation damage, cancer, and a wide
variety of inherited diseases. Figure 5 shows that one
single flagged segment can correct a whole cluster [12].
A new decomposition method for overlapping and
touching M-FISH chromosomes is also presented. To
overcome the limited success of previous decomposition
methods that use partial information about a chromosome
cluster, researchers have incorporated more knowledge
about the clusters into a maximum-likelihood frame work.
A cluster was better decomposed by incorporating more
knowledge. Multiple hypotheses were formed based on
color and the geometry defined by the basic elements of
Copyright © 2013 SciRes. ENG
a cluster, and then evaluated based on the pixel classifi-
cation results and chromosome sizes. A hypothesis that
has a maximum-likelihood is chosen as the best decom-
position of a given cluster. About 90% of accuracy was
obtained for two or three chromosome clusters, which
consist about 95% of all clusters with two or more chro-
mosomes, and 100% accuracy was obtained for clusters
with a single chromosome. Figure 6 shows some seg-
mentation results [13].
4. Conclusion
Since in almost every metaphase image partial touching
and overlapping of chromosomes are a common phenol-
menon, finding solutions for automated separation of over-
lapped chromosomes is difficult yet vital. In this paper,
some segmentation algorithms for overlapped chromo-
somes are investigated. The principle and the realization
of these algorithms are analyzed. Results of these algo-
rithms are compare d and discus s ed.
(a) (b) (c)
Figure 5. Singl e flagged segment can correct a whole cluster.
(a) M-FISH cluster; (b) Incorrect segmentation and classi-
fication; (c) Correct segmentation and classification.
Figure 6. Segmentation results.
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Likelihood Decomposition of Overlapping and Touching
M-FISH Chromosomes Using Geometry, Size and Color
Information,” Proceedings of the 28th IEEE EMBS An-
nual International Conference, 2006, pp. 3130-3133.