Automatic Accurate Quantification of Cellular Nuclei in the OPMD-affected Cellular
Image
by Hierachically Adaptive threshold and GA-based Shape Detection
Yong Ping Guo
-----------------------------------------------------------------------------------------------------------ABSTRACT
--1 A perfect definition of the problem being
solved. (by Gordon)
--2 Review of the 2-D image processing related to
touching cells (by Gordon) – done.
--3 Hierarchical image processing methods description
(by Gordon)
--4 Adaptive image thresholding method description
(by Gordon)
--5 Ellipses detection method description (by Jane)
--6 Combine 3.4 & 3.5 into Hierarchical structure.
(by Kharma + Hussien)
-- Result statistics (by Jane)
--Introduction, Result analysis, Conclusion, Text and
English editing (by Kharma
KEYWORDS: Genetic Algorithm; Ellipse detection; OPMD Genetics; Cellular image; Automatic
quantification; hierarchical adaptive threshold (HAT); GA-based shape detection (GASD)
-----------------------------------------------------------------------------------------------------------1, INTRODUCTION
Oculopharyngeal muscular dystrophy (OPMD) is an autosomal dominant, adult-onset genetic disease with
a worldwide distribution that has been reported in at least 33 countries[1, 2]. It is characterized by a
progressive eyelid drooping (ptosis), swallowing difficulties (dysphagia), and proximal limb weakness. This
disease is particularly present within two groups distinct: québécois where the estimated mutation-carrier
prevalence is in the order of 1:1000, and Bukhara Jews settled in Isreal now, where the prevalence is close
to 1:600.
OPMD disease results from the mutation of one type of gene called PABPN1. The mutated PABPN1
(mPABPN1) induces the formation of muscle intranuclear inclusions that are thought to be the hallmark of
this disease[2]. These inclusions are dot-like filaments that have a tubular appearance with an outer
diameter of 8.5 nanometers, an inner diameter of 3 nanometers, and a length of approximately 0.25
micrometer. Two of the examples are illustrated in Fig.1 in which the ellipse-like green objects are cellular
nuclei, and the brighter dots inside some of the cellular nuclei are called inclusions. In order to do research
concerning and diagnose OPMD, it is prerequisite to count cell nuclei and the inclusions inside each cell
nucleus from microscopic imagery. Presently, this task is taken manually by a well-trained specialist. This
process is laborious and tedious, yielding subjective and imprecise results. Hence, there is an increasing
demand for an automatic quantification system to process the digitized histological images and extract
useful information reasonably accurately from the images [3]. The objective of our study aims to present an
automatic quantification software system to detect and count nuclei and their inclusions from the OPMD-.
1
(a)
(b)
Fig.1 Example of OPMD-affected cellular images : (a) with cell occlusion, (b) with non-uniform background
affected cellular image, thereby, release human specialists from difficult and time-consuming work and
improve objectivity meanwhile. Two core parts in this study lie in: (a) identification of cellular nuclei from the
image; (b) identification of inclusions from each nucleus. The automatic accurate quantification system of
cellular nuclei being presented here is part of our current research work. In this paper, we will follow a
hierarchical structure to recognize and identify objects of interest. This is coming from the following
observation. The human being will perceive objects in a hierarchical way when he is trying to recognize
something. Humans are always to perceive bigger areas of interest against the background at first and then
he perceives smaller areas of interest against the area identified in the last step if necessary, and so on.
Our research is to model roughly this hierarchical recognition organization. The popular threshold
methodology originated by N. Otsu [4] has been modified and improved to increase its adaptivity. As a
fundamental object-recognition means, it works altogether with the hierarchical structure to form hierarchical
adaptive threshold (HAT) mechanism. Considering the low-sensitivity to the object shape of the threshold
mechanism, a GA-based shape detection mechanism has been developed to handle cell occlusion, in
which case the cytometric features of objects play the roles while segmenting touching and/or overlapped
nuclei. The experiment results indicated that our automatic accurate quantification system can achieve an
accuracy of more than 95% in the OPMD-affected cellular nucleus quantification.
Theoretically, in the case of no or low noise (how to handle image noise is another large issue), an
appropriate image segmentation algorithm (i.e., object contour detecting followed by object classification)
could accurately quantify the cellular nuclei on the image. In practice, however, no attempt is very
successful and robust enough [5]. The reasons why it is are that there are two major difficulties for object
segmentation from an image. One is the non-uniform background, another being cellular occlusion (i.e., cell
touching, aggregation, cluster or overlapping to form cell heaps, in some of literature). In the past two
decades, a large quantity of research effort had been made to attempt to handle the two difficulties
mentioned above [3, 5-20].
2
There are at least four categories of methodologies appearing in the cell image processing literature to cope
with the non-uniformity of background. The most conventional method is to partition the whole image into
smaller patches sized N×N pixels and think these smaller patches as background-uniform [3, 9]. The
second method to correct these inhomogeneities of cellular image is to multiply each image by a
background control matrix (BCM) [6]. The third method involves the employment of certain morphological
operations (specifically: erosion and dilation) to remove all relevant foreground objects (i.e., cells, inclusions
and heaps), while maintaining the background, then apply image subtraction operation to remove
background from the original image [10]. The adaptivity of all these three methods is limited and the control
parameters of them (such as patch size, BCM coefficients, structure element’s size) need to be known in
advance. The fourth attempt to crack down the image’s non-uniformity is to adopt a hierarchical image
processing structure [3, 7-8, 10-14]. This method, called two-step segmentation strategy in [3, 12], called
quadtree (or multi-resolution) approach in [7-8, 10, 13] and called recursive/iterative thresholding technique
in [12, 14], is the most promising methodology, which can handle not only the non-uniformity problem of
background but also that of image foreground. In Section 2, we will further develop and formulize this image
processing mechanism. We name it as hierarchical structure.
If the non-uniformity of cellular image is the small one of the difficulties on the road to achieve automatic
quantification of cellular nuclei, the nucleus occlusion (or heap) is the really big challenge. According to the
authors of Paper [5], this is a difficult task for most image-processing algorithm and no attempt is successful
enough to be able to work well in all the cases. Even if so, there are still a large number of literature which
attempt to solve this problem and achieve at least partial success. From application point of view, the
techniques to separate cells located in cell heaps can be categorized as the following four classes:
 Model-Matching Method [15-17]
In this method, some a priori knowledge of object to be detected, for example, the fact that most cells have
a roughly elliptical shape, is established in advance and represented as models with some unknown
parameters like general elliptical equation, then apply the model to the image and seek the optimal
matching between the model and the object in the image so that those unknown parameters can be found
out. In Paper [15], a parametric fitting algorithm was successfully applied to segment overlapped ellipticallyshaped cervical and breast cells. In [16], Jiang and Yang used some a priori knowledge to approximate,
and hence detect, cells in images. In [17], a template-matching method was used, in which one certain
agreement was sought between a pre-known prototype and an object within an image. This method
incorporate object’s shape information into seeking algorithm and is conforming to the way in which human
being perceives objects, and is a very promising approach to identify and recognize individual object from
object heaps. The main drawback of these model-matching methods is that they are computationally
expensive and they need to have a good object model in advance.
 Watershed or Water Immersion Methods
The watershed or water immersion algorithm is a powerful technique for touching object detection. Many
automatic cellular image segmentation system relied on watershed or water immersion to deal with object
occlusion [3, 6-8, 18-19]. The main problem is that it needs seed points and it typically over-segments the
individual cells [3] in case of histological noise.
 Object Heap Shape Analysis Method
In [20], an innovative algorithm was presented to analyze the concavities along the cell heap contour so as
to find out the segmenting concave points for segmentation. The authors attempted to imitate the procedure
human segments overlapping and touching objects. The main problem of this method is that it is very
difficult to judge which concavities are the real segmenting concave points in case of histological noise and
false concavities caused by upstream image processing algorithm.
 Snake-based Method
3
The Active Contour Model [21-22] is a very popular method in medical image processing. The reason why it
gains popularity is that it tries to incorporate geometrical information of object into the algorithm. In [23-25],
snake algorithm was employed for detection of cell contours. Currently, this approach has three
shortcomings: (a) a set of parameters should be tuned by human; (b) it needs seed points to ignite the
snake algorithm; (c) it is computationally expensive.
In general, a large number of research activities are going on around how to deal with object occlusion.
Traditional approaches, like watershed or threshold, ignore or focus little on the object’s shape information
so that they perform awkwardly in case of occlusions. Some innovative approaches, such as concavity
analysis and active contour deformation, need further polishing before they can be used in real applications,
though they try to incorporate geometrical information. In a specific image processing application, modelmatching method performs most promising in the case that we have enough domain-specific knowledge of
applications. In Section 2, we will present a GA-based shape detection algorithm to handle the nucleus
occlusion problem in our study.
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