Morphological Dilation and Erosion for Particle Analysis ...€¦ · that "optimal" general...
Transcript of Morphological Dilation and Erosion for Particle Analysis ...€¦ · that "optimal" general...
Morphological Dilation and Erosion for Particle
Analysis: A Review
Prabhdeep Singh and Vandana Chauhan Electronics & Communication Department, Kurukshetra University, Kurukshetra
Email: [email protected], [email protected]
Abstract—Illumination is a standout amongst the most
critical variables influencing the presence of a picture. It
frequently prompts decreased structures and
inhomogeneous forces of the picture because of various
surface of the protest surface and shadows cast from
various light source bearings. This impact creates from non
uniform background enlightenment and its belongings are
unfriendly in the event of biological images. Methods, for
example, segmentation, edge detection and general picture
handling calculations in view of 'region of interest' couldn't
separate between a portion of the particles and their
background or neighbouring pixels. This paper is expected
to evacuate these issues in minute picture handling by right
off the bat expelling the issue of non-uniform background
brightening from the picture utilizing Morphological
Opening, Adaptive Histogram Equalization and Image
Enhancement to change the information picture to its listed
frame with greatest exactness including thresholding and
difference modification strategies lastly utilizing the
associated segment methodology to discover number of
particles present in the picture, making pseudo shaded list
picture to figure the qualities of each molecule plainly and
registering the attributes of each molecule lastly plotting
zone based measurements and histogram of the last picture.
Index Terms—morphology, histogram equalization,
thresholding, dialation
I. INTRODUCTION
Image processing is utilized to adjust pictures to
enhance them (improvement, reclamation), extricate data
(investigation, acknowledgment), and change their
structure (arrangement, picture altering). Pictures can be
prepared by optical, photographic, and electronic means,
however picture handling utilizing advanced PCs is the
most widely recognized technique on the grounds that
computerized strategies are quick, adaptable, and precise.
Image handling innovation is utilized via planetary
researchers to upgrade pictures of Mars, Venus, or
different planets. Specialists utilize this innovation to
control CT sweeps and MRI pictures. Picture
Enhancement enhances the quality (clearness) of pictures
for human survey. Evacuating obscuring and commotion,
expanding difference, and uncovering points of interest
are cases of improvement operations. Image Processing
fundamentally incorporates examination, controls,
Manuscript received March 1, 2017; revised July 11, 2017.
stockpiling and show of graphical pictures from sources,
for example, photos, drawings et cetera. Picture handling
traverses a grouping of 3 stages, which are the picture
obtain, preparing and show stage. The picture secures
stage changes over the distinctions in shading and
shading in the photo into twofold values that a PC can
handle. The enhancement phase can include image
enhancement and data compression. The last phase
consists of display or printing of the processed image.
The term morphology means form and structure of an
object. Sometimes it refers to the arrangements and inter-
relationships between the parts of an object. Morphology
is identified with the shapes and advanced morphology is
an approach to depict and examine the state of a
computerized question. In science, morphology relates all
the more specifically to state of a creature, for example,
microorganisms. Morphological opening is a name
particular innovation that makes a yield picture to such
an extent that estimation of every pixel in the yield
picture depends on a correlation of the comparing pixel
in the information picture with its neighbors. By picking
the size and state of the area, one can build a
morphological operation that is delicate to particular
shapes in the information picture. Morphological
capacities could be utilized to perform regular picture
preparing undertakings, for example, contrast
enhancement, noise removal, thinning, skeletonization,
filling and segmentation.
II. RELATED WORK, ISSUES AND POSSIBLE
SOLUTIONS
There are many research papers that propose
Histogram modelling systems to alter a picture so that its
histogram has a coveted shape. This is helpful in
extending the low-differentiate levels of a picture with a
narrow histogram, accordingly accomplishing contrast
improvement. This is driven by the absence of evacuation
of non-uniform illumination, thus of which nearness of
additional light pixels at a few positions in the picture
and additional dull pixels around different particles in the
picture is variable, so this difference improvement at the
beginning of the picture handling does not make the
exact limits of the items to be identified. [8] Komal Vij,
et al. has proposed picture upgrade strategy to build
picture perceivability and subtle elements. They covers
every one of the components like enhancement efficiency,
computational necessities, noise amplification, user
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International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 6, No. 3, July 2017
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intervention, and application suitability. [10] Yan Wan,
et al. has proposed a double edge ascertaining technique
to acquire precise and nonstop fiber edge, and to control
the picture noise. [11] M. Kowalczyk, et al. has proposed
origination of viably working gatherings of morphology
capacities specifically picture cases. [12] David Menotti
has proposed two strategies for quick picture differentiate
improvement based on Histogram Equalization (HE), one
for gray-level images, and other for color images. For
gray-level images, technique called Multi-HE has been
proposed. [13] Ley, et al. has proposed a basic
background illumination adjustment based approach for
enhancing tangling issues with uneven or poor lit blue-
/green screens. [14] Joanna Sekulska, et al. has proposed
general techniques for organic pictures handling. These
procedures are arranged to better picture interpretation. [7]
has proposed a programmed strategy for assessing the
enlightenment field utilizing just picture power
gradients.[15] has proposed a novel model-based redress
technique is proposed, in view of the suspicion that a
picture ruined by force inhomogeneity contains more
data than the relating uncorrupted image. [9] has
proposed another way to deal with the revision of power
inhomogeneities in attractive reverberation imaging
(MRI) that fundamentally enhances force based tissue
segmentation. [3] Yadong Wu, et al. has proposed a
picture light revision calculation in view of tone mapping.
The proposed calculation consolidated shading space
decay and tone mapping based picture shine conformity,
which can enhance the picture differentiate while keeping
up the better shade of the first picture, and can't expand
noise. [4] M Rama Bai has Introduced a novel algorithm
based on multi-scale morphological method for the
purpose of border detection. Standard morphological
border detection methods use single and symmetrical
structure elements. [2] YanFeng Sun, et al. has proposed
a Multi-scale Fusion TV-based Illumination Normalized
(MFTVIN) display. In this Illumination impacts in the
expansive scale part are expelled by district based
histogram evening out and homomorphic filtering. [13]
Emerson Carlos Pedrino, et al. has proposed a unique
reconfigurable design utilizing sensible, number juggling,
and morphological directions created consequently by a
hereditary programming approach. They likewise
exhibited Binary, dark, and shading picture pragmatic
applications utilizing the created design.
III. TECHNOLOGY OVERVIEW
Mathematical Morphology (MM) is a hypothesis and
strategy for the examination and preparing of geometrical
structures, in light of set hypothesis, cross section
hypothesis, topology, and irregular capacities. MM is
most regularly connected to advanced pictures, yet it can
be utilized too on diagrams, surface cross sections, solids
and numerous other spatial structures, Topological and
Geometrical space ideas, for example, estimate, shape,
convexity,and geodesic separation, can be described by
MM on both persistent and discrete spaces. MM is
likewise establishment of morphological picture handling,
which comprises of an arrangement of administrators that
change pictures as indicated by the above
portrayals.Denoting an image by f(x) and the structuring
function by b(x), the grayscale dilation of f by b is given
by:
where "sup" denotes the supremum. Similarly, the
erosion of f by b is given by
MM was originally developed for binary images, and
was later extended to grayscale functions and images.
The subsequent generalization to complete lattices is
widely accepted today as MM's theoretical foundation. In
binary morphology, an image is viewed as a subset of
a Euclidean space Rd or the integer grid Zd, for some
dimension d.
Figure 1. (a) Original Image (b) Result of Dialation (c) Result of Erosion
A portion of the essential components of binary
morphology incorporates Structuring Element, Basic
Operators: Euclidean Distance and Shift Invariant,
Erosion and Dilation and Opening and Closing of double
Images. The essential thought in binary morphology is to
test a picture with a straightforward, pre-characterized
shape, reaching determinations on how this shape fits or
misses the shapes in the picture. This basic "test" is
called organizing component, and is itself a parallel
picture. Widening adds pixels to the limits of items in a
picture, while disintegration evacuates pixels on question
limits. In greyscale morphology, pictures are capacities
mapping an Euclidean space or network E into R U {∞, -
∞}, where R is set of reals, ∞ is a component bigger than
any genuine number, - ∞ is a component littler than any
genuine number. Grayscale organizing components are
additionally elements of a similar organization, called
"structuring functions".
a) b) c)
Figure 2. (a) The mug image; (b) Its histogram; (c) Thresholded using global segmentation at T=16. [16]
Various techniques and common approaches to solve
the problem of particle identification are Image Filtering,
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International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 6, No. 3, July 2017
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Boundary detection, Edge Detection, Linear Filtering,
Segmentation, Morphological operations: Dilation and
Erosion etc. However, a large portion of these systems
alone neglect to precisely decide the items genuine limits
because of the issue of non-uniform brightening in the
background of the picture because of which the vast
majority of the particles give off an impression of being
either dim or light in a picture and utilizing methods, for
example, division, edge discovery and general picture
handling calculations in view of 'region of interest'
couldn't separate between a portion of the particles and
their background or neighbouring pixels.
Notwithstanding when the particles are extricated, there
are changes to their shape and size which prompts broken
readings in the calculations of range of such particles. In
this way, propelled picture handling and picture upgrade
devices must be utilized for most extreme precision of
the outcomes and to distinguish the particles precisely
from the picture without missing a solitary protest.
Figrue 3. Boundary extraction usng 3*3 square element. [4]
The Following is the flow-diagram of algorithms
followed in previous approaches to solve this desired
problem:
Figure 4. Image processing algorithm previously used.
Considering the above calculation, the minuscule
pictures are first experienced through histogram
equalization, a strategy that speaks to the relative
recurrence of event of dark levels inside a picture.
Histogram displaying systems change a picture so that its
histogram has a coveted shape. This is valuable in
extending the low-differentiate levels of a picture with a
tight histogram, subsequently accomplishing contrast
improvement. In histogram balance (HE), the objective is
to acquire a uniform histogram for the yield picture, so
that "optimal" general differentiation is seen.
IV. CONCLUSIONS AND FUTURE WORK
It has been inferred that because of non-uniform
background illumination, the vast majority of the
particles give off an impression of being either dull or
light in a picture and utilizing strategies, for example,
division, edge location and general picture preparing
calculations in light of 'region of interest' couldn't
separate between a portion of the particles and their
experience or neighbouring pixels. In future, it is plan to
play out the reenactments and investigation of a picture
and its upgrade to amend for non uniform enlightenment,
then utilize the improved picture to distinguish discrete
items/particles exhibit in the picture. The work to be
done is based upon pictures taken for natural reviews, for
example, pictures comprising of bunch of cells,
microorganisms, or different particles where it is critical
to discover the convergence of the particles. In this way,
a specific characterized zone of a photographic plate is
taken and uncovered by the particles the attributes of
which are to be registered. Thus, the strategy utilized is
make a calculation to at long last analyze each molecule
of the picture, to see obviously every question in the
picture, and evacuate any of the issues, for example, non-
uniform brightening, less brilliance and so forth that
make it hard to separate between the particles. At long
last, when all the perception and breaking down issues
are expelled, the attributes of every molecule, its region
must be processed and results would be appeared in
range based measurements and histogram equalization.
REFERENCES
[1] P. Lehana, et al., “Effect of enhancement technique uniform
ultrasound images,” Journal of Computational Engineering, 2015.
[2] Y. F. Sun, et al., “A multi-scale TVQ-based illumination normalization model,” Lecture Notes in Engineering and
Computer Science, vol. 1, pp. 1-6, 2011.
[3] Y. Wu, et al., “An image illumination correction algorithm based on tone mapping,” in Proc. 3rd International Congress on Image
and Signal Processing, 2010, pp. 245-248.
[4] M. R. Bai, “A new approach for border extraction using morphological methods,” International Journal of Engineering
Science and Technology, pp. 3832-3837, 2010.
[5] K. Loquin, et al., “Convolution filtering and mathematical
morphology on an image: A unified view,” pp. 1-4, 2010.
[6] P. Kupidur, “Semi-automatic method for a built-up area intensity
survey using morphological granulometry,” Problems of Landscape Ecology, pp. 271-277, 2010.
[7] A. Acharya, et al., “FPGA based non uniform illumination
correction in image processing applications,” Journal of Computer Technology and Applications, vol. 2, pp. 349-358, 2009.
[8] K. Vij, et al., “Enhancement of images using histogram processing techniques,” International Journal of Computer
Technology and Applications, vol. 2, pp. 309-313, 2009.
[9] J. M. Morel, et al., “Fast implementation of color constancy algorithms,” Proceedings of SPIE - The International Society for
Optical Engineering, vol. 19, pp. 2825-2837, 2009.
©2017 Int. J. Elec. & Elecn. Eng. & Telcomm.
International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 6, No. 3, July 2017
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[10] Y. Wan, et al., “A dual threshold calculating method for fiber’s
edge extraction,” in Proc. International Conference on Intelligent
Computing and Intelligent Systems, 2009, pp. 247-254.
[11] T. Seemann, “Digital image processing using local segmentation,” thesis B. Sc (Hons), 2002, p. 61.
[12] D. Menotti, “Contrast enhancement in digital imaging using
histogram equalization,” Paris Est, pp. 1-85, 2008. [13] E. C. Pedrino, et al., “A genetic programming approach to
reconfigure a morphological image processing architecture,”
International Journal of Reconfigurable Computing, pp. 1-10, 2011.
[14] J. Sekulska, et al., “Digital image processing methods in
biological structure recognition a short review,” pp. 24-27, 2006.
[15] M. Ranzato, et al., “Automatic recognition of biological particles
in microscopic images,” pp. 1-19, 2006. [16] M. Kowalczyk, et al., “Application of mathematical morphology
operations for simplification and improvement of correlation of
images in close-range photogrammetry,” pp. 153-158, 2008. [17] N. Ley, et al., “GPU-Based background illumination correction
for blue screen matting,” in Proc. International Symposium on
Consumer Electronics, 2007, pp. 1-102.
©2017 Int. J. Elec. & Elecn. Eng. & Telcomm.
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