An unsupervised hair segmentation and counting system in microscopy images

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AN UNSUPERVISED HAIR SEGMENTATION AND COUNTING
SYSTEM IN MICROSCOPY IMAGES
By
A
PROJECT REPORT
Submitted to the Department of electronics &communication Engineering in the
FACULTY OF ENGINEERING & TECHNOLOGY
In partial fulfillment of the requirements for the award of the degree
Of
MASTER OF TECHNOLOGY
IN
ELECTRONICS &COMMUNICATION ENGINEERING
APRIL 2016

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CERTIFICATE
Certified that this project report titled “An Unsupervised Hair Segmentation and Counting
System in Microscopy Images” is the bonafide work of Mr. _____________Who carried out the
research under my supervision Certified further, that to the best of my knowledge the work
reported herein does not form part of any other project report or dissertation on the basis of which
a degree or award was conferred on an earlier occasion on this or any other candidate.
Signature of the Guide Signature of the H.O.D
Name Name

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DECLARATION
I hereby declare that the project work entitled “An Unsupervised Hair Segmentation and
Counting System in Microscopy Images” Submitted to BHARATHIDASAN UNIVERSITY in
partial fulfillment of the requirement for the award of the Degree of MASTER OF APPLIED
ELECTRONICS is a record of original work done by me the guidance of Prof.A.Vinayagam
M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any
other thesis or work on the basis of which a degree or award was conferred on an earlier occasion
to me or any other candidate.
(Student Name)
(Reg.No)
Place:
Date:

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ACKNOWLEDGEMENT
I am extremely glad to present my project “An Unsupervised Hair Segmentation and Counting
System in Microscopy Images” which is a part of my curriculum of third semester Master of
Science in Computer science. I take this opportunity to express my sincere gratitude to those who
helped me in bringing out this project work.
I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.),
PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project.
I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from
my deep heart for her valuable comments I received through my project.
I wish to express my deep sense of gratitude to my guide
Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for
successful completion of this project.
I also express my sincere thanks to the all the staff members of Computer science for their kind
advice.
And last, but not the least, I express my deep gratitude to my parents and friends for their
encouragement and support throughout the project.

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ABSTRACT:
This paper focuses on the development of medical software for clinical applications using
advanced image processing algorithms. Three critical issues of hair segmentation and counting are
addressed in this paper. First, the removal of any bright spots due to oil or moisture, which generate
circular patterns in the middle of the hair and significantly affect the accuracy of determining the
line. Second, two contacting or overlapping hairs are recognized and counted as a single hair. To
solve this problem, we proposed a hair-bundling algorithm to calculate any concealed hairs.
Finally, hairs may be wavy or curly, making the conventional Hough-based line detection
algorithm unsuitable, since it suffers from parameter selections, such as the minimum length of
line segment, and distance between line segments. Our proposed hair counting algorithm is
substantially more accurate than the Hough-based one, and robust to curls, oily scalp, noise-
corruption, and overlapping hairs, under various white balance.

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INTRODUCTION:
Biomedical image processing plays an important role in improving the accuracy of medical
and cosmetic applications. An accurate medical image allows a doctor to make a more informed
decision regarding the treatment of his patient. The image processing technology substantially
improves the fields of health and medicine. For example, when investigating hair and scalp
conditions are due to living in a different climate zone, age, or gender, image processing
technology can be applied in both scalp and skin examinations to provide an accurate diagnosis.
Furthermore, household pets are becoming increasingly common in modern society, and pet hair
loss is a major issue for many pet owners. Modern image processing technology can also be of
benefit in the field of veterinary medicine. The advances in digital image processing technology
allows for computer-aided diagnosis and treatment that is capable of dealing with computed
tomography (CT), positron emission tomography (PET), cardiovascular, ultrasound, and X-ray
images.
Most research topics for hair image processing usually focus on hair modeling, creating a
synthesis, and an animated global view. For instance, Mao et al. presented a method for generating
a realistic human hair texture. Chen and Zhu , proposed a generative model for synthesizing
realistic hair images from a stylistic drawing or sketch. It is based on the global hair style regardless
of the congenital hair flow for the purpose of hair editing and hair rendering. Yacoob and Davis
developed computational models for measuring hair appearance using a multidimensional
representation for human identifi- cation and hairstyle retrieval. An example-based hair geometry
synthesis appeared . For hair shape modeling, Wang et al. presented a part-based model that was
robust to both hair shape and variations in the environment. The so-called subspace clustering
dependency guarantees a reasonable output and allows for evaluating the effectiveness of part-
wise constraints in an information-theoretic way. In recent years, a number of articles on hair-
region segmentation and modeling appeared.
Regarding fine hair, medical image analyses have been focused mainly on pigmented skin
lesion images for hair removal and border detection , Research literature has been focused mainly
on hair segmentation and removal because the hairs are an obstacle for the clinical diagnosis of
the scalp. Huang et al. Evaluated a hair removal software, DullRazor, and proposed conventional
matched filters to enhance the curvilinear structures and improve the detection of thin hairs and

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hairs in shadow. The authors also demonstrated that missing hair intersections can be recovered
by applying regional growing algorithms with a color-similarity criteria. However, these
aforementioned techniques do not take into consideration the condition of the hairs such as their
health condition, density, diameter, oiliness, nor the number of hairs on each single follicle.
Line segments are fundamental component for the analysis of hairs. The Hough transform (HT)
is the most popular method to detect lines in images. A very recent survey can be found in the
principle of the HT is based on the scatter position points of an image for identifying the parameters
of a certain shape (e.g. straight line or circle). The wellknown advantage of the HT is its tolerance
for the discontinued line segment and the additive noise. However, since the global voting schemes
in the HT do not take line connectivity into account, the HT-based methods do not deal well with
cluttered images. Guerreiro et al. proposed a so-called STRAGITH method to solve this problem
by incorporating connectivity in the voting process. In addition, an improved voting scheme for
The HT that allows a real-time performance even on relatively large images has been
developed in on the other hand, Guru et al. presented a line detection algorithm based on small
eigenvalue. Similarly, Lee et al, proposed a method based on principal component analysis (PCA)
to detect straight lines and their orientations. Until now, much of attention in the robust detection
of line segments are still challenging and remaining an open frontier , At present, the diagnosis of
a person’s hair and scalp relies mainly on the skill and knowledge of professional scalp assessors.
The density and diameter of a person’s hairs usually reflects the health condition of that person’s
scalp. Hair counting is usually performed manually by a human assessor, often with unreliable
results. In addition, manual counting cannot tell us the diameter, length, and oiliness of the hairs.
An automated system capable of counting body hairs was proposed in.
In the present study we focused on hair counting on the scalp. Performing a detailed scalp
assessment is very time consuming. In an attempt to solve this problem, this study proposed an
automated scalp diagnosis system to reduce the time required for making a detailed scalp
assessment. Testing images were obtained by means of digital microscopy and then used to,
together with the knowledge of professional scalp assessors and doctors, accurately diagnose the
hair condition and determine the optimal scalp treatment. Rather than manually counting the hairs
on a person’s scalp, the proposed system determines the density, diameter, length and level of
oiliness of the hairs. The remainder of this paper is organized as follows. Section II presents the

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system overview. Section III describes the preprocessing procedures. Section IV presents the
multiscale line detection algorithm. A robust hair labeling and counting approach is illustrated in
Section V and the results are discussed in Section VI. Finally, we draw our conclusions in Section
VII

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CONCLUSION:
This study presented an automate hair segmentation and counting system to reduce the time
required for making a detailed scalp assessment by human assessors. First, oily and moist hairs
tend to generate a bright spot in the middle of the hair. Before counting the number of hairs, we
need to eliminate this bright spot on the hairs so as to avoid the problem of double-counting some
hairs. Second, the wavy and curly hairs tend to result in line detection faults. When a hair is not
straight,
The conventional line detection algorithm is invalid. Third, under-estimation of the number
of hairs occurs when hairs cross and occlude each other, making it very difficult to locate all hairs
accurately. Finally, because the scalp is comparatively unexposed, the image of the scalp is often
blurred or difficult to see. In addition the scalp is often insufficiently illuminated or over-exposed.
The framework of this paper can be seen as the first step towards intelligent computer-aided
medical image processing for cosmetic hair and scalp treatment applications.

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REFERENCES:
[1] P. Mukhopadhyay and B. B. Chaudhuri, “A survey of Hough transform,” Pattern Recognit.,
vol. 48, no. 3, pp. 993–1010, 2015.
[2] R. F. C. Guerreiro and P. M. Q. Aguiar, “Connectivity-enforcing Hough transform for the
robust extraction of line segments,” IEEE Trans. Image Process., vol. 21, no. 12, pp. 4819–4829,
Dec. 2012.
[3] R. G. von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, “LSD: A fast line segment detector
with a false detection control,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 4, pp. 722–
732, Apr. 2010.
[4] S. Du, C. Tu, B. J. van Wyk, and Z. Chen, “Collinear segment detection using HT
neighborhoods,” IEEE Trans. Image Process., vol. 20, no. 12, pp. 3612–3620, Dec. 2011.
[5] R. F. C. Guerreiro and P. M. Q. Aguiar, “Extraction of line segments in cluttered images via
multiscale edges,” in Proc. 20th IEEE ICIP, Sep. 2013, pp. 3045–3048.
[6] A. Borkar, M. Hayes, and M. T. Smith, “Polar randomized Hough transform for lane detection
using loose constraints of parallel lines,” in Proc. IEEE ICASSP, May 2011 pp. 1037–1040.
[7] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Reading, MA, USA:
Addison-Wesley, 2008.
[8] H. C. Shih and B. S. Lin, “Hair segmentation and counting algorithms in microscopy image,”
in Proc. IEEE ICCE, Las Vegas, NV, USA, Jan. 2014

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An unsupervised hair segmentation and counting system in microscopy images