Computational hair quality categorization in lower magnifications

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Computational hair quality categorization in lower magnifications

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Heshmat, Barmak et al. “Computational Hair Quality Categorization

in Lower Magnifications.” Proceedings of SPIE, San Francisco,

California, USA, 10 March, 2015. Vol. 9333., Edited by Adam Wax and

Vadim Backman, SPIE, 2015. n.p. © 2015 SPIE

As Published

http://dx.doi.org/10.1117/12.2078027

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

Final published version

Citable link

http://hdl.handle.net/1721.1/110721

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Article is made available in accordance with the publisher's

policy and may be subject to US copyright law. Please refer to the

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Comp

Bar

We take adv tip-side illum magnification resolution. O all cases. Thi

Keywords: d Hair quality Hair quality down to mol Additionally factors are m healthy and diffraction li necessarily v numerical ap study these f increased ali hair is the ab transmission Figu 0.75 sign arrow large We have take illumination)

putationa

rmak Heshm

a

_{Media La}

b

_P

vantage of hum mination. We ns (down to Our technique f is technique ha dark field micr

can be detecte can be classif ecular level. T , it is not alway manifested in t unhealthy (UV mit of optical visible in a simp perture (e.g. 10 features2, 3, 5, ignment sensit bsorption and s

mode and stro

ure 1. Challenge 5NA) (a) Black nal to noise ratio

w) from the out er dynamic rang en the idea of o ) to capture m

al hair q

mat

a*

, Hayato

ab, Massach

PES Institute

man hair specifi show that the 20×) with les for quality cate as strong poten roscopy, obliqu

ed with conven fied thoroughly The current ins

ys necessary to the geometry V damaged9) microscopy. T ple widefield m 00× with 1.4N 6 . This higher ivity and comp scattering of lig ong reflection o

es in using con hair is imaged

(SNR) at the ar ter layers and th ge sensor. Scale b oblique illumin microscopic im

uality ca

o Ikoma

a

, Ik H

husetts Institu

e of Technol

AB fic geometry to statistics of t ss powerful ob egorization of b ntial for lower c

ue illumination 1. INTR ntional polarim y by extensive struments for a o analyze the c of the hair4, 9 hair are rathe These are three microscopy con NA) and more c r resolution is mplexity. Other ght by inner st of light from ou nventional illum in transmission reas shown by a he geometry of bars are 40 micro

nation10 to extr mages of hair

ategorizat

Hyun Lee

a

, K

ute of Techn

logy, Bangal

BSTRACT o visualize spar these features bjectives when black, blond, a cost, portable, a n microscopy, p RODUCTIO metry technique e spectroscopy nalyzing hair q chemical comp 9 . Unfortunatel er small (subm e dimensional nfiguration. Th complex interf s obtained in p challenges fo tructures of ha uter layers in b mination configu mode. Absorpti arrows. (b) The i the hair renders ometer. reme. Here we for extracting

tion in lo

Krishna Ras

nology, Cam

lore, Karnata

rse submicron can directly e n the features and grey huma and autonomou portable diagno

ONS

es, SEM and d y and analysis quality are rath pounds of hair a ly the variation micron) in x-y features that c herefore, highe ferometric or c price of reduce or using optica ir (cortex and blonde or grey urations with lo ion and scatterin intense reflection s the sides of th

e have employe g mechanical d

ower mag

stogi

a,b

, Ram

mbridge, MA,

aka, India

cuticle peeling estimate hair q s themselves a an scalp hair sa us hair diagnos ostics, human h different spectr of the essenti her bulky and r as the effects o ns in the featu

plane (sampl can have larger er magnificatio confocal techn ed field of vie al microscopy w medulla) in bl hair (Fig. 1 (b) ow magnificatio ng from inner l n in the middle e blonde hair da ed near perpend damage featur

gnificatio

mesh Raskar

a

, USA

gs with highly quality in muc are below the amples is succ stic apparatuse hair roscopic techn ial components relatively expen of some of the ures of the cu le plane) and r z variation th n objectives w niques are nece ew and depth while studying lack hair (Fig. )) in reflection

on objective (20 ayers reduces th region (shown b ark. This require

dicular (highly res. By exploi

ons

y oblique ch lower e system essful in es. iques1-9. s in hair nsive5-9. invasive uticles of close to hat is not with large essary to of field, g human 1 (a)) in mode. 0× he by es y oblique iting the

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Sensor Can opt Pol Can opt Sensor nera Aperture arizer nera ics Aperture a rizer LED

O

tax

I

D

Absorbent21 LED 20X

ED

Absorbentha Hair tip I Icier Hair follicle Ill )Ider sparsity of th with 20× obj these feature damaged) wi phone add-on The setup co into an apertur vertically in m in Fig. 2. The sensitive due t Figu featu pola Only pola Due to speci 8.5 times in there are mo that is strong (Fig. 2 (a)). cuticle edges when the ref scattering wh small angle w he submicron f jective at highl es and, based o ith 100% succ ns11 that could nsists of an inf re, a rotatable li midair with a sam

e sample holder to low magnifica

ure 2. Measurem ures that can be arized as seen in

y a few larger arization with the

fic geometry o the defined da re number of c gly polarized in This helps us s (shown with flection from t hen we illumin with the main a

features and po ly oblique LED on these numb

ess in our sam d analyze hair q

finity corrected

near polarizer, a mple holder and i is coupled into a ation.

ment setup schem detected with 2 n the measureme irregularities c ese scatterings).

of hair, the diff amage level pa cuticles that ar n the horizonta to record the

in Fig. 2). W the body of the nated the sampl axis of the hair

olarization of sc D illumination bers, can categ mple sets. This

quality. 2. EXPERI d objective (20 and then a 1.3 m illuminated with a course 0.5" mi

matics. (a) Illum 20× objective. Th ents on the right can be seen. ( ference betwee arameter) whe re peeled off fr al direction com background w We realized tha e hair is comp le with the sam r strand (Fig. 2

cattered light, w n. Our method

orize black, bl paves the way

IMENTAL S × Olympus UPL mega pixel camer h a broadband n

iniature dovetail

mination from the he scattering fro . (b) Illuminatio is cross pola

en healthy and en a hair strand from the hair st

mpared to the when the polari at the backgrou parable with th me angle from (b)). we have been a computes the lond, and grey y for lower cos

SETUP LSAPO Objectiv

ra (Point grey F

non-polarized L

l mechanical 3D

e tip side results om the edges of on of the same s

rized with cuti

damaged hair d is illuminate trand. The edg weaker reflect izer is cross po und measurem he cuticle edge the root side. T

able to charact distribution qu y hair into heal st, portable for

ve, 0.75NA, 0.6 m

FL3-U3-32S2C-C

LED in highly

D stage. The setu

in large number f the peeled cutic ample from the cle scattering,

becomes signi ed from the tip ges of these cu

tion from the b olarized with t ment was only n

es. We also did This is expecte

terize human sc uality and area

lthy and unhea rm factors such mm WD) that i CS). A hair stran y oblique angle up is not highly a r of spot-like cles is highly follicle side. ∥ is parallel ificantly eviden p side. This is uticles will scat body of the ha the scattering f necessary for g dn’t find as sig ed as the peelin calp hair a ratio of althy (or h as cell s coupled nd is held (85°) as alignment nt (up to because tter light air strand from the grey hair gnificant ngs have

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stribution qua ity Tip side al illumination Featu extract ó R The block di color. If the h hair is grey, when the sca after image r image registr from the user

Figu

conv Features are blob intensit region are fil are extracted feature area Another appr to measure th Figu To calculate patches. If a counted. Fin size of ROI features on 7 iagram of the c hair is black or a preprocessin attered light fro registration (th ration12). Altho r in this case.

ure 3. Block diag

verted to grays extracted by u ty is over certa ltered by using d, the algorithm ratio (FAR). T roach12 was to he damage leve ure 4. An examp FAR, a region feature existe ally, FAR is c is 300×700 an 75 patches, then computational r blonde, the al ng step is requ om the cuticle here is a small ough hair color

gram of computa

scale as input im using blob dete

ain threshold, g area criteria o m enters the sta The distributio o find area ratio

el of the hair. le of our method n of interest is ed in the patch alculated by u nd the size of n FAR is 75/52 categorization lgorithm extrac uired to prono

edges are blo shift in the im r can be easily

ational categoriz

mage.

ction. This use it is considere or number of pi atistical analysi on quality of s o which contai d applied to a rea selected as a m h, feature count

sing this accum f patch is 20×2

25 = 0.143.

n is shown in F cts the features

unce the featu cked by the po mage when the distinguished

zation process. O es the regional ed as a feature ixels that are a is block that co cattering featu ins features alo

al image that fol

mask. Then, re t is set to 1. T mulated counts 20, then we ha Fig. 3. The ha s in the image a ures. For the g

olarizer. This b polarization i under the micr

Original red-gr maxima that a e. To remove b above the thresh onsists of calcu ures can be stu

ong each small

llows Fig. 3 egion of intere This count is a s over total nu ave (300/20)×( ir quality is ca and proceeds w grey hair the b

background im s rotated and t roscope, we as reen-blue (RGB are computed w big or small b hold in that reg ulating distribu udied by using

l image patch.

est (ROI) is div accumulated un mber of patche (700/20) = 525

ategorized base with the analys

ackground is r mage is then su this is compen sume that it is B) images are with a threshol blobs, the pixe gion. After the ution quality (D g Delaunay tria

We adapt this

vided into sma ntil last image es. For examp 5 patches. If t ed on its sis. If the recorded ubtracted nsated by an input ld, e.g, if ls in the features DQ) and angles13. concept all image patch is le, if the there are 3. COMPUTATIONAL CATEGORIZATION

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Dan-Black laged samplE , Blonde es Grey BI< Health ack 13 iy samples fonde Grey For DQ the e area of each t Higher value multiplied to categorized a oblique illum configuration distribution q parameter. Fi

Our data set consists of si oblique illum Figu It is notewo diffraction lim categorize th space that co damage leve and damaged the boundary However eve decision line the clustering categorizatio better decisio extracted featu triangle, and th e of DQ and FA o represent the as damaged an mination intens n with coaxial quality and are

inally the dama

ts are limited t ix images with mination from t

ure 5. Healthy ha

orthy that the mit is not viol he hair as in Fi

orrectly catego l for each hair d hair is reduce y of healthy an en in the curren . Fig. 6 (b) sho g is also furthe on method that on making can

ures are consid he radian value AR depicts a h e damage leve d vice versa. F sifies the subm illumination; ea ratio of thes age level is com

to black, blon h three healthy tip side of the h

air versus damag

imaged featur ated in any wa ig. 6. For all o orized the sam r color data set ed as we go fro nd damaged clu

nt limited data ows further det er evident here. we have chose be performed dered as vertice e of each triang higher damage el parameter. Fig. 4 shows an micron feature the features a e features are t mpared with a 4. EXPERIM

de, and grey h and three dam hair renders he

ged hair (96 hour

res are sparse ay. However, d of our measure mples into heal t. As it is evide om black hair t

usters. We exp set it is appare tails of this dec . It is notewort en and this mig

by molding thi es of Delaunay gle contributes level. To class If this param n examples of es compared to re extracted by then calculated threshold to ca MENTAL RE human scalp h maged (96 hour

althy and dama

rs UV exposure)

and also not due to sparsity ements we cou lthy and dama ent from this f to grey hair. In pect to see som

ent that the dat cision lines wh thy that the fixe ght not necessa is problem into

y triangles. Th to the DQ. sify hair condit meter is higher an image that o conventional y blob detectio d and multiplie ategorize the h ESULTS

hair for this in s UV exposed) aged hair notab

). scale bar is 40

fully resolved y we can study uld find a thres aged as in Fig figure and also n this graph the me outliers as t ta points start t hich take the fo ed damage lev arily be the bes o an optimizati

he number of D tion, these two

than certain goes through t l transmission on as highligh ed to calculate hair. nitial study. Ea ) hair images. bly different. micrometer. d as seen in F the statistics o shold line in da g. 6. Fig. 6 (a) Fig. 5, the co e decision line i the number of to form cluster orm of constan el curves are ju st way to categ ion framework Delaunay trian parameters ar level then the this process: th or reflection hted by red cir the final dama

ach hair color As in Fig. 5 th Fig. 5. Theref of these and, th amage level pa ) shows the ca ntrast between is drawn right samples are in rs above and b nt damage level ust a simple an gorize these sam k. gles, the e simply e hair is he highly imaging rcles, the age level data set he highly fore, the herefore, arameter alculated n healthy between ncreased. elow the l curves; nd global mples. A

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(a) Black Black -Black 0.7 0.6 0.5 a) > a) - 0.4 a) a m E 0.3 ca 0 0.2 0.1 0 < healthy ç damaged c decision curve Black Blonde healthy Blonde damaged - Blonde decision ci o B o 8 Blonde ® Grey healtl y Grey dama urve T Grey decis

Grey

flack healthy lack damaged lack decision curve.

6 .5 5 .5 4 .5 3 5 2 .5 1 0.05 7v V Blonde healthy Blonde damagE Blonde decisior 0.1 Feature are ® Grey he =_d e Grey dar

r curve -Grey dec

,,_

0.15 0 a ratio althy maged cision curve 12 As shown he unable to res if more than better SNR f that only me regardless of Figu featu We have pro magnification statistical stu optically or that have spa

We acknowle [1] Brown, A birefringence [2] Lefaudeu hair tresses u [3] Gamez-G Sci., 58, 269-[4] Lefaudeu Hair] 5th ed, [5] Tsugita, T hair," Skin R

ere lower magn solve the dama

one strand of h for feature extr easures the abs f accuracy it is

ure 5.(a) differen ures in high angl

oposed and de ns. While the d udy of each sa computationall arse anisotropic

edge the contri

A. C., Belser, R e and low sulfu ux, N., Lechoc using polarizati Garcia, M. and -282 (2007). ux, N., Lechoc Springer, (201 T. and Iwai, T Res. Technol. (2 nifications can age features pro hair is used2 or raction. Anothe olute value of relatively inco

nt hair types are s le illuminations

emonstrated a damage feature ample and cor ly expensive a c surface irregu ibutions from O R. B., Crounse, ur content," J. I cinski, N., Clem ion imaging," J Lu, Y., "Ligh

cinski, N., Cle 12).

., "Optical coh 2014).

be used for ca operly with low r if higher reso er appealing di f scattering from ompatible with

successfully cate for healthy and u

5. CO

microscopy t es are not direc rrectly categor and, therefore, ularities such a Olympus Corp 7. RE , R. G. and We Invest. Dermat menceau, P. an J. Cosmet. Sci. ht interference p emenceau, P. a herence tomogr ategorizing diff wer end 10X ( lution sensors irection can be m the whole sa today's portab

egorized into hea unhealthy hair.

ONCLUSION

technique that ctly resolvable rize hair qualit is highly app as ones in skin oration and Na EFERENCES ehr, R. F., "A c tol., 54, 496–50 nd Breugnot S , 60, 153–169 phenomena pr and Breugnot raphy using im fferent levels o 0.25NA) objec (as in modern e use of laser il ample, this mi ble electronic tr

althy and unheal

NS

can be used , the sparsity o ty to healthy a ealing for port and nail. atura. S congenital hair 09 (1970). S., "New luster (2009). roduced by cut t, S., [Chemica mages of hair st f hair damage. ctive. However smartphones) llumination and ight sound as a rends11.

lthy (a) Statistic

to investigate of these feature

and damaged. table form fac

defect: trichos r formula for t ticle cells of hu al and Physica tructure and dy . At this point r, it is conceiva

are used there d very sensitiv a simpler appro s of imaged hair quality i es has allowed This techniqu ctors for other

schisis with alt the characteriz uman hair," J. al Behavior of yes penetrating we were able that can be a ve sensor oach but in lower us to do ue is not samples ternating zation of Cosmet. f Human g into the 6. ACKNOWLEDGMENTS

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[6] Hadjur, C., Daty, G., Madry, G. and Corcuff, P., "Cosmetic assessment of the human hair by confocal microscopy,"

Scanning, 24, 59–64 (2002).

[7] Kuzuhara, A., "Analysis of structural changes in permanent waved human hair using Raman spectroscopy,"

Biopolymers, 85, 274–283 (2007).

[8] Jachowicz, J. and McMullen, R. L., "Tryptophan fluorescence in hair-examination of contributing factors," J. Cosmet. Sci., 62, 291-304 (2011).

[9] Nogueira, A. C. S., Diceliob, L. E. and Joekes, I., "Perspective about photo-damage of human hair," Photochem. Photobiol. Sci., 5, 165-169 (2006).

[10] Mertz, J., [Introduction to Optical Microscopy], Boberts & Company (2010).

[11]Zhu, H., Isikman, S. O., Mudanyali, O., Greenbaum, A. and Ozcan A., "Optical imaging techniques for point-of-care diagnostics," Lab Chip, 13, 51-67 (2013).

[12] Ik-Hyun, L. and Choi, T., "Accurate registration using adaptive block processing for multispectral images," IEEE Trans. Circuits Syst. Video Technol., 23, 1491-1501 (2013).

[13] Qing, Z., Wu, B. and Xu, Z., "Seed point selection method for triangle constrained image matching propagation," IEEE Geosci. Remote Sens. Lett., 3, 207-211 (2006).