Designing an automated system for medical diagnosis

1.5 MB·English

by  Parekh, Ranjan

57

Designing an Automated System for Medical Diagno:

Abstract

This paper proposes we auiomaied spsiem for
rengnising disease conditions ef nema skit tn
nates! t0 eal faformatics. Skin texte images
‘hpioying thvee dermatological stin conditions, ae
fai cedusing atertureanahsieiehinigue, Based oa
Set of normalized symmetrica! Grey Level Co-
deuumence Marices (GLCAM) and fetes are
enacted feom zhom using automated algorithms. The
Features are fed i neural necwork elseif for
Monica of the disease pe. The jentures are
onsdered in vevigus combvnations vis, tivity:

joint Data 3D feature spaces, fin ut the best

Keywords

Metical image anal, texture recognition, grey level
evocourene male (GLEM), seul ‘eons,
umpulee vision

L Introduction

rere, omens vile mcnhloiheva
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Smetne fr nomad. Gagnon ot cocees
Inert ir aooml hat dag as beh
tiga ty he hie olan ef nal ages
fer ceyiny hi ove he worl Sur eg
Snags Depurnentot ie Caroma espe
fdereaine aces moe hana tage
ty Tle, 2004, aban digs se

fe owe gst potas fr peli gna
frend inproving he cosine an Sosy af
Sadia dagnnie“agnnte eros te’ Boge
¢ouperpaononsaseihopl siya epored
tials aguas 1987) Inte Cael es
thee mde Teale 4.00998. 000
Somes deh each stand 1H D0 eases

in medizal analysis hac shown great promice in
redengatch eres [Copee, 2003]

Automated diagnostic systems based on
nelical imaging, work DY using image processing

VOLUME 3, NO. 1, JANUARY, 20)

Ranjan Parekh

techniques to recognize and formate disease
characteristics frem digital images, Two ixporin’
‘Steps are used : (1) Vishal ftures are extract fot
ths images and represent using a mathe eal Cala
model (2; the data representation is then
Siatitical classifier like a neural newark
‘etificaion and class eatin of hedisease

Innaye features usually involve the following either
{ndisidoally rin various combinations cole, texture,
Shape, fo this poper we foeus on using tenure as
‘means of idortifying akin divenses som medal
fmages using an automated procedure. Texte peers
fo viel pattors for descr the waration of exlor
fr grey tones over the image, The eboies af the
Teatives, depend on what characteristics we are ying
lo identify fram the image tht best desenibes the
diseases in question. In many cases computer system
designers need in take help from medical professionals
to isolate the best set uf features: most useful in
deseciting 4 specific disease condition. ‘The
‘organization of the paper is as follows section
provides en overview at “he related Work, sexton
futlines the prapasec approaches with diussionso1
overview, feature eorspatation and. classification.
Schemes, section 4 provides details ofthe daset ane
fexpenmenal resus obtained, sectien 5 provides a
Analysis of te current work Visa-vis other tatee
‘works, and section 6 provides the oxerall cone usior
and scope for futher researc.

2.Related Works

Many methodologies have bsen propose to analyze
and recognize textures aid shapes tn an atsomaed
Fishion, One ofthe fist studies nvolvee derivation of
texture energy measures using, 2st of simple masks
(Gerticl, hurizonual, diagonal and_aatidiagoral)
Wang, 1986). Authors like Taasura (Jamnuet, 1978)
made en artempt at defiang a set of visually relevant
tent features, This melades coarseness, couttast,
liteeionality, lne-hkeness, regula, roughsess.
Pentland (Pentland, 1984 reports a high detec of
correlation between fecal dintensions and human
estimates of roughness. Th-slate Merkow models
hhsve(been used to detect ientureedposcharacterizedoy
changes in fist order stasis [Muang, 1984]. Gabor
fliers have been used in several image analysis
applications including texture slasiication and
segmentation Bovik, 1990)
58

Spevifcally, computer vision techniques involving
lekluse analysis have heen applica wo" health
Ilotates to predict and eherastenze skin diseases
NORA eal [Alabbat 200) have proposed
2 imetod forakin texts rasogntion using 23 ayer
fstr network sng Both skin color and texte
iss im [Robsuns 2092" wutharspropossamethod
ff divans af pigmented skin lesions by ng 8
gia Germs snasvr to evalinte a sone oF
‘nical lees Mat pigmented sbi lesions acl
pater such es Tacunari and ret cimansions
fave been" used) in diggnows of skin cancars
Blactledue 2009). The se of Basesian nesworks far
Skin tealire ceccynition has beet reported in
iStatveza, 2008). A zeview of image analysis
lecanigaes for sued danoss eat Be Sound
iMuitcs 500

3. Proposed Approach
3.1GLOM An Overview

This section describes how a popular texture modeling
tecmique called Grey Level Co-uecurtense Mat.
IGLCM? can Ge ised fo model texsurs content in
images, A GLCM (Hacaliek, 1579] ‘indicatss
Prebability of a grey-leve. 1 occurring inthe
heighdouthood of grey-levol j at distance d and
‘rvs

id) o

GLOMSs can be computed rom texture images using
dierent values of Wand tad these ponablty values
treate the so-ageureace matric Consider a4 hy 4
Seve ofan nage having Tear wreyleyel intensities
sshown

Do

0

Te cempute the faqueney of ene ays. tone in the
Boood af ots 214 man onmed ince
theta forcistnct grey tones) and scenarios
Song the lf (referee) sn op asi) 8
iste the
tre frig s
sow emp for: efernge grey-one or ma
tines the negbour epy-ome curs nea en I,
fn ts ovatus the (th emert of GLCN orn
Gor sly se we conser he steed
ie-nly alse piel ars emanate as
JEclonsthepovtve coe ea ight

Fr. pagation

Forexample, (reference) adjacent io 0 (neighbour) n
Toceurs 2 times (rows 1 and 2), heres Wy2 pul 2
pasitinn (00) thadjscentt 1 peruse Lines (ms
Tare 2) nence (0, }conains 2, 0 adizeentto2 cecats 1
time (row 3) hence (0,2) conains 1 anc 50 an. This
Prceedureisrpested forsllpairsoF intensities,

If we hed moved along the -ve x-axis, 1. we had
toakes froma right te Tel then she ati frend auld
have been the anspose mantix G. To meke dae mate
inlepenest ot this tata, Ute nape side tbe
drignal:omake tsymimetica’viz.8-G—G"

2210) [200 0)[a2
0200/2 20a/\24
Se oosriosaflic
0001 loolilloc

The symmetvicsl GLOM is finaly. normslized by
dividing esch sloment by the sum of all elements fo
“arm $y. The "0" nthe subscript indicates angle
Dureeitanal GCMs ean als be computed along three
ther escetions vertical (0 90", ht diagonal (0
459 and lett diagonal (@/= 135°) generating matices
SoS aid S

@

“

L

06

20

at

ig
fooad
nile 29
Se"pala 2 2 2)
lon2a
aa
atid
Swrisls 1 0
oz

3.2 GLCM based Features,

An S-bit grayscale image typically contains 256
aierent grey tones which generates GLCMS havin
59

‘vith Such large matics, 9 set of sear features are
nally derived Pom directional morinalized
symatetrical GLCMs and used for. texture
fhacacteization viz. GLEM. Conteast “©, GilCM
Homogeneity (ID), GLC Mean (NM), GLCM Variance
(and GLCM Eherey (N} as defined in Ey, (2) Here
Sig tepresents the slement (ij) 0° a aomlized
symmetries GLE and ene mcimber of ary evel,

ob sweat

Atexure classi consists ofa set fn member images:
Tooat» Foreaeh mentber

senmetical wamalzed GLEMS ate compute 3s
ingles below

{Meta tantd

lett

For ach directional GLCM, features in Eq, (21 ate
sonputed, “Lach ‘caure is averaged ove" the four
Seeional GLCMs, foreach member magevi2,

hh

where t_stathathatho soa xe (C,HM.N.V}
Atextiré cas is ebaratarzed by the collection of ts
{este values obtained during a frssing phase test
image Sw th is compte averape Tetares [8 1
seid ta’ helnrg, oa specific texture class the
probability ofits fate values eng # emer ofthat
traning clas. is maximatr. To. vos class
prebsnility neural tetwrk classifies (muli-ayer
Pereestton MLD) sing. feed-forward hack
Drupagation architectures are emplayed inthis work

:
:
7
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34 Neural Networks : An Overview

Artifical rowral networks area se of algnits mean
io sirsulate working of ussan nerve cls or net tons.
‘A neuron reesives input atti tom a numberof
soareesusingancleetto-chemical proesssanl rodeos
response (fies when eamcentatin electrical
‘charges exceeds cera threshold. An sical newal
Unit isabsovisalizodas aseuctone having inp nd
each input char! | ear eatry a signal 8 The neal
luni ea produce an output o then thes of the apt
Signals zxeseds a ceviaig predefined threshold
whe 4 98 SoeFig

Fig. Amica neuron

Ose of the easiest neurons is the MeCulloh- Pts
neuron where the inputs and outputs ac eo sidered ts
binary values. Pig sows srapl nilcmestations at
"AND! and "OR" lugial gator using” Mec lol Pitts
neuranshaving treshalesaf and espectively.

+ 109,11 Ano

ee

= B.40,1)

Nous
wnfon

Fig, 2. Using MeCuob-Pits neurons to mplemoet ANS
sinlOR vical ets
60

Pru: pagation

A tural of Sone Camano

‘The perceptron was punpased ay a more generalized
model of the Milloh- Pits 2uron ae x onlays
tevtensively used in class fication problems in atigus
\ortains. The esseanst difference the pessence of
‘urietieal weights associated with input lines. See Fig
5. This imparts the perceptron ae capability ofa hy
Sclaping the weights to Suit aarulae classi feation
Jeovlen, Tae iuput signals and Weights are mow ro
loge restected to binary values bate lake om ary
real value. The inpursignals are designated by an input
settor X-f{r, sac} and the weights By a weight
\eetoe f/¥ sen). The pecesplren ao hss a bias
Tine cennecied otis prt {4 kept pere-anenty 4
andautsssoeiated bias weisht (Q), Theme inpat Neo the
Perveptran given by

wx Sew

a

The oufpu 9 prxduced by the peregption is no longer
sternined by & threshold value bul by a earsier
fnetnn Jn rust eases the Wansterfumeson i
Iba:sigmioig orth tn-sigmo foros depicted bolow

On 8

6

ono

Fie SThepeswepzon

Josolvea problem requires uve steps: taining phase
sand tesine phase: Dun te tmng pase, # set oP
inputs are fed to the neal une end the outs ey
should produce are lio newn, and these er eld
faruets. For example, (or simulating ar AND gate wa
Jnpct of [1,0] should pradace a target of [0] ad
lnpot of 11] should produce a targer of [1] Th:

\seighis are not known, so ini esinats areassumed
foften candor values o all zeroes). The ata output
G produced san bo ealelsted fromm Fa. [3) ard Bq.)
above. I the aupot does match the target thes an
‘ent r prdiicdand this error is used ro mod Ty the

weights in such a way that subsequent ermoes ae
‘edkiced. This constitutes an ieratien sis calla sn
fepoc Ta the next Herston the inputs ar aga fed
the uni and the new weiahts gre used tn eal ot te
fame This process is repeated ieratively wnt the
forsareall educestn 7toorse:neprefinel spall
‘alte. The unit is sid wohave converged und the na
_woights are called the salanced weights. Te balanced
‘weighs provide a represeniation of he profiem
Per suce fr all inpus those weights pradice te
Soret oats

‘During the fessng phase, an unknown seco pats ae
{edt the neural untand hebaleaeed weiehs are then
used to caeulate the eoroct cpus, The power and
Meafoty of aeurs unis ie in he fact Ui the fest
‘inpits nged note exactly envi) eh any oF the
‘sini set inputs but only sim, forte percept
{o prodiece the correct decision, This propery 's
Iequenty used. to aolve.rutlem like patent
recoynition and charactor reeogeitun, where eaining
's lone using: separats sot et choraciers en testing
done on anctherset of simile characters ea product
tsinga different fon:

Fn nos sel-word problems ingtead ef sing sine
reweptron. we tne acer ko eon.ceted neal ui
tne we went mine outpus wh sar tne Such
Tensor oflen have multiple layers of seal ens
etveen dear and nt layer in which ace
called mul-ayred

ig, 4 Ancurl nena
In. MLP computations uiple extrs are produc at
‘the output comesperding to cach neural unit 303
‘culate eur called the Mean Square rrr MSF}
is used for updating the weights, as depicted lox,
lahere fis the error produced al hth outputunind
thereavem seh unite athe oil

we 186 6
agalion

hus of Seence Comair

61

4 Experimentations and Results
AA Dataset

Skins ngs uid fe, Doras
rneelstien i wdermpet co) ar sed
Frlegtnctatie fic date Sens of ta
180 rages cide flo hve chest clases ene
fClas-Aytehtyess (cls and Kertsi Close
Kyl Ob imgespe ease Each images 125 [28
esta dimensomundi GIF Me wmat Atul fC
Tings sie used or the Trung see (0) and ihe
teming 9 imagers the Tetinget 9). The mages
trocremtinineanderAc] Ke chefissueae OF
tages of ining (or esting) st belongs Class-A
theseu soon Clavel aad heat acto Classe
Serle ages cles 1 are shown in FigueS

Tor computing recition rats, features are fist
fice! inv iuly tnen a two disvosioal (2
DYleaurcepuceand ly inereeiengaasal oD)
ani spaces, volving seliple GLCM features
Smutondauty. Compton benscon wating std
Gazecs ex done sng mullayer percepts AL
Goh cage the hes rvals ae late andthe
<Gnesptding deinen plot are reproguced
SSelegendsysed mn tniworkareIstedin Table | ere
enetesaclss meiner easheeierAcrIocR

Fig 5. Spee of medial images bolenging teen
‘ononelasse(a) 4 O}LE

Table : Legends

42 Individual Features

‘ats ond GLO fas CH MN
snc in Ey 12) for aime ad testing aes Tor
Seehof ite ive uses ae computed. Tesemages ae
Seraared fo taining elite using NN clas es
AXccsucy cose are semaarzed ip TaMe 2 The st
alin eps ihe eure cael, ie second cela
Sto tetera network confguraion WSC) siz |

10°} ais Tap un fr nd eat),
{Wan in the hide lager an ite nts eu
layer (coresponding the 3 chases. to be
idtngaisied) "Tae ti fur and fy clas
ined the centage resogion azure or We
three clase, the sith column provides Me eral
Shreuracy Tor the thre elses and The se cohen
inet che best ean Sgucre Ecor (MS!) sbttted
{Tinh tang phase of Cie NN al eases the
IAN dssirs were ran fur SOON poss Feae
Salas were propre sce We within the range
Diol belonc beng Ted els

“Table? : Accuracy results forsingle Features

FORE TA | pepe we
Gps) a |e [ies| ese | as
aa ea] [03 | a
eee or

From able 2 it i observed. that est results are
rodiced by M(TS and ‘rtsspondisg
hls sie ng ho variation of ese feature vals foe
the hnee classes aver the reining ad fesing datasets
sre stow below

—
5

Pace
Ta tesa

o)
62 P-. pagation

‘A Joel of Sines Gomenicton

ry
Fi. 6 Peau plots fir "AM, TIM, TKM, SAM, SIM, Fig 7, Heme pls firs) TM vs. TN, SM, SNESITAL
SKM IF TAC TIC) TRE, SAE, SIC, SKE veil Mae ST

4.3 Joint Features in 2-D Feature Space 4.4 Joint Features
a improve upon the tens obtained using nds
Sout, joint Raruro ae net Conde, in ve
‘imensadol featur spaces ve CHL CM, CCR, CVI
MUTEN, HAE MAT ALY. ACV. Acca route
Ssinsarzed iTable 3. In all eases the NN chases
were ran or SUOD) epochs, Feature valies New GI cages the NN class fers were run for SODGH epcehs.
sppoprately scl lowe range Ot | Feature vals ore spproprinly sled oe wih
Table3 Accuracy resus orjoin 2D features HtangeD 1b

heen Table:
=

3D Feature Space

io improve upon the results obaned using advil
rues, jon feares ave next cansieed in tee
dlinensional feaare spgees he: CHM, Coll Ws Cll.
NOMEN, COMIN, CNN, FEV, HEMEN, IRS!
MAN-V. Accuracy isulisane suman Tables

rom ‘able 3 if ig observed at best resus are
Produced by MAN TsO) aud TEM (77-850, From Tebls 4 ie obserd tat best vests se
Corresponding plots ¢epicting the vayiation of taese prasuced by COMEN (82.270) and HM-V 173.324

7 : Tependog psn deci. ts emanate
fccahesloniediochatsover he tainngand (Seng ge ec I casio las
{testing datasets are shown below. testing datasets are shown belaw

@
P pagation

orl of Sense Connie on

63.

o
Fig § Fst plas foe (ah TC vTM vs. TN, SC va, SM
SSEN DTH TMee TM Sits, Sa os 8

5. Analysis

Automated discrimination betwson tres skin texte
nse was done ising.» vviery of epnenuches to fd
tteoptinmrents. Out Hive GLCM based features
tousicered individually. SM produced. the. best
fRevaniton rate of 73.5% Among joine 2-D feature
Spaces MN praduse te bes resn 7 80%. Joint 3-D
Tenure spaces were seen to imprave on the accufaey
fates to) S22% using CAEN, Rest results are
Siminavizod Slow.

“ble$: Best performance results

<Jelng20seiat30 |
MA CMA
soo 22

Dut ofthe disease classes, I was she best recounized
tata single Features (0.6%). whyle A was te best
st using 2-D features (8-Pe) ane 0)

te best recogoition resull Ly using a combination of
Coots, Mean, Energy ferlutes, was ebrained by
Ising 9 neural network having « cosfiguration of 3
103 so, input ums, 13D unis in the der layer
sand 3 oortumis. The tse Fetare esters C, MLN
fichtrsning sample, were fa wth three inputs he
earl ner and it wasn tis mannerby a total oT
50 sampes, 30 per ela, The perceptron took $0900
poche to cancerge 4 an MSF. value of CO4. The
ewer convergence ain! oper plets are shown
[ew The sutpat plot depicts a 86.6% accuracy Tor
rmovghizing disease A, 93.38% for Band 66.6% for C,
tech lass being represent ly 30st samals.

o

Fig. 9. NB convergence and cau pls r the C-MEN
feenrevector

spat theabove results in perspective wih the sate
thera, the best rosulis reported i [Shyu 1999] tor
FMentifing disse classes trom 302 lng. section
sages iavalving lestn’e fentures,humeseneity,
fonast,comition an elute it akin to other
euros lige rey evel histogram, is 76.3%. Accuraey
thr elassidetion of ROD eneoscopie images Ia [Xia
nis sing fusion of colo, exeuteandslipe Fates
ranges fem T71a 60, ony sboat 25% involving
texte features clone, Accuracy resulis repose 1m
‘lebbod, 2008) tested om 300 skin exe images
1% uses 9 color Ieaires in adi 0 4 Textare
‘atures, eso, ener, ewEus,hornogerciy

6. Conclusions and Future Scopes

“This paper proposes an automated syste for
feocemzng divense vonditons. of human skin by
bhalyzing skin Tlexiier images usiag texture
feocpniton feciques, Skin diseese conden dior
fn eppesranoe in 4 vay, which eannet be modeled
appropeciely by specific colors and can bea be
‘dentfiec using statstical variation of texture. Such
futomated medical agnosis sysems can prive
xtrinely usefl where Uaere miprt be a dearth ob
oval mneical profess. Ox ute ta this woul
Fe Nselul fox ckrmataiegists to reduce dlagnostic
tors, ale on the ahr cu seve as he intl test
Fed forpationts before seeking expen ac vice
64

agation

A ovine o Scenes Commmenon

‘The su revels that performance improved when
Thai foray sien feature of ivapproac x
the low comply dara sds ing cham whereby 8
‘all sue of salar value are aso fo rapreseat
Tonge eoment sent of ritedinensinal Peto
ie histograms. This, Males the system low on
mputaonafoxsrPesd and makes ae ye
"md al emptor
fRacurees cn he wares Lave resaances als py ow

‘The accuraey of the eurent system i compare 99
hase "sported in contemporary works. Mest af the
‘tes mnctes have deal with 24-bit imagss and have
Stilized color basod features in aedition to sexe
Dred features. [comparison the surrene were hes
nee St rages ard nny texte base! eames. Ls
{Sxpscied that sceuaey results ean Fe improved unon
fy asin (1) Celor fatares ang wath texte
employing GLCMs en individual R, GB colar
hanels of 24-bit images (2) Normalization of the
trnghiness and contast oe tmiges by pre

involving histogram squnizetien, helone
ote.

References

1. H.MulletN. Mishous,D. Bardon, A. Gelssbuhler.
NAcmvew af contenthased ewer retrial
sysfems in medical applications ~elnial ben:
fang fre directions. fay, J of Meaical
Tnsormates, vol 73,2008 pp. 1
DA Bes N Spell Dt Cullen E Burdick, N
Vir. Boteson, S15 Seal Hat’ Swett
Lo Leapo, “The costs of edverse drug evens i
hospi potent, Journal of the American
Mail Aasortin v0 277,164, 199%, pp 30%
an
NS. Wega RM. Wing, RW ier: 8
ign, "Epidesilegy 0° mecieal etror", 2A.
carom >anh pp 7
2. DCopes, Ml Kabit,D, Reigharh,O, Rotsehil
JAS Castielione.. "Hatran etrors. in medical
practee systematic eassificatian ang reduction
Sith automated icvormation sysierss" Joven! of
“Meatisa Sestome, 801, 27,n0, 4,083, pp. 297-313

wet

nin vac

De. Ranjan Preble Schoo! Eluction Tee

I. Wang, AR Hanson, EM Risoman, "Text

analysis bused on Ineal sandard ceva a

ittetsits", Pree Jos, Conf en Compote Vistar

Panter Recognition, ora, 186. pp 482-485,

6 TiTamuna 8 Mi. Yara, "Texel Peas
comsspending to sisuel perseptions" ELE
True tam Sese, Man aed Cyervatic, el
1.6, 1978, pp. 473

3. A PPontland,"Factl hosed dose
scones! JEEE Trans. Pater

inn of ata
Inns cond Mace

Inetigiace, wf no 6, 1984, pp 681TH,
RNC KS Huang. Markov model for ima
segmentation, Proc. 22nd Merion Cin -ov
Centanunicaion, Caveral “and Compucing.

Moniveeli, 1981 pp. 775-781

9, ALC Bove M, Cian WS Gish." Mukichae
texture anajsis sire oclid spa irs" FE
Tins. Panarn Anais ae MavtaeSneligene, vu
120.1, 199, pp 55-73,
IK. Al abbadi, N. §, Dahir, ZA Alksreen
kin oxtre reeggnirion using neural nonwores,
rics lig cl Uo on Information fecroloe
Tunisia, 2008,

11. P Rubeyh eta" Auinated disses of piprnened
skin sions, Jewwnat on Canes, xl 101, 2002,

ph. S768,

Pha. Blackledge, DA, Dubsitshiy, “Textuie

ixsificaton using. fractal seometty tor the

Ssgnosis of skin cancers’, FO UK Theory and

Praeee of Computer raphies, W. Ting. J

{Cellemose (eck }2099. pp! 8

1I3.S. Shalieza, M." Mousavi, "A. new Heyesian
closifier for shin detection”, Proe. al Tat Con
oy Innovative Compattag, Information and
Comino 2008, pp 7-178

1a.RML Hort ick, "Stic and stracarel sopasaches
to Tealre™ Pre JEEE. vol 67, 1975,p9, 7860,

IS.CR Shy. Ke 8. Kosala, “ASS'RT s phasic
inthe loop CBIRS fo: FIRCT inaue datbeses™. Comp,

Vision andl image Understanding, vol 75. rn. 199

pe lil

Siig, W. Mo, Z, Zhane, “A contre base treval

Systens_ lar endoscopic images”, “ot, ourel

Infomation Technology, vob TI, no. 2,215, pp
2732

16

sadn Univer, Kali i
bine ul ical aging: He the ane of tle book

Principles Malis: polis fy MG