Kiu, Siew Ming

Wang, Yin Chai

This paper is inte nded to assess [he feature ex trac lion te chnique for identificatIOn

and c lassificatIon of whi le blood ce ll uue to ove rl app ing cond iti on in ieuka CI11IJ d isease.

Accordi ng to the dala from SEER Cancer Stat istic s [ 1), leukaemia IS ont ofrhe lOp ten

most common type s of blood ca ncer. In lhe year 20 17, there are {)2 130 peopl e are

expec ted to be diagnosed and 24500 peop le 3re expected to di e. In ano ther word,

app roxi mately (he re are I pe rso n is diagn osed with a bl ood ca ncer in every 3 minutes.

Th esc da la have shown how hi gh the risk of le ukaemia to be d iagno sed. Therefore, an

accurate Image process ing approa ch is proposed for cha gnosis leu k.tlcmia

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Innovation Technology Expo CONFERENCE PROCEEDINGS IIDigitalising Sarawak Translational Research" The 11th UNIMAS 77-78 3uly 2078 Research & Development PULLMAN HOTEL, KUCHING Expos iti0 n www.rimc.unimas.my/intex78 organised b y UNIMAS Inn o vation ~ INNOVATION TECHNOLOGY EXPO (INTEX) 2018 CON FERENCE Kuching , Sarawak, Malays ia, Jul y 17-1 8,201 S ICCT-2 0 18 -13 Geometric Feature Extraction for Identification and Classification of Overlapping CeUs for Leukaemia Kiu Si e\\' Mingl. ' a nd \\f ang Yin Chai! I Facu hy of Co mputer Sc ience an d Ini'ormation Technol ogy >I< (E-ma Il: eve_kiu@holmail.co l11 ) Thi s paper is inte nded to assess [he feature ex trac lion technique for identificatIOn and c lassificat Ion of whi le blood ce ll uue to ove rl app ing cond iti on in ieuka CI11IJ d isease. Accordi ng to the dala from SEER Cancer Stat istic s [ 1) , leukaemia I S ont ofrhe lOp ten most comm on type s of blood ca ncer. In lhe year 20 17, there are {)2 130 peopl e are expec ted to be diagnosed and 24500 peop le 3re expected to di e. In ano ther word, app roxi mately (he re are I pe rso n is diagn osed with a bl ood ca ncer in every 3 minutes. Th esc da la have shown how hi gh the risk of le ukaemia to be d iagno sed. Therefore, an accurate Image process ing approa ch is proposed for cha gnosis leu k.tlcmia. After rev lew'ed ex ist ing studies, Ihere are a lot o f re lated mdhods was proposed. However, 1110S1 of exi stin g methods or algorithms had enc oLl 11l cred the ac curacy problem ill ce ll cOllntin g when ove riapplll g ce ll occurs. Furthermore, the higher the degree o f over lapping ce ll s, the lower the acc uracy or ce ll counting. Hence. the identifi ca tion of ove riapPlllg ce ll s is proposed in hope to improve the acc uracy of ce ll counti ng. Moreover. the c lassi fi ca't ion of ove rl apped ce ll s was not conducted befo re. In Illi s study, overl apped ce ll s are grouped ac cordin g 10 ove rlappin g degree a nd nU11lb..- r of ce lls overl apped. Acc u n~te Idemi ft ca tio n and clas ~ j fi "':i.:1l ion of ove rlapping cel ls \\ a ~ ex pected to furthe r in crease the acc uracy o f WBC segmentation and countin g by using a ll image process in g approach. There are 103 mi croscopic images used ill thi s study 3Ci: co ll ec (ed from a few online Im age databases, such as AS H Image Ba nk and Medi cR I Stoc k Jmage Another 164 rca l mic roscop ic images are ed iled by lI slIlg Adobe Pholoshop CS6 to fo rm diffe re nt magnlftcclti ons rangin g from IOOx 10 500x and saved in in the JP EG fo rmal Additi ona ll y, there are 100 artificial overlapping cell s images are form by crop off the s lJ1g le WB C from rea l micro scopic images to i'orm additiona l overlapp ing cases \-v1th differe nt number of ce lls. All Images wi ll be proce'lsed with M AT LA B R20 14a. Tbe ge nt' ral method for ohjec l co ulltin g cO lllai ns fo ur s Lep~ , namely ima ge acqu is iti on, image en hance ment , image se glll t":l1lal ion and object co ullllng. The proposed me thod in thi s st ud y ha s an addit iona l step in between image :;eg lllentatio ll and object co unti ng, "v hi ch is fea lUre ext raction (Figure I ). In th e proces-; o f fea ture e:xt ract ion, ove rl app in g ce ll s will be idenllfied and c lass ified IlltO di ffe renl group bClsed on overlap ping degree 50 

Geometric Feature Extraction for Identification and

Classification of Overlapping Cells for Leukaemia

http://ir.unimas.my/id/eprint/26738/1/Geometric%20Feature%20Extraction.pdf

Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia

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