Feat u re Ext ract ion from Sy n ech ocy stis s p. PCC 6803 cell im ages By Sh ylaja Kokoori. Ad visors Dr. Robert W. Roberson Dr. Rosem ary Ren au t

Size: px

Start display at page:

Download "Feat u re Ext ract ion from Sy n ech ocy stis s p. PCC 6803 cell im ages By Sh ylaja Kokoori. Ad visors Dr. Robert W. Roberson Dr. Rosem ary Ren au t"

Felicia Porter
6 years ago
Views:

1 Feat u re Ext ract ion from Sy n ech ocy stis s p. PCC 6803 cell im ages By Sh ylaja Kokoori Ad visors Dr. Robert W. Roberson Dr. Rosem ary Ren au t

2 In t er n s h ip Where Dr. Robert W. Roberson an d Dr. Allison van d e Meen e, Sch ool of Life Scien ce, ASU When Su m m er Sp rin g 2004 Wor k a tm osp here Con gen ial Op p ortu n it y to in teract with biologists an d u n d erst an d th eir p ersp ective. See exp erim en t p erform ed At ten d Lab m eetin gs

3 Pr o b lem St at em en t Extra ct in clu sion s su ch a s ribosom es, th y la k oid m em bra n es a n d fila m en ts from Syn ech ocys t is cell for 3 D a n a ly sis a n d m od elin g, to h elp u n d ersta n d th e ba sic cell biology of th is orga n ism.

4 In t r o d u ct io n Elect ron t om ograp h ic s lice t h rou gh Sy n ech ocy stis cell

5 High m a gn ifica t ion s h owin g t h ylakoid m em b r an es

6 High m a gn ificat ion s h owin g rib os om es (arr ows ) an d filam en t s (a rr ow

8 Sy n ech ocy stis s p. PCC Non - n itrogen - fixin g cyan obacteriu m an d an in h abitan t of fresh water Disp lays a u n iqu e com bin ation of h igh ly d esirable m olecu lar- gen et ic, p h ysiological, an d m orp h ological ch aracteristics Most p op u lar organ ism s for gen etic an d p h ysiological stu d ies of p h otosyn th esis Sm all an d su itable for qu an titative th reed im en sion al u ltra stru ctu ral an alysis Closely related to th e an cestors of ch lorop lasts

9 Tr an s m is s io n Elect r o n Micr o s co p y Tran sm ission electron m icrograp h s are 2D p roject ion s of a 3D sam p le. Meth od ology Prep are th e sam p le an d in sert it in to TEM Param eters su ch as tilt ran ge are set u sin g a software Im ages acqu ired u sin g CCD cam era at d ifferen t tilt an gles Im ages p rod u ce by TEM m eth od sen t to UNIX workstation for 3D recon stru ct ion Provid es in form ation on stru ctu ral com p osition an d organ iz ation of cellu lar

10 Exis t in g Segm en t at io n Met h o d Segm en tation in electron tom ograp h y is alm ost a m an u al op eration. Segm en tation is often th e m ost tim e con su m in g an d su bjective step in th e p rocess. Alterna tiv e: Try ing to auto m ate the pro ce s s us ing im age pro ce s s ing algo rithm s and m inim iz e m anual inte rv e ntio n

11 St ep s in vo lved Preprocessin g - Filter im age t o red u ce n oise Featu re Extraction Post Processin g - Im p rove segm en ts extract ed based on ap riori in form ation Im plem en tation option s: Morp h ological op eration s Th resh old in g Watersh ed segm en tation Differen tial equ ation s Neu ral Net works : Bet ter solu tion s can be obtain ed bu t takes t im e to train th e

12 Lan gu age an d To o ls Pyth on Pyth on Im agin g Library SDC m orp h ology toolbox for Pyth on Matlab

13 Mo r p h o lo gy o p er at io n s Meth od for im age an alysis based on sh ap e. Aim is t o tran sform th e im age in to sim p ler on e by elim in atin g u n wan ted in form ation, u sin g a stru ctu rin g elem en t. h t t p :/ / r kb.h o m e.cern.ch / r kb / AN1 6p p / n o d e1 7 8.h t m l

14 Mo r p h o lo gy o p er at io n s Basic m orp h ological op eration s are erosion an d d ilation. Su p p ose O rep resen ts th e im age, an d S th e stru ctu rin g elem en t Erod ed im age is th e set of all referen ce p oin ts for wh ich S is com p letely con tain ed in O. Dilated im age is th e set of all referen ce p oin t for wh ich O an d S h ave at least on e com m on p oin t Op en in g is d efin ed as an erosion followed by d ilation

15 Morphology operations- Results

16 Mo r p h o lo gy o p er at io n s is s u es Segm en tation of featu res with low con trast is d ifficu lt Extract in g lin es an d cu rves d ifficu lt Solution: Mod ify the a lg orithm to m a k e use of the curv a ture p rop erty

17 Cu r ve s egm en t at io n Differen tial geom etry u sed t o d etect 2D cu rves in im ages Th e cross section of a cu rved featu re su ch as th ylakoid m em bran e h as a gau ssian - like p rofile. Th is p rop erty can be u sed to extract th e featu res Algorith m Calcu late secon d ord er sp atial d erivative Ixx, Ixy= Iyx, Iyyof th e im age I(x,y). Sp atial d erivative of an im age is obtain ed by con volvin g th e im age wit h d erivatives of gau ssian.

18 Co n vo lu t io n Con volu tion is a sim p le m ath em atical op eration u sed in im age p rocessin g op erators Con volu tion p rovid es a way of ` m u ltip lyin g toget h er' two m atrices, gen erally of d ifferen t siz es, bu t of th e sam e d im en sion ality, t o p rod u ce a th ird m atrix of th e sam e d im en sion ality.

19 Co n vo lu t io n Exam p le im age an d kern el to illu strate con volu tion I I I I I I I K11 K12 K I18 I19 I21 I22 I23 I24 I25 I26 I27 I28 I29 K21 K22 K23 I31 I32 I33 I34 I35 I36 I37 I38 I39 I41 I42 I43 I44 I45 I46 I47 I48 I49 I51 I52 I53 I54 I55 I56 I57 I58 I59 I61 I62 I63 I64 I65 I66 I67 I68 I69 Used in im age p rocessin g to im p lem en t op erators wh ere th e ou tp u t p ixel valu e is a lin ear com bin ation of cert ain in p u t p ixel valu es. Th e valu e of th e bottom righ t p ixel in th e ou tp u t im age will be given by:

20 Gau s s ian Eq u at io n Gau ssian equ ation is given by: G(x) = 1 exp [- (x- µ)2 / 2σ 2 ). σ 2 for a con tin u ou s ran d om variable x, wh ere µ is th e m ean an d σ is th e stan d ard d eviation for th e gau ssian For Gau ssian kern el th e kern el wid th is th e stan d ard d eviation

21 Algo r it h m. Determ in e th e h essian m atrix, H Th e Hessian is th e m atrix of p artial secon d d erivatives. So th e Hessian m atrix of a fu n ction I: R2 - > R is: d2 I 2 dx H := 2 y x I I x y 2 d I 2 dy 2

22 Algo r it h m. Fin d th e eigen valu es an d eigen vectors of th e Hessian m atrix Largest eigen valu e an d th e eigen vect or corresp on d in g to it in d icates th e stren gth an d d irection of th e cu rve. Filter th e p ixels wh ich m eet s th e cu rvatu re p rop erty

23 Curve Segmentation- Result Thylakoid Membranes

24 Curve Segmentation- Result Filaments

25 Curve Segm entation- Issues Followin g cu rves wh en on e crosses an oth er Solution: Ba sed on the d ir ection of the p rev ious p oint a nd look ing a hea d of the current p oint p red ict the p rob a b le d irection. Tracin g fain t cu rves Curves that fad e Curves that d isap p ear and reem erge Solution: Once a curv e end s look for its p ossib ility to rem erg e

26 Fu t u r e Dir ect io n Fin e tu n e th e algorith m to au tosegm en t in a tim ely m an n er stru ctu res of in terest obtain ed from Electron Tom ograp h y d ata Develop a User In terface in ord er to m ake th e p rogram m ore u ser- frien d ly an d t o let th e u ser ed it th e resu lts obtain ed, if n eed ed.

27 Sk ills At t ain ed Un d erstan d im age m in in g an d im age p rocessin g better. Ap p lyin g m ath em atical m et h od s to solve p roblem s Better u n d erstan d in g of biology sid e

28 Refer en ces Lich en Lia n g, Qia n g Ji, a n d Bru ce McEw en Extr action of 3D Micr ot u b u les Axes fr om Cellu lar Electr on Tom ograp h y Im ages. 16 t h In ter n ation al Con fer en ce on Pat ter n Recogn ition. 1: A. Bartesa gh i, G. Sa p iro, S. Lee, J. Lefm a n, a n d S. Su bra m an ia A n ew ap p r oach for 3D segm en tat ion of cellu lar tom ogr am s obtain ed u sin g th r ee- d im en sion al electr on m icr oscop y. In s tit u t e for Math em atics an d it s Ap p licat ion s, Decem b er Pr ep r in ts #1950. A dam Hu a n g Th r ee- Dim en s ion al Biom ed ical Im age Segm en t ation An d Vis u aliz ation : A Sh ap e- Bas ed Ap p r oach. Ph.D. th es is. Ar iz on a Stat e Un iver sity. h ttp :/ / jh h.op i.u p m c.ed u / m ain / p op / u p load s / Pr ob lem Or ien ted Pr ogr a m m in g h ttp :/ / b.u am.es / ~ b ioin fo/ p ap er / etb ios p e/ etbiosp e.h t m l h ttp :/ / ls web.la.as u.ed u / Syn ech ocyst is / tom ogr ap h y.h tm bio3d.color ad o.ed u / im o d/ h ttp :/ / yth on.or g/ h ttp :/ / tw.n l/ p r or is c/ p r oc / sch r ijver.p d f

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N