lung nodule classification

8/17/2019 lung nodule classification

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LUNG NODULE CLASSIFICATION

WITH MULTILEVEL PATCH-BASED CONTEXT ANALYSIS

GUIDED BY PRESENTED BY

Ms. M J JAYASHREE REMYA M M

HOD OF DEPT. ECE M2 TCE

NO : O7

1Dept. of ECE


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INTRODUCTION

• Lung is magnificent organ that performs

vital functions in every seconds of our

lives!!

• Lung nodules are small masses of tissue inthe lung typically round in shape.

• Lung cancer maor cause of cancer related

death.

• "#$ of lung nodules represents lung

cancers.

"Dept. of %C%

LUNG NODULE CLASSIFICATION WITH MULTILEVEL PATCH-BASEDCONTEXT ANALYSIS


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INTRODUCTION &Cont...'

• There are ( types lung nodules)Well-circumscribed (W)

Vascularized (V)

Juxta-pleural (J) Pleural-tail (P)

Figure: 1- 4 types of odule *Dept. of %C%



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+LOC, DI-R-/

(Dept. of %C%



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+LOC, DI-R-/ DI0CRI1TION

LDCT

1-TC2 +-0%D DI3I0ION• 0uper pi4el formulation

•

Concentric level partition 5%-TUR% %6TR-CTION

• 0I5T

• /R78L+1

•

2O CONT%6T -N-L90I0 CL-00I5IC-TION

• 03/

• pL0-

:Dept. of %C%



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1-TC2 +-0%D DI3I0ION

• 1artitioning the original image into order less collection of

small patches.

• Does not have fi4ed si;e and shape

•

" steps• !uper pixel formulatio

• "ocetric le#el partitio

0U1%R 1I6%L 5OR/UL-TION

• Dividing an image into multiple segments.• It reduce spurious la shift clustering method.

?Dept. of %C%



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1-TC2 +-0%D DI3I0ION &Cont...'

@UIC, 02I5T CLU0T%RIN /%T2OD

• /ode see>ing algorithm.

• Cannot shift in an iterative Aay Aith)Image amplification

DoAn sampling

• @uic> shift is applied to an amplified image Aith "

parameters)Kernal s!e"#$% used to estimate density

Ma& 's(")$% ma4imum distance


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1-TC2 +-0%D DI3I0ION &Cont...'

• I/-% -/1LI5IC-TION

The image is amplified Aith nearest neigh


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CONC%NTRIC L%3%L 1-RTITION

CON0TRUCTION• Divide the patches in one image into multiple

concentric level.

•

+ased on distance


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5%-TUR% %6TR-CTION

• 5eature set is e4tracted for each patch of the image.

• 5eatures are intensity) te4ture and gradient.

• 50* features are)0I5T &scale invariant feature transform' descriptor

/R78L+1 &local


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0I5T D%0CRI1TOR

• Invariant to image translation) scaling) rotation and

illumination changes.

•

Overall description &intensity) te4ture and gradient'.• enerates a E"7 length vector near the centroid of

each patch.

• 0I5T &pao' calculated ey point

near the centroid.

Dept. of %C% EE



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2O D%0CRI1TOR

• 2istogram of oriented gradients.

• Cannot handle rotation invariant pro


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/R78L+1

• Com


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CONT%6T -N-L90I0 CL-00I5IC-TION

• La


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03/

• 0upport vector machine.

• 5or lung nodule pro


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pL0-

• 1ro


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TOOLS AND DATABASES

• Using image proceesing tool in /-TL-+ softAare.

• Data


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1%R5OR/-NC% /%-0UR%0

Dept. of ECE 1(


LUNG NODULE CLASSIFICATION WITH MULTILEVEL PATCH BASED


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CONCLU0ION

• Relia


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)EFE)ENCES

FEG. 5an Hhang )9ang 0ong) eidong Cai) /inHhao Lee) 9un

Hhou) 2eng 2uang)0himin 0han) 5ulham /..) 5eng D.D.)

&"#E(') J $ug %odule "lassificatio Wit& 'ultile#el Patc&

ased "otext alysis*) I%%% transaction on +iomedical

%ngineering)volumeK?E) 1age&s'K EE:: EE??

F"G. / 2 2asna) o

lung nodule classification

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