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 in the lung typically round in shape.

    • Lung cancer maor cause of cancer related

    death.

    • "#$ of lung nodules represents lung

    cancers.

    "Dept. of %C%

    LUNG NODULE CLASSIFICATION WITH MULTILEVEL PATCH-BASED CONTEXT 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%

    LUNG NODULE CLASSIFICATION WITH MULTILEVEL PATCH-BASED CONTEXT ANALYSIS

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    +LOC, DI-R-/

    (Dept. of %C%

    LUNG NODULE CLASSIFICATION WITH MULTILEVEL PATCH-BASED CONTEXT ANALYSIS

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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%

    LUNG NODULE CLASSIFICATION WITH MULTILEVEL PATCH-BASED CONTEXT ANALYSIS

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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

    • "ocetric 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%

    LUNG NODULE CLASSIFICATION WITH MULTILEVEL PATCH-BASED CONTEXT ANALYSIS

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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

    LUNG NODULE CLASSIFICATION WITH MULTILEVEL PATCH-BASED CONTEXT ANALYSIS

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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 CONTEXT ANALYSIS

    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) /inHhao Lee) 9un

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

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

     ased "otext alysis*) I%%% transaction on +iomedical

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

    F"G. / 2 2asna) o

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