X Optimal Feature SelectionThe Mass Detection in MammogramsM98G0202

OUTLINE

: (Feature Space)

:

:MIAS Mini-Mammographic Database322 :X X 207 115

:Otsu[m, n]T=m(m:n:)m, n > 1 n > m(Within-class Variance)(Between-class Variance)

:OtsuMCS MCS(T) T=T+1,234T=nMCS(T)T

:[m,n]T = m(m:n:) m,n > 1n > mW (co-occurrence matrix)TW

(BB)(BO)(OB)(OO)

:3.HLE(T)T = T +1,3.4.T = nHLE(T)T

:(a) ROI(b) Otsus (c) Entropy Thresholding (d) Otsus (e) Entropy Thresholding

:()M (basis block)M

M1/2M10N(S)

:SN(S)log N(s)log(1/s)

::P2A:P2A

:

:

:V ={V0,V1,,V8}V0:

:(Texture Unit, TU)TU={E1, E2, ,E8}01 2 6561

:

:10

: 33 (Texture Feature Number, TFN)

:(Eigenvalue)(Eigenvector)

:1. (Chromosome) bit 0 1 2. (Population)

:3. c bibit i,mass ROI i i,normal ROI i i,massROI i i,normalROI if(c)

:4. (Parent Selection)(Crossover)(Mutation):(Roulette Wheel):bitbit(Exchange)(Offspring):bit 101

:5. ()()

:Step1(Pool)212 Step2Step3F-enter function13

:Step4F-remove function7 Step5Step2Step3 Step4 X 7~13

:(Mahalanobis method):(Testing) :i:AB 1:X:(sample Matrix)DADB:

:(Back propagation Neural Network)(Sigmoid Function)

:(Probabilistic Neural Network)(Radial Basis Function Neural Network)

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