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)
90%30X
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