2-19 [ 1] : B-mode ultrasonic image (liver) : : ( US) : : (?) (B-scans) DICOM, Video, ), -177

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1 &

2 -9 [ ] : B-mode ultrasonc mae lver : : US : :? B-scans DICOM Vdeo -77

3 3-9 : [ ] -77

4 4-9 [ 3 ]. feature selecton. Sampler Feature Generaton Feature Reducton Feature Selecton MDC desn B-scan mae data Optmum MDC - [ 3] C C : : Z Z 5 Z 5 0 Z Z3 Z4 Z Z 3 Z 4 Z

5 5-9 Z Z Z Z Z Z Z Z Z U Z Z U Z Z U > 0 classz U < 0 classz 8z +.6z = 7.8 U : U = > 0 classz U : U = < 0 classz U 3 : U 3 = > 0 classz U 4 : U 4 = < 0 classz -77

6 [ 3] k U Z ku Z k mn{ U} = k U classz k -77

7 7-9 : - : Eucldean Mahalanobs : Least Squares MDC [ ] : -77

8 8-9 [ 9] -77

9

10 0-9 : &. Feature Generaton. Feature Reducton 3. Feature Selecton : : : # : # -77

11 -9-77 FEATURE GENERATION :. :. ray levels [ ] : mean varance skewness curtoss R ave N R ave N var 3 3 R ave R ave skew N 4 4 R ave R ave curt N. ray levels : anular second moment contrast correlaton [ 4] N N asm p f } n N N N n contrast p n f 0 {} y x y x N N corrl p f {}

12 -9 3. ray levels [ 6] run-lenths RF3 GLNU N Nr r N Nr 4. r FEATURE REDUCTION : features. [ 5] features : 3 4 features features: N C 4 N N C N N N N C3 N3 O N 6 N N N N 3 N4 4 4 T-test F-test -77

13 3-9 FEATURE SELECTION : features. features Leave--Out & - : Class-NMDC Class-AMDC Class-NTRUE 40 7 Class-ATRUE 3 30 Correct% = [c]/ [c] = 70% : Leave--out features dm & MDC -77

14 4-9.. feature en. 3. feature red. 4. features & MDC Ornal B-scan mae data B-scan mae sampln data features Full feature trann patterns features T-test features Leave--out Optmum MDC confuraton -77

15 x x5 3. x50 & bas spatal co-occurrence matrces N xn. st order statstcs [f -f 4 : mean varance skewness curtoss]. nd order statstcs [f 5 -f 6 : Haralck] features T-test : =0.00 n N =50 n A =00 features Sorted feature selecton: feature 03 -> tval=4.73 -> +[Selected] feature 5 -> tval=.849 -> +[Selected] feature 0 -> tval= > +[Selected] feature 4 -> tval= > +[Selected] feature 06 -> tval= > +[Selected] feature 04 -> tval=.338 -> [Reected] feature -> tval=.839 -> [Reected] feature 0 -> tval=.896 -> [Reected] feature 3 -> tval=.440 -> [Reected] feature 08 -> tval= > [Reected] feature 05 -> tval=.85 -> [Reected] feature 0 -> tval= > [Reected] feature 09 -> tval= > [Reected] feature 07 -> tval= > [Reected] feature -> tval=0.0 -> [Reected] T-Test Results: selected 5 out of 5 features lmt=.35-77

16 6-9 features { 3 4} features Leave--out features features Class-0 confuraton: MDCnfo: ta = { 3 5 } center = [ ] stddev = [ ] Class- confuraton: MDCnfo: ta = { 3 5 } center = [ ] stddev = [ ] CPMnfo: cn ca pn: pa succ = 73.3% -77

17 7-9 OPTIMUM MDC: Class-0 confuraton: MDCnfo: ta = { } center = [ ] stddev = [ ] Class- confuraton: MDCnfo: ta = { } center = [ ] stddev = [ ] CPMnfo: cn ca pn: pa succ = 80.0% OPTIMUM MDC Confuraton fle:

18 8-9 & features features st & nd order features RF-RF5 run-lenths : Least Squares MDC : BP - GUI Wn3 SDK mult-threadn -77

19 9-9 [0] Raeth Schalaps Lmber Zuna Lorenz Kack J. Lorenz Kommerell Danostc Accuracy of Computerzed B-Scan Texture Analyss and Conventonal Ultrasonoraphy n Dffuse Parenchymal and Malnant Lver Dsease J Cln Ultrasound 3:87-99 Feb. 985 [0] Bocch Coppn Domncs Vall Tssue characterzaton from X-ray maes Med. En. Phys. Vol. 9 No 4 pp [03] Feature Selecton n Pattern Reconton [04] Haralck Shanmuam Dnsten Textural Features for Imae Classfcaton IEEE Trans. on Systems Man and Cybernetcs Vol.smc-3No.6 Nov. 73 [05] Lersk Smth Morley Barnett Mlls Watknson MacSween Dscrmnant Analyss of Ultrasonc Texture Data n Dffuse Alchoholc Lver Dsease Ultrasonc Iman [06] Galloway Texture Analyss Usn Gray Level Run Lenths Computer Graphcs and Imae Processn [07] Bleck Ranft Gebel Hecker Westhoff Thesemann Waner Manns Random Feld Models n the Textural Analyss of Ultrasonc Imaes of the Lver IEEE Trans. on Medcal Iman Vol. 5 No 6 Dec. 96 [08] Haralck Statstcal and Structural Approaches to Texture Proceedns of the IEEE Vol. 67 No 5 May 79 [09] Khotaznad Classfcaton of Invarant Imae Representatons Usn a Neural Network IEEE Trans. on Acoustcs Speech and Snal Proc. Vol. 38 No [A]

20 &

21 - -6 NNC : MDC/NNC separaton levels & trann sets : NNC separaton levels Leave-Half-Out LHO : NNC & layers MDC-optmum trann set. : NNC & layers LHO. -77

22 - 3-6 TRUTH TABLES MDC: sp=0% p=750 LOO CN CA PN 7 33 PA 8 48 Correct = 73% NNC L=n=5: sp=0% p=750 CN CA PN 9 3 PA Correct = 67% NNC L=n=5: sp=0% p=750 CN CA PN 5 98 PA 389 Correct = 7% NNC L=n=5: sp=0% p=50 LHO CN CA PN 66 4 PA Correct = 879% NNC L=n=5: sp=0% p=50 LHO CN CA PN 68 3 PA 9 37 Correct = 884% -77

23 - 4-6 SIFX: sm. data ns/asp/asm feature calculatons lv sdm rlm. FCD: T-test feature selecton feature combnatons -3-4 & Leave-One-Out method. BPM: NNC param. topoloy maxepoch oodval% sets LHO: trn=50% val=50% tst=00% total #wehts shuffled patterns x50 smulated data. Set-: 750 patterns full set Set-B: 3 50 patterns LHO method Input: feature values -9-77

24 - 5-6 : > 85% %-0% feature set { f 0 :5 f :3 f 05 : f 07 : f 9 : } NNC MDC. NNC T-test features MDC-optmal set. LHO full feature set. separaton level 0% -layer NNC -0% -layer NNC. -layer NNC -layer NNC n=5. pa/ca. NNC BPM MDC FCD. NNC. NNC teratons 400 epochs -77

25 - 6-6 [0] A. Tanenbaum Y. Lansam M. Auensten Data Structures Usn C Prentce-Hall Internatonal Ed. 990 [] [] [3] 3 & 990 [4] I. Guyon J. Makhoul What sze test set ves ood error rate estmates? IEEE CS [A] 3] 4] : &

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