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The CALMA project

The CALMA project. A CAD tool in breast radiography A.Ceccopieri, Padova 9-2-2000. C omputer A ssisted L ibrary in MA mmography. Screening mammography sensitivity (identified positives / true positives) 73% - 88% specificity (identified negatives / true negatives) 83% - 92%

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The CALMA project

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  1. The CALMA project A CAD tool in breast radiography A.Ceccopieri, Padova 9-2-2000

  2. Computer Assisted Library in MAmmography Screening mammography sensitivity (identified positives / true positives) 73% - 88% specificity (identified negatives / true negatives) 83% - 92% These merit figures INCREASE if diagnosis is performed by 2 independent radiologists

  3. CALMA aims to: • Build a DATABASE of mammograms in digital format • Perform an automatic classification of parenchyma structures • Detect the spiculated lesions • Detect micro-calcification clusters

  4. FA 37 % OUR DATABASE DN 5 % 900 patients 2900 images Glandular 58 %

  5. HARDWARE DAQ: granularity: 85 mm range:12 bit dimensions: ~2000x2600 pixels STORAGE 60 images/ CD (no compression) up to 240 CD

  6. DAQ panel & database search Preview and images’ description Queries Full screen display

  7. Automatic classification of breast parenchyma Spatial frequencies analysis (FFT) Left to right / top to bottom: - dense (DN) - irregularly nodular (IN) - micro-nodular (MN) - fiber-adipose (FA) - fiber-glandular (FG) - parvi-nodular (PN) -Glandular (IN+MN+FG+PN) Supervised FF-ANN

  8. 2dim FFT Feature extraction 512x512 pixels analysis ANN classification GLANDULAR

  9. RESULTS: TEXTURE ANALYSIS DENSE ADIPOSE GLANDULAR DENSE >95% 0% 0% ADIPOSE 16% 68±3% 16% GLANDULAR 4% 3% 93±1%

  10. SPICULATED LESIONS Unroll spirals Spatial frequencies analysis(FFT) FF-ANN examples

  11. RESULTS @ sensitivity=90(±3)%: Method Area (cm2)spread(cm2) B(0-0) 31 16 B(1-3) 27 13 B(2-5)25 13 Cneural 36 12 Cnormalized 36 18 Ccorona 49 27

  12. Integration range 2-5

  13. Red= radiologist Spiculated lesions: CAD performances Blue= CAD

  14. RESULTS: SPICULATED LESIONS Sensitivity (per patient) 90±3% FALSE POSITIVES / IMAGE 1.4 AVERAGE ROI 25 cm2 DATA REDUCTION ~ 10

  15. MICROCALCIFICATION CLUSTERS FF-ANN + Sanger learning rule PCA Examples

  16. Method • Image Preprocessing (convolution filters) • PCA through a NN trained with the Sanger rule • Study of the first Principal Components • Classification

  17. Preprocessing • 60x60 pixels windows selection • convolution filters with dims: 5x5 7x79x9 Best results with a 7x7 filter with A=1\N2 aij <0 (aij kernel element)

  18. Results No Micro-calcification clusters With micro-calcification clusters Sensitivity = 73 ±2 % Specificity= 94 ± 2 %

  19. Red= radiologist Micro-calcification clusters: CAD Blue= CAD 3 2 1

  20. RESULTS: MICRO-CALCIFICATION CLUSTERS SENSITIVITY 73±2% SPECIFICITY 94±2%

  21. FUTURE • Software developement: 1- Local classification of parenchyma 2- Use parenchyma classification for lesions CAD 3- Use the asymmetry between the two sides to detect cancer. • Increase the DATABASE • “ON-LINE Validation”: Is CALMA a good (second) radiologist? • Implementation of physician-friendly CAD workstations in the collaborating Hospitals

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