List of Tables

1. Single image reviewed works.
2. Quantitatively compared mass segmentation approaches.
3. Test set of mammograms.
4. Computational time comparison.
5. Lesion shape influence.
6. Lesion size influence.
7. Breast tissue influence.
8. Number of clusters influence.
9. Breast density literature review.
10. Kappa scale.
11. Radiologists confusion matrices.
12. Agreement of automatic and radiologists opinion.
13. Agreement of automatic and consensus classification.
14. Confusion matrices for the DDSM database.
15. Comparison of breast density strategies.
16. Classification rates using a breast tissue segmentation strategy.
17. Comparison with reviewed works.
18. Results using the MIAS database.
19. Lesion size influence for the MLO Málaga database.
20. Lesion size influence for the CC Málaga database.
21. Mammograms training view comparison.
22. False positive review.
23. False positive results for the MIAS database.
24. False positive results for the DDSM database
25. Lesion size influence
26. Training analysis.
27. Lesion size analysis.
28. Lesion breast tissue analysis.
29. Lesion shape analysis.
30. False positive results taking the breast density into account.
31. Results for each BIRADS category.
32. False positive reduction proposals.
33. Confusion matrix for breast density estimation.
34. Influence of the tissue on the algorithms performance.
35. Table summary.
36. MIAS mammograms.
37. MIAS mammograms containing masses.
38. DDSM digitization features.
39. DDSM mammograms with masses.
40. Trueta mammograms with masses.
41. A confusion matrix.
42. Kappa scale.
43. A confusion matrix with only two cases.
44. Description of the notation used in CAD diagnosis.