Digital processing of diagnostic images

Lecture Notes in Electrical Engineering

Volumn 55 LNEE, Issue , 2010, Pages 186-209

  Capriglione, Domenico ^a   Ferrigno, Luigi ^a   Liguori, Consolatina ^b   Paolillo, Alfredo ^b   Sommella, Paolo ^b   Tortorella, Francesco ^a  

^a UNIVERSITY OF CASSINO AND SOUTHERN LAZIO   (Italy)

^b UNIVERSITY OF SALERNO   (Italy)

Author keywords

Digital image processing; Measurement uncertainty in digital processing; Medical diagnosis

Indexed keywords

BREAST CANCER DIAGNOSIS; DIAGNOSTIC IMAGES; DIGITAL IMAGE PROCESSING; DIGITAL PROCESSING; EPILUMINESCENCE MICROSCOPY; MEASUREMENT SYSTEM; MEASUREMENT UNCERTAINTY; MEDICAL DIAGNOSIS; PARAMETER EVALUATION; SKIN LESION; ULTRASOUND IMAGES; WIDE FIELD;

DIAGNOSIS; DIGITAL IMAGE STORAGE; IMAGE PROCESSING; IMAGING SYSTEMS;

UNCERTAINTY ANALYSIS;

EID: 78651545322     PISSN: 18761100     EISSN: 18761119     Source Type: Book Series    
DOI: 10.1007/978-3-642-05167-8_12     Document Type: Conference Paper

References (56)

1. Bond, M.G., Wilmoth, S.K., et al.: Detection and Monitoring of Asymptomatic Atherosclerosis in Clinical Trials. The Americal Journal of Medicine 86(suppl. 4A), 33-36 (1989)
2. Touboboul, P.J., Prati, P., et al.: Use of monitoring software to improve the measurement of carotid wall thickness by B-mode imaging. Journal of Hypertension 10(suppl. 5), S37-S41 (1992)
3. El-Barghouty, N.M., Levine, T., Ladva, S., Flanagan, A., Nicoladeis, A.: Histological Verification of Computerized Carotid Plaque Characterisation. European Journal Vascular Endovascular Surgey 11, 414-416 (1996)
4. Gill, J.D., Ladak, H.M., Steinman, D.A., Fenster, A.: Segmentation of ulcerated plaque: a semi-automatic method for tracking the progression of carotid atherosclerosis. In: Proc. of EMBS Conference, pp. 669-672 (2000)
5. Noritomi, T., et al.: Carotid Plaque Typing by Multiple-Parameter Ultrasonic Tissue Characterization. Ultrasound in Medicine & Biology 23(5), 643-650 (1997)
6. Fenster, A., Downey, N.M.: 3-D ultrasound imaging: A review. IEEE Engineering in Medicine and Biology 15(11), 41-51 (1996)
7. Hossack, J.A., Sumanaweera, T.S., Napel, S.: Quantitative 3D ultrasound imaging using an automated image tracking technique. In: Proc. of IEEE Ultrasonic symposium, pp. 1593-1596 (2000)
8. Rosenfield, K., et al.: Three-dimensional reconstruction of human carotid arteries from images obtained during non-invasive B-mode ultrasound examination. The American Journal of Cardiology 70(8), 379-384 (1992)
9. Wilhjelm, J.E., et al.: Estimation of plaque contents with multi-angle 3D compound imaging. In: Proc. of IEEE Ultrasonic Symposium, pp. 1077-1080 (1996)
10. Liguori, C., Paolillo, A., Pietrosanto, A.: An automatic measurement system for the evaluation of carotid intima-media thickness. IEEE Trans. on I&M 50(6), 1684-1691 (2001)
11. Betta, G., Liguori, C., Pietrosanto, A.: A structured approach to estimate the measurement uncertainty in digital signal processing algorithms. IEE Proc. Inst. Elect. Eng. Sci. Meas. Technol. 146(1), 21-26 (1999)
12. UNI CEI ENV 13005 (2000)Guide to the expression of uncertainty measurement
13. Capriglione, D., Ferrigno, L., Liguori, C., Paolillo, A.: Volumetric Carotid Plaque Measurements Based on Ultrasound Images: A Preliminary Approach. In: Proceedings of the 12th IMEKO TC-4 Part 1, pp. 255-260 (2002)
14. Ferrigno, L., Paciello, V., Paolillo, A.: A low cost measurement system for the 3-D evaluation of carotid plaque based on ultrasound images. In: Proceeding of 13th International Symposium on Measurements for Research and Industry Applications, IMEKO TC 4, pp. 468-474 (2004)
15. Ganster, H., et al.: Automated melanoma recognition. IEEE Transaction on Medical Imaging 20, 233-239 (2001)
16. Schmid-Saugeon, P., Guillod, J., Thiran, J.P.: Towards a computer-aided diagnosis system for pigmented skin lesions. Computerized Medical Imaging and Graphics 27, 65-78 (2003)
17. Hoffmann, K., Gambichler, T., et al.: Diagnostic and neural analysis of skin cancer (DANAOS). A multicentre study for collection and computer-aided analysis of data from pigmented skin lesions using digital dermoscopy. Br. J. Dermatology 149, 801-809 (2003)
18. Binder, M., Schwartz, M., et al.: Epiluminescence microscopy: a useful tool for the diagnosis of pigmented skin lesion for formally trained dermatologists. Archives of Dermatology 131, 286-291 (1995)
19. Fabbrocini, G., Argenziano, G., et al.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Archives of Dermatology 134, 1563-1570 (1998)
20. Di Leo, G., Fabbrocini, G., Liguori, C., Pietrosanto, A., Scalvenzi, M.: ELM image processing for melanocytic skin lesion based on 7-point checklist: a preliminary discussion. In: Proc. of the 13th IMEKO TC-4 Symposium, pp. 474-479 (2004)
21. Betta, G., Di Leo, G., Fabbrocini, G., Paolillo, A., Scalvenzi, M.: Automated Application of the 7-point checklist Diagnosis Method for Skin Lesions: Estimation of Chromatic and Shape Parameters. In: Proceeding of Instrumentation and Measurement Technology Conference, IMTC, pp. 1818-1822 (2005)
22. Di Leo, G., Liguori, C., Paolillo, A., Sommella, P.: An improved procedure for the automatic detection of dermoscopic structures in digital ELM images of skin lesions. In: IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems, VECIMS, pp. 190-195 (2008)
23. Otsu, N.: A threshold selection method from gray-level histogram. IEEE Transactions on System Man Cybernetics 9(1), 62-66 (1979)
24. Schmid-Saugeon, P., Guillod, J., Thiran, J.P.: Towards a computer-aided diagnosis system for pigmented skin lesions. Computerized Medical Imaging and Graphics 27, 65-78 (2003)
25. Stolz, W., Braun-Falco, O., et al.: Color Atlas of Dermatoscopy. Blackwell Science, Malden (1994)
26. Argenziano, G., Soyer, H.P., et al.: Interactive Atlas of Dermoscopy. EDRA Medical Publishing & New Media, Milan (2002)
27. Di Leo, G., Fabbrocini, G., Paolillo, A., Rescigno, O., Sommella, P.: Toward an automatic diagnosis system for skin lesions: estimation of blue-whitish veil and regression structures. In: International Multi-Conference on Systems, Signals & Devices, SSD 2009 (2009)
28. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, New Jersey
29. Koonty, W., Narenda, P.M., Fukunya, F.: A graph theoretic approach to non-parametric cluster analysis. IEEE Transactions on Computer 25, 936-944 (1976)
30. Witten, H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)
31. Landwehr, N., Hall, M., Frank, E.: Logistic Model Trees. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 241-252. Springer, Heidelberg (2003)
32. Levy, A., Lindenbaum, M.: Sequential Karhuen-Loeve basis extraction and its Application to imageş. IEEE Trans. on Image Processing 9(8), 1371-1374 (2000)
33. National Black Leadership Initiative on Cancer III: Community Networks Program, Breast Cancer Fact Sheet (Revised 10/21), http://www.nblic.org
34. Chan, H.P., Sahiner, B., et al.: Improvement of Radiologists' Characterization of Mammographic Masses by using Computer aided Diagnosis: An ROC study. Radiology (212), 817-827 (1999)
35. Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcification in mammograms: a survey. Pattern Recognition 36, 2967-2991 (2003)
36. Nishikawa, R.: Current status and future directions of computer-aided diagnosis in mammography. Computerized Medical Imaging and Graphics 31, 1357-1376 (2007)
37. Wu, Y., Giger, M.L., et al.: Artificial neural networks in mammography: Application to decision making in the diagnosis of breast cancer. Radiology 187, 81-87 (1993)
38. El-Naqa, Yang, Y., Wernick, M.N., Galatsanos, N.P., Nishikawa, R.M.: A support vector machine approach for detection of microcalcifications. IEEE Transactions on Medical Imaging 21(12), 1552-1563 (2002)
39. Wei, L., Yang, Y., Nishikawa, R.E., Wernick, R.E., Edwards, A.: Relevance vector machine for automatic detection of clustered microcalcifications. IEEE Transactions on Medical Imaging 24(10), 1278-1285 (2005)
40. Jiang, J., Yao, B., Wason, A.M.: A genetic algorithm design for microcalcification detection and classification in digital mammograms. Computerized Medical Imaging and Graphics 31, 49-61 (2007)
41. Nakayama, R., Uchiyama, Y., Yamamoto, K., Watanabe, R., Namba, K.: Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms. IEEE Transactions on Biomedical Engineering 53(2), 273-283 (2006)
42. Strickland, R.E., Hahn, H.I.: Wavelet transform for detecting microcalcifications in mammograms. IEEE Transactions on Medical Imaging 15(2), 218-229 (1996)
43. Netsch, T., Peitgen, H.: Scale-space signatures for the detection of clustered microcalcifications in digital mammograms. IEEE Transaction on Medical Imaging 18(9), 774-786 (1999)
44. Gurcan, M.N., Yardimci, Y., Cetin, A.E., Ansari, R.R.: Detection of microcalcifications in mammograms using higher order statistics. IEEE Signal Processing Letters 4(8), 213-216 (1997)
45. Li, S.Z.: Markov random field modeling in image analysis. Kluwer Academic, Dordrecht (2001)
46. Medina, R., Garreau, M., et al.: Markov random field modeling for three-dimensional reconstruction of the left ventricle in cardiac angiography. IEEE Transactions on Medical Imaging 25(8), 1087-1100 (2006)
47. Suliga, M., Deklerck, R., Nyssen, E.: Markov random field-based clustering applied to the segmentation of masses in digital mammograms. Computerized Medical Imaging and Graphics 32, 502-512 (2008)
48. Yu, S.N., Li, K.Y., Huang, Y.K.: Detection of microcalcifications in digital mammograms using wavelet filter and markov random field model. Computerized Medical Imaging and Graphics 30, 163-173 (2006)
49. Tsujii, O., Freedman, M.T., Mun, S.K.: Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network. Pattern Recognition 32, 891-903 (1999)
50. Cheng, H.D., Wang, J., Shi, X.: Microcalcification detection using fuzzy logic and scale space approach. Pattern Recognition 37, 363-375 (2004)
51. Papadopoulos, A., Fotiadis, D.I., Likas, A.: Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artificial Intelligence in Medicine 34, 141-150 (2005)
52. De Santo, M., Molinara, M., Tortorella, F., Vento, M.: Automatic classification of clustered microcalcifications by a multiple expert system. Pattern Recognition (3), 1467-1477 (2003)
53. D'Elia, C., Marrocco, C., Molinara, M., Tortorella, F.: Detection of Clusters of Microcalcifications in Mammograms: A Multi Classifier Approach. In: Proc. 21st IEEE International Symposium on Computer-based Medical Systems, pp. 572-577. IEEE Computer Society Press, Los Alamitos (2008)
54. D'Elia, C., Poggi, G., Scarpa, G.: A tree-structured Markov random field model for bayesian image segmentation. IEEE Transactions on Image Processing 12(10), 1259-1273 (2003)
55. Marrocco, C., Molinara, M., Tortorella, F.: Algorithms for detecting clusters of microcalcifications in mammograms. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 884-891. Springer, Heidelberg (2005)
56. Peng, Y., Yao, B., Jiang, J.: Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis. Artificial Intelligence in Medicine 37, 43-53 (2006)