Telehealth and Computer-aided Diagnosis

1. Telehealth and Computer-aided Diagnosis By Juan Shan April 2013

2. Telehealth • Telehealth is the delivery of health-related services and information via telecommunications technologies. • Telehealth could be as simple as two health professionals discussing a case over the telephone or as complicated as doing robotic surgery between facilities at different ends of the globe.

3. Telehealth and Computer-aided Diagnosis • Telehealth is an interdisciplinethat involes medical image processing, networking, cybersecurity, machine learning, database management, and data mining. • Computer-aided diagnosis (CAD) systems use computer techniques to assist doctors to analyze data and make diagnosis. • Computer-aided diagnosis (CAD) systems could be able to serve independently as one terminal in the telehealth network in the future.

4. CAD for Breast Cancer • Breast cancer is the #1 leading cause of cancer death for women at ages 20 to 59 [1,2]. • 226,870 newly diagnosed cases and 39,510 deaths in the United States in 2012 [2]. • The earlier the cancers are detected, the better the patients are cured [1]. Fig. 1. Incidence rate of cancers for females

5. Mammography • Previously, the most effective modality for detecting breast cancer is mammography. • Limitations of mammography: • The radiation might be harmful for the patients and radiologists. • High false positive rate: 65%–85%. • Hardly detect breast cancer for dense breasts. Fig. 2. Mammography images

6. Ultrasound Imaging • Breast ultrasound (BUS) imaging is superior to the mammography: • Having no radiation, safer than mammography for patients and radiologists in daily clinical practice [14]. • Cost effective and portable. • More sensitive than mammography for dense breasts ( i.e., suitable for women younger than 35 years old[12]).

7. Computer-aided Diagnosis (CAD) Advantages of a CAD system: • Calculate computational features and statistical features, which cannot be obtained visually or intuitively by humans (doctors). • Minimize the operator-dependent nature of ultrasound imaging [4] and make the diagnosis process reproducible.

8. Computer-aided Diagnosis (CAD) • Preprocessing: enhance the image contrast and reduce speckle noise. • Segmentation: separate the lesion from the surrounding tissues. • Feature extraction: extract critical features to distinguish benign and malignant lesions. • Classification: use machine learning techniques to classify the lesion into benign or malignant types.

9. Computer-aided Diagnosis (CAD) • Segmentation is the most important step. • Since many crucial features to distinguish benign and malignant lesions are based on the shape and boundary of the lesion.

10. Example of benign and malignant lesions • Benign • Malignant

11. Lesion segmentation is important and… difficult as well! • Due to the nature of ultrasound imaging, the breast ultrasound (BUS) images are degraded by speckle noise, low contrast, blurred edges and shadow effect. • Automatic segmentation is a challenging task.

12. An automatic segmentation method J. Shan, H. D. Cheng and Y. X. Wang, “Effective and Automatic Breast Ultrasound Image Segmentation Using L-Means Clustering”, Medical Physics. Vol. 39, Issue 9, pp. 5669-5682, 2012 Sep

13. The proposed method • ROI generation • Speckle reduction • Contrast enhancement • Clustering

14. ROI Generation Binarize the image into background and foreground, by an adaptively selected threshold • (a) Original image • (b) Binarized Image

15. ROI Generation Delete the boundary-connected regions and noise regions. • (c) Image after binarization • (d) Image after region deletion

16. ROI Generation Rank the regions. The one with the highest score is considered as the lesion region. • (e) Image of the winning region

17. ROI generation result • (a) Original BUS image (b) Binary image (c) Winning region • (d) ROI generation

18. The proposed method cont. • ROI generation • Speckle reduction [5] • Contrast enhancement • Neutrosophic l-means

19. Speckle reduction An effective and fast algorithm is used: speckle reducing anisotropic diffusion (SRAD) [5]

20. The proposed method cont. • ROI generation • Speckle reduction [5] • Contrast enhancement • Neutrosophic l-means

21. Fourier Transform • Spatial domain  Frequency domain • 1-dimentional

22. From 1-D signal to 2-D image • 2-D Log-Gabor filters defined in polar coordinates: where k is related to the bandwidth of the filter and w0 is the center frequency of the filter. q0is the orientation. sq defines the spread of the Gaussian orientation function. • 6 orientations (0°, 30°, 60°, 90°, 120°, 150°) are chosen to cover the whole spectrum. • In each orientation, local phase feature LPA is calculated.

23. PMO Image • Which orientation should be used? • The LPA in the direction of the edge can better characterize the structure than the LPAs in other directions. • Phasein max-energy orientation = PMO

24. How to process in frequency domainto get PMO image

25. PMO image example (a) ROI (b) De-speckled image (c) PMO image (d) Enhanced PMO image (a) (b) (c) (d)

26. The proposed method • ROI generation • Speckle reduction [5] • Contrast enhancement • Clustering

27. Clustering • Clustering is the partitioning of a data set into subsets so that the data in each cluster have some common attributes. • Basic clustering: • http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/AppletKM.html

28. The proposed method • ROI generation • Speckle reduction [5] • Contrast enhancement • Clustering

29. Evaluation

30. Breast ultrasound database • The database is composed of 120 BUS images. 58 cases are benign, 62 cases are malignant. • Every lesion is manually outlined by an experienced radiologist. The manual delineations are served as the standard to evaluate the segmentation method.

31. Comparison is necessary • Compare the new method with: • A segmentation method using active contour model [21] • A segmentation method using level-set model [28] • A segmentation method using watershed model [70]

32. Result of segmentation methods (a) The original image. (b) Manual delineation by radiologist. (c) Output of the method in [21]. (d) Output of the method in [28]. (e) Output of the method in [70]. (f) Output of the proposed method.

33. Area error metrics

34. Accuracy comparison

35. Future direction • Continue the research on breast cancer ultrasound, to find more reliable segmentation methods, to extract the features of tumors, and to train classifiers that can automatically classify tumors into benign/malignant. • Dental X-rays dataset, to detect tooth root and dental diseases. • Other type of medical images and explore the possible application of computer-aided diagnosis.

36. Thanks! Questions?

37. References 1. Cheng, H.D., Shan, J., Ju, W., Guo, Y., and Zhang, L. Automated breast cancer detection and classification using ultrasound images: A survey.Pattern Recognition43, 1 (2010), 299-317. 2.Jemal, A., Siegel, R., Xu, J., and Ward, E. Cancer statistics 2010.CA Cancer J. for Clininicians60, (2010), 227-300. 3.Sahiner, B., Chan, H.-P., Roubidoux, M.A., Hadjiiski, L.M., Helvie, M.A., Paramagul, C., Bailey, J., Nees, A.V., and Blane, C. Malignant and benign breast masses on 3D US volumetric images: Effect of computer-aided diagnosis on radiologist accuracy.Radiology 242, 3 (2007), 716-724. 4. Hwang, K.-H., H., Lee, J.G., Kim, J.H., Lee, H.-J. Om, K.-S., Yoon, M., and Choe, W. Computer aided diagnosis (CAD) of breast mass on ultrasonography and scintimammography. In Proceedings of 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, 2005, 187-189. 5. Yu, Y. and Acton, S.T. Speckle reducing anisotropic diffusion.IEEE Trans. on Image Processing 11, 11 (2002), 1260-1270. 6.Shankar, P.M., Piccoli, C.W., Reid, J.M., Forsberg, F., and Goldberg, B.B. Application of the compound probability density function for characterization of breast masses in ultrasound B scans.Physics in Medicine & Biology50, 10 (2005), 2241-2248. 7.Taylor, K.J.W., Merritt, C., Piccoli, C., Schmidt, R., Rouse, G., Fornage, B., Rubin, E., Georgian-Smith, D., Winsberg, F., Goldberg, B., and Mendelson, E. Ultrasound as a complement to mammography and breast examination to characterize breast masses.Ultrasound in Medicine & Biology28, 1 (2002), 19-26. 8.Zhi, H., Ou, B., Luo, B.-M., Feng, X., Wen, Y.-L., and Yang, H.-Y. Comparison of ultrasound elastography, mammography, and sonography in the diagnosis of solid breast lesions.J. Ultrasound in Medicine26, 6 (2007), 807-815. 9.Chang, R.-F., Wu, W.-J., Moon, W.K., and Chen, D.-R. Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis.Ultrasound in Medicine & Biology29, 5 (2003), 679-686. 21. Madabhushi, A. and Metaxas, D.N. Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans. on Medical Imaging 22, 2 (2003), 155-169. 28. Liu, B., Cheng, H.D., Huang, J., Tian, J., Liu, J., and Tang, X., Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance. Ultrasound in Medicine & Biology 35, 8 (2009), 1309-1324. 70. Zhang, M. Novel Approaches to Image Segmentation Based on Neutrosophic Logic. Doctoral Dissertation, Utah State University, 2010.