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1. Obtaining research funding and support from national and international funding agencies.
2. Consolidating biomedical research within SIIT and TU by collaborative interactive efforts among researchers with complementary backgrounds, skills, and expertise.
3. Publishing high quality research papers in top international journals.
4. Helping young faculty members to start up and develop academic research and applications.
5. Organizing international research conferences and workshop.
6. Organizing training courses and research seminars.
7. Finding sponsorships to award the best student research works in this area.
8. Providing PhD and postdoc scholarships in biomedical engineering

1. Educational programs in collaboration with other schools such as the school of Dentistry of Thammasat University, or the Department of Radiology. The Master or Ph.D. program aimed at particular aspects of Biomedical Engineering and possibly sponsored by the industry serves this idea.
2. Developing solid self supporting research groups dedicated to particular aspects of Bioengineering or Biomechanics.

1. Diabetic Retinopathy Screening System
2. About 6,000,000 people need to have their eyes scanned for diabetic retinopathy each year. Thailand’s total of 800 ophthalmologists is clearly outnumbered. The project offers an automatic computerized image processing system to help the doctors to screen the patients (National Research University Project) .
3. Diagnostic of Breast Cancer: Continuous Force Field Analysis for Ultrasound Image Segmentation
4. The project offers a new approach to segmentation of ultrasound images of the breast tumors based on the active contour method combined with a new force field analysis techniques The project will make it possible to extend these ideas to a variety of practical examples of the breast or liver tumor segmentations from noisy images and to the cases when the contour is initialized far from the boundary (Thailand Research Fund).
5. Five-Axis Computer Aided Manufacturing of Dental Implants
6. The project aims to create an easy to use and inexpensive system for producing dental crowns using multi axis milling machines. The methods will be adapted to the specific demands of dental machining. The optimization makes it possible to produce the crowns faster and more accurate as compared to conventional rapid prototyping and general purpose CAD/CAM systems or DentMill, CEREC, etc (National Research University Project) .
7. A real-time motion estimation technique using unit gradient vectors (Thammasat University).
8. Automated retinal image analysis for glaucoma detection (Thammasat University).

1. Dr. Annupan Rodtook, Ramkhampang University , Medical Pattern Recognition
2. Ir. Erik Bohez, Asian Institute of Technology, CAD/CAM CNC
3. Dr. Sarah Barman, Kingston University, Medical Image Processing
4. Dr. Chanjira Sinthanayothin, NECTEC, Image Processing
5. Dr. Sirikan Chucherd, Medical Image Processing, Me Fah Luang University

1. Thammasat University, Department of Radiology
2. Thammasat University, Department of Dentistry
3. Kingston University, UK
4. Asian Institute of Technology
5. National Institute of Metrology of Thailand

1. Keatmanee, C., Makhanov, S.S., Kotani, K., Kondo, T., and Thongvigitmanee, S.S.(2015). Inferior alveolar canal segmentation in cone beam computed tomography images using an adaptive diffusion flow active contour model, Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015, Art. No. 7153132, pp. 57-60.
2. Moodleah, S., and Makhanov, S.S. (2015). 5-axis machining using a curvilinear tool path aligned with the direction of the maximum removal rate, International Journal of Advanced Manufacturing Technology, Vol. 80, No. 1-4, pp. 65-90.
3. Muangnak, N., Aimmanee, P., Makhanov, S., and Uyyanonvara, B.(2015). Vessel transform for automatic optic disk detection in retinal images, IET Image Processing, Vol. 9, No. 9, pp. 743-750.
4. Srijuntongsiri, G., Makhanov, S.S. (2015). Optimisation of five-axis machining G-codes in the angular space, International Journal of Production Research, Vol. 53, No. 11, pp. 3207-3227.
5. Vanderperre, E.J., and Makhanov, S.S. (2015). Reliability of Birolini’s duplex system sustained by a cold standby unit and subjected to a priority rule, TOP, Vol. 23, No. 2, pp. 441-465.
6. Clangphukhieo, B., Aimmanee, P., and Uyyanonvara, B. (2014). Segmenting the ventricle from CT brain image using Gray-level Co-occurrence Matrices (GLCMs), Lecture Notes in Engineering and Computer Science, Vol. 1, pp. 585-589.
7. Japunya, T., Jitpakdee, P., Uyyanonvara, B., Aimmanee, P., Philippaki, E., Hull, C., and Barman, S. (2014). Software for the quantification of glistenings in intra-ocular lenses, Lecture Notes in Engineering and Computer Science, Vol. 1, pp. 34-38.
8. Thanathornwong, B., Suebnukarn, S., Songpaisan, Y., and Ouivirach, K. (2014). A system for predicting and preventing work-related musculoskeletal disorders among dentists, Computer Methods in Biomechanics and Biomedical Engineering, Vol. 17 No. 2, pp. 177-185.
9. Turior, R., Uyyanonvara, B., and Chutinantvarodom, P. (2014). Automatic evaluation of plus disease in retinopathy of prematurity, Applied Mechanics and Materials, Vol. 530-531, pp. 189-196.
10. Turior, R., and Uyyanonvara, B. (2014). Comparative analysis of automatic tortuosity classification algorithms, WIT Transactions on Information and Communication Technologies, Vol. 58 VOL I, pp. 309-317.
11. Ekkachai, K., Tungpimolrut, K. and Nilkhamhang, I. (2013). Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network, Smart Materials and Structures, Vol. 22, No. 11, November 2013.
12. Kazi, H., Haddawy, P., and Suebnukarn, S. (2013). Clinical reasoning gains in medical PBL: An UMLS based tutoring system, Journal of Intelligent Information Systems, Vol. 41, No. 2, pp. 269-284.
13. Rodtook, A. and Makhanov, S.S. (2013). Multi-Feature Gradient Vector Flow Snakes f or Adaptive Segmentation of the Ultrasound Images of Breast Cancer, Journal of Visual Communication and Image Representation, Vol. 24, No. 8, November 2013, pp. 1414-1430.
14. Sopharak, A., Uyyanonvara, B., and Barman, S. (2013). Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images, Computerized Medical Imaging and Graphics, Vol. 37, No. 5-6, pp. 394-402.
15. Turior, R., Chutinantvarodom, P., and Uyyanonvara, B. (2013). Automatic tortuosity classification using machine learning approach, Applied Mechanics and Materials, Vol. 241-244, pp. 3143-3147.
16. Turior, R., Onkaew, D., and Uyyanonvara, B. (2013). PCA-based retinal vessel tortuosity quantification, IEICE Transactions on Information and Systems, Vol. E96-D, No. 2, February 2013, pp. 329-339.
17. Turior, R., Chutinantvarodom, P., and Uyyanonvara, B. (2013). Semi-automated computer analysis of retinal vessels in premature infants, Applied Mechanics and Materials, Vol. 241-244, pp. 2962-2968.
18. Fraz, M.M., Barman, S.A., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., Rudnicka, A.R. and Owen, C.G. (2012). An approach to localize the retinal blood vessels using bit planes and centerline detection, Computer Methods and Programs in Biomedicine, Vol. 108, No. 2, November 2012, pp. 600-616.
19. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G. and Barman, S.A. (2012). Blood vessel segmentation methodologies in retinal images - A survey, Computer Methods and Programs in Biomedicine, Vol. 108, No. 1, October 2012, pp. 407-433.
20. Fraz, M.M., Barman, S.A., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., Rudnicka, A.R. and Owen, C.G. (2012). An approach to localize the retinal blood vessels using bit planes and centerline detection, Computer Methods and Programs in Biomedicine, Vol. 108, No. 2 , pp. 600-616.
21. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., and Barman, S.A. (2012). An ensemble classification-based approach applied to retinal blood vessel segmentation, IEEE Transactions on Biomedical Engineering, Vol. 59, No. 9, pp. 2538-2548.
22. Kazi, H., Haddawy, P., and Suebnukarn, S. (2012). Employing UMLS for generating hints in a tutoring system for medical problem-based learning, Journal of Biomedical Informatics, Vol. 45, No. 3, pp. 557-565.
23. Suebnukarn, S., Rhienmora, P., and Haddawy, P. (2012). The use of cone-beam computed tomography and virtual reality simulation for pre-surgical practice in endodontic microsurgery, International Endodontic Journal, Vol. 45 No. 7, pp. 627-632.
24. Anotaipaiboon, W. and Makhanov, S.S. (2011). Minimization of the kinematics error for five-axis machining, Computer-Aided Design, Vol. 43, No. 12, December 2011, pp. 1740-1757.
25. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Owen, C.G., Rudnicka, A.R. and Barman, S. (2011). Retinal Vessel Extraction Using First-Order Derivative of Gaussian and Morphological Processing. In proceeding of: Advances in Visual Computing - 7th International Symposium, ISVC 2011, Vol. 1, September 26-28, 2011, Las Vegas, NV, USA, pp. 410-420.
26. Sopharak, A., Uyyanonvara, B. and Barman, S. (2011). Automatic Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Mathematical Morphology Methods, IAENG International Journal of Computer Science, Vol. 38, No. 3, August 2011, pp. 295-301.
27. Duanggate, C., Uyyanonvara, B., Makhanov, S.S., Barman, S., and Williamson, T. (2011). Object Detection with Feature Stability over Scale Space, Journal of Visual Communication and Image Representation, Vol. 22, No. 4, May 2011, pp. 345-352.
28. Duanggate, C., Uyyanonvara, B., Makhanov, S.S., Barman, S. and Williamson, T. (2011). Parameter-free optic disc detection, Computerized Medical Imaging and Graphics, Vol. 35, No. 1, January 2011, pp. 51-63.
29. Yuenyong, S., Nishihara, A., Kongprawechnon, W. and Tungpimolrut, K. (2011). A framework for automatic heart sound analysis without segmentation, BioMedical Engineering Online, Vol. 10.
30. Duanggate, C., Uyyanonvara, B., Makhanov, S.S., Barman, S. and Williamson, T. (2011). Parameter-free optic disc detection, Computerized Medical Imaging and Graphics, Vol. 35, No. 1, pp. 51-63.
31. Sopharak, A., Uyyanonvara, B. and Barman, S. (2011). Automatic microaneurysm detection from non-dilated diabetic retinopathy retinal images using mathematical morphology methods, IAENG International Journal of Computer Science, Vol. 38, No. 3, pp. 295-301.
32. Duanggate, C., Uyyanonvara, B., Makhanov, S.S., Barman, S. and Williamson, T. (2011). Object detection with feature stability over scale space, Journal of Visual Communication and Image Representation, Vol. 22, No. 4, pp. 345-352.
33. Boonsieng, P., Kondo, T. and Kongprawechnon, W. (2011). Unit gradient vectors based motion estimation techniques, Transactions on Electrical Engineering, Electronics, and Communications, Vol. 9, No. 2, pp. 246-254.
34. Nakaguro, Y., Makhanov, S.S. and Dailey, M.N. (2011). Numerical experiments with cooperating multiple quadratic snakes for road extraction, International Journal of Geographical Information Science, Vol. 25, No. 5, pp. 765-783.
35. Rhienmora, P., Haddawy, P., Suebnukarn, S., and Dailey, M.N. (2011). Intelligent dental training simulator with objective skill assessment and feedback, Artificial Intelligence in Medicine, Vol. 52, No. 2, pp. 115-121.
36. Inglam, S., Suebnukarn, S., Tharanon, W., Apatananon, T., Sitthiseripratip, K. (2010). Influence of graft quality and marginal bone loss on implants placed in maxillary grafted sinus: a finite element study, Medical and Biological Engineering and Computing, Vol. 48, No. 7, pp. 681-689.
37. Rhienmora, P., Haddawy, P., Khanal, P., Suebnukarn, S., and Dailey, M.N. (2010). A virtual reality simulator for teaching and evaluating dental procedures, Methods of Information in Medicine, Vol. 49, No. 4, pp. 396-405.
38. Rodtook, A., and Makhanov, S.S. (2010). Continuous force field analysis for generalized gradient vector flow field, Pattern Recognition, Vol. 43, No. 10, October 2010, pp. 3522-3538.
39. Sopharak, A., Dailey, M.N., Uyyanonvara, B., Barman, S., Williamson, T., Nwe, K.T. and Moe, Y.A. (2010). Machine Learning Approach to Automatic Exudate Detection in Retinal Images from Diabetic Patients, Journal of Modern Optics, Vol. 57, No, 2, January 2010, pp. 124 – 135.
40. Sopharak, A., Dailey, M.N., Uyyanonvara, B., Barman, S., Williamson, T., Nwe, K.T. and Moe, Y.A (2010). Machine learning approach to automatic exudate detection in retinal images from diabetic patients, Journal of Modern Optics, Vol. 57, No. 2 , pp. 124-135.
41. Suebnukarn, S., Haddawy, P., Rhienmora, P., Jittimanee, P., and Viratket, P. (2010). Augmented kinematic feedback from haptic virtual reality for dental skill acquisition, Journal of Dental Education, Vol. 74, No. 12, pp. 1357-1366.
42. Sopharak, A., Uyyanonvara, B., Barman, S. and Williamson, T. (2009). Comparative analysis of automatic exudate detection between machine learning and traditional approaches, IEICE Transactions on Information and Systems, Vol. E92-D, No. 11, pp. 2264-2271.
43. Sopharak, A., Uyyanonvara, B. and Barman, S. (2009). Automatic exudate detection from non-dilated diabetic retinopathy retinal images using Fuzzy C-means clustering, Sensors, Vol. 9, No. 3, pp. 2148-2161.
44. Sukkaew, L., Uyyanonvara, B., Makhanov, S.S., Barman, S. and Pangputhipong, P. (2008). Automatic tortuosity-based retinopathy of prematurity screening, IEICE Transactions on Information and Systems, Vol. E91-D, No. 12, December 2008, pp. 2868-2874.

1. Dr. Annupan Rodtook : Adaptive Noise Removal and Wavelet Moments for 2D Pattern Recognition.
2. Dr. Sirikan Chucherd: Phase Portrait Analysis for Generalized Gradient Vector Flow Applied to Segmentation of Ultrasound Images of Breast cancer
3. Dr. Weerachai Anotaipaiboon: New Algorithms for Tool Path Generation and Optimization for Five-Axis Milling Machines.
4. Dr. Akara Sopharak: Automatic Detection of Diabetic Retinopathy from Digital Retinal Images.
5. Dr. Cattleya Duanggate: Variable Component Detection with the Scale Space Theory

1. Yoichi Nakaguru, Snakes for Segmentation of Complex Objects.
2. Samart Moodleah, 5 Axis Machining to Produce Medical Implants
3. Khwunta Kirimasthong, Ultrasound Image Processing
4. Ms. Rashmi Turior, Automatic Tortuosity measurement
5. Ms. Chadaporn Keatmanee, 3D active contours
6. Ms. Nittaya Muangnak, Retinal Image Processing
7. Ms. Parisut Jitpakdee, Retinal Image Processing
8. Mr. Kittipong Ekkachai, Smart Prosthetic Knee Design with Variable Damping Control using Magneto-rheological Dampers
9. Mr. Pramuk Boonsieng. Optical Flow Estimation in Video Sequences with Application to Medical Imaging
10. Suthum Keerativittayanun, Generation of a High-dynamic Range Images with Application to Medical Image Processing

1. Optimization of the tool path of 5 axis milling machines (National Electronics and Computer Technology Center).
2. New methods for optimization of the tool path of 5 axis milling machines (Thailand Research Fund)
3. Snakes for breast cancer detection (Thailand Research Fund ).
4. Diabetic Retinopathy Screening System (National Electronics and Computer Technology Center ).
5. Feasibility study of angle-closure glaucoma screening device ( National Science and Technology Development Agency).
6. Development of an eye tracking system for ophthalmic diagnosis (National Science and Technology Development Agency).
7. Automatic gesture recognition in real image sequences (Thammasat University)