The invention of the microscope enabled doctors and researchers to make observations at the cellular level. The advent of the X-ray, and later of magnetic resonance and other imaging technologies, enabled visualization of tissues and organs never before possible. Each of these technological advances necessitates a companion advanced in the methods and tools used to analyze and interpret the results.
Instead of biomedical imaging, with the increasingly frequent use of technologies like DNA and RNA sequencing, DNA microarrays, and high-throughput proteomics and metabolomics, comes the need for novel methods to turn these new types of data into new information and that new information into new knowledge. By using bioinformatics, it is possible to capture the effects of common variations in people's genomes of phenotype. Hence it can be analyzed how people get sick (including hereditary disease, cancer, etc.), how people age, how people metabolize pharmaceutical drugs. This information will be used for diagnosis and will be necessary for tailoring treatments to individuals. There will be an insight into how genetic variations impact the effect of different treatments.
Bioinformatics research will also have immense value in fields other than human genetics, such as agriculture. For example, how do variations in a plant genome allow it to respond to the environment, and what additional nutrients are needed to provide to optimize crop production. Similar computational biology tools can be applied in these different research fields.
Those above, the biomedical field is changing from a hypothesis-driven science to data-driven science. The combination of medical imaging to analyzing organ and tissue to the molecular level and Bioinformatics for sequencing and other high-throughput experimental techniques is potentially exponential growth used for future molecular and clinical diagnosis and treatment.
Currently, Indonesia lacks both data availability and research in terms of Computational biomedical imaging and Bioinformatic. Hence the establishment of the research center based on its Indonesia data profile becomes mandatary required to support the advanced genomic-based diagnosis.
The Center for Computational Biomedical Imaging and Bioinformatics is designed as an interdisciplinary center within the Krida Wacana Christian University whose goal is to catalyze research at the interface of the medical biology, computational and information sciences. The center is supporting active research programs in a diverse range of disciplines including but not limited.
The establishment of the Center of Computational Biomedical Imaging and Bioinformatics in Ukrida promises to be the pioneer to provide the biomedical research and data center for translational Computational Biomedical Imaging and bioinformatics research in Indonesia.
- Providing data center for biomedical imaging and bioinformatics related matters
- Working together with hospital and Biomedical industry to provide reliable big data analysis for clinical diagnosis
- An interdisciplinary community of investigators who share a common interest in studying fundamental biological problems from a quantitative, computational perspective.
- A mentorship for students who are pursuing education in biomedical imaging and bioinformatics fields.
Leader: Lina Septiana
Florentia Rosani Purba
Kris Herawan Timotius
- Septiana L., Suzuki H., Ishikawa M., Obi T., Kobayashi N., Ohyama N., Ichimura T., Sasaki A., Wihardjo E., Andiani D., Elastic and Collagen Fibers Discriminant Analysis using H&E Stained Hyperspectral Images, Journal of Optical Review, Springer, August 2019, Volume 26, Issue 4, pp 369–379.
- Septiana L., Suzuki H., Ishikawa M., Obi T., Kobayashi N., Ohyama N., Ichimura T., Sasaki A., Wihardjo E., Arjadi H., Elastic and Collagen Fibers Segmentation based on U-net Deep Learning using Hematoxylin and Eosin Stained Hyperspectral Images, The Institute of Image Electronics Engineers of Japan (IIEEJ) (Accepted).
International Conference (Peer Review)
- Septiana L., Suzuki H., Ishikawa M., Obi T., Kobayashi N., Ohyama N., Ichimura T., Sasaki A., Wihardjo E., Arjadi H. , Elastic and Collagen Fibers Segmentation based on U-net Deep Learning using Hematoxylin and Eosin Stained Hyperspectral Images, Image Electronic and Computer Visualization (IEVC 2019), Bali, August 21-24, (2019) .
- Septiana L., Suzuki H., Ishikawa M., Obi T., Kobayashi N., Ohyama N., Ichimura T., Sasaki A., Wihardjo E., Andiani D. , "Classification of Elastic and Collagen Fibers in H&E Stained Hyperspectral Images", 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC 2019), Berlin, Germany, July. 23-27, (2019)
- Septiana L., Suzuki H., Ishikawa M., Obi T., Kobayashi N., Ohyama N., Staining Adjustment of Dye Amount to Clarify the Appearance of Fiber, Nuclei, and Cytoplasm in HE-stained Pathological Kidney Tissue Image", International Multidisciplinary Conference and Productivity and Sustainability (IMPS), Jakarta, Indonesia, December 5-7(2017).
- Septiana L., Haryanto F., Lin K.P., Comparison of Independent Component Analysis (ICA) Algorithm for Heart Rate Measurement Based on Facial Imaging, WorldCongresBiomedical, IFMBE Volume 51, Toronto Canada. 2015
- Septiana L., Lin W.C., Huang S.C., Lin K.P., X-ray image enhancement using a modified anisotropic diffusion, Bioelectronics, and Bioinformatics, IEEE, Taiwan, 2014.
- Septiana L., Lin W.C., Huang S.C., Lin K.P., A quantification method for radial artery pulsation device, Bioelectronics, and Bioinformatics, IEEE, Taiwan, 2014
- Septiana L., Lin W.C., Huang S.C., Lin K.P., Mammogram Enhancement using Anisotropic Diffusion and Weighted K-means Clustering, Transaction of Japanese Society for Medical and Biological Engineering 2013.
Center for Computational Biomedical Imaging and Bioinformatics
Universitas Kristen Krida Wacana