From a small beginning in 2002 with 2 faculty members and 8 students the department has progressed considerably over the years. It now has 9 full-time faculty members and several associated faculty and experts from the other departments of IIT and also from other research and medical institutes.

It has now around 50 research scholars (seventy percent of them are funded from research funds) and 30 post graduate students working in 9 well equipped laboratories. The research output in terms of sponsored projects and publications in international journals have increased almost exponentially and are now comparable to any good department in the country.
Given the opportunity to unleash our potential and utilized the best of the facilities available, the following projects are carried out:

• Tissue level and cellular level characterization of epithelium for early detection of Oral Cancer.
Development of image processing algorithms on FPGA and Texas Instruments TM DSP processor.
• Automated classification of cells in Sub-Epithelial connective tissue of oral sub-mucous fibrosis.
• MEMS based blood flow sensor development.
• Development of image processing algorithms to determine the different geometrical measurements for Tibia-nail design.
• Extraction of texture features and their analysis from SEM images of biomaterial based solution spun fiber.
• Diagnose liver disease from liver function test with the help of supervised and unsupervised pattern classification techniques.
• Segmentation of retinal blood vessels using vesselness measure parameters from retinal images.
• An image processing strategy for the detection of microaneurysms from fluorescent angiograms of the ocular fundus.
• Introduction to MSP 430 microcontroller programming for medical applications.
• Having knowledge of designing analog front end circuitry for different filtering and data acquisition applications in medical instrumentation scheme.
• Application of various embedded systems in medical instrumentations.
• A comparison of usability methods for testing interactive health technologies, methodological aspects and empirical evidence.
• Study on mammographic mass data using different pattern classification techniques.
• Segmentation of malaria parasite on peripheral smear images by using specific mask.
• Segmentation of exudates from diabetic retinopathy retinal images.
• Application of supervised and unsupervised pattern classification techniques on diabetic patient's dataset.