Academicians, researchers and industrialists working together on state-of-the-art, research-based solutions in biomedical signal and image processing, data science and embedded systems.
RetinaColour fundus
BioMedical Image and Signal Analysis (BIOMISA) research group was formed in 2013 at Department of Computer Engineering, NUST College of EME, Rawalpindi, Pakistan.
The mission was to pursue research on the development of new algorithms for biometrics and computer aided diagnostic (CAD) systems for medical applications using state of the art image/signal processing, pattern recognition and deep learning techniques.
BIOMISA then joined hands with Emerging Technologies Lab (ETL) which is a platform to nurture multifaceted research and development activities in undergraduate students. In 2016, BIOMISA/ETL was physically established in computer engineering department for undergraduate and postgraduate students.
The Research lab is established with the vision to promote R & D activity, equip students with professional skills and develop research aptitude amongst students through internships, research assistantship etc. In this regards Workshops and Seminars are also conducted on regular basis alongside R & D activity. The lab has received funding from multiple resources for its different research related projects with commercial importance.
Our prime areas of research are:
Analysing signals recorded from the human body: EEG to study the effects of vibration-based therapy on the brain, surface EMG to classify muscle movements, and lung sounds recorded with a digital stethoscope.
Automated analysis of fundus, OCT, chest X-ray and histopathology images, to help detect conditions such as glaucoma, diabetic retinopathy, chronic respiratory diseases and colorectal cancer.
Identifying people from the unique pattern of blood vessels in the retina, captured with a digital fundus camera. Our Retinal Identification Database (RIDB) holds 100 healthy retinal images collected from a local hospital.
Low-cost medical devices: a mobile-mounted system for initial retina screening, the Smart-Steth digital stethoscope, and EKKO, a portable device that uses vibrational waves for cognitive rehabilitation.
Decision support systems that assist doctors, such as early glaucoma detection in the AL-BASR ophthalmology suite and Intelligent Radiologist for holistic insights from chest X-rays.
Deep learning models for medical images, including CNN-STSF, which automatically segments up to eight retinal layers in OCT scans, and models that classify H&E-stained colorectal tissue.
Statistical analysis of research data, such as EEG recordings that compare a therapy group with a control group, and building curated medical datasets for glaucoma, ARMD and hypertensive retinopathy.
Connected healthcare: an i-telemedicine system that links patients in rural areas with doctors in urban locations, and retinal image analysis integrated into the i-Hospital information system.
This project is the enhancement over our previously developed hospital management & information system (HMIS) which also includes the added feature of automatically analyzing fundus imagery for the possible detection of diabetic retinopathy (DR) and glaucoma.
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The purpose of this project was to develop algorithms for early detection of glaucoma.
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Our product “EKKO-A Wave Therapeutic Device” proposes a new solution for cognitive rehabilitation using vibrational waves.
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