PRPI 2010

Congratulation to Hasan and his team for winning gold medal in PRPI 2010.

Next step will enhance the 3D, fitting it with polynomial fitting, deploying the directive partial derivative and identify the sudden changes. At the same time, enhance the voice activation platform and database design to suit the main system.

At IT-Healthcare Exhibition

Can be seen here, Mrs Puteri Suhaiza, Mrs Rabiah Abdul Kadir, Mrs Lilly Suriani and Mrs Rahmita Wirza infront of Clinical Research Centre booth. We were interested with the One Stop Centre and would like to know what information that this centre can provide. Most probably our next visit will be this centre.

This is a poster in the CRC booth. Listed were academics' publication in highly impact factor journal.

CRC poster.

We were interested with OT management system even though not within our project's scope.

An Improved Seed Point Detection Algorithm for Centerline Tracing in Coronary Angiograms

We presents a new method to detect initial seed points for automatic tracing of the vessel center lines in coronary angiograms. Vessel tracing algorithms are known to be fast and efficient among several feature extraction methods. However, most of them suffer from incomplete results due to inappropriate trade-off between the completeness of seed point detection and computational efficiency. Imposing strict validation rules decreases the number of background traces, but results in more false negatives and more computation time. We show that using the geometrical properties of gradient vectors calculated at vessel boundary points as a validation criterion, improves the performance of the seed point detection algorithm. The results illustrate that the proposed method improves upon the prior method in both performance and computation time.continue-->

Quantitative Detection of Left Ventricular Wall Motion Abnormality by Two-Dimensional Echocardiography

Echocardiogram has become an important diagnostic tool in modern cardiology. Although this imaging modality is the most widely used to evaluate regional left ventricular function, it allows non-invasive real-time visualization of left ventricular motion. Evaluation of the left ventricular function based on subjective experience-dependent visual interpretation of dynamic images of echocardiograph. Unfortunately echocardiograph is notoriously difficult to interpret even for the best physicians where they could misdiagnose the heart disease. Hence there is a tremendous need for an automated technique that can provide objective diagnostic assistance, particularly to the less-experienced cardiologist.
In this study, the team address the task of building a computer aided diagnosis system that can detect LV motion abnormalities from echocardiograph. The team work based on the anatomical structure of the left ventricular wall in short axis echocardiograph and the team will use the wall thickening as a parameter to evaluate a profile for the regional myocardial function in normal conditions.  Continue -->

Trip to Singapore (4th June 2010)

A New Tracing Algorithm for Automatic Boundary Extraction from Coronary Cineangiograms

The need for reliable identification of vessel contours from X-ray image sequences within a limited computation time is still a challenge in medical image analysis. In the literature, only a few vessel boundary extraction methods are suitable to meet automatic and real-time constraints for capturing and processing coronary artery cineangiograms. Among many approaches, vessel tracing algorithms are known to be fast and robust for  practically detecting the vessel structures from live two- dimensional angiogram sequences. However, they often do not directly extract the boundary points and instead the locations of boundary points are achieved after identifying the correct position of center line points. This boundary detection scheme seems to be less efficient in speed demanding clinical applications. The team introduced a new algorithm for automatic tracing of vessel boundaries using an efficient estimation of local gradient vector. The results illustrate that the team method is a promising method for real-time vessel segmentation and linear feature extraction. Continue -->