Aortic valve disease occurs due to calcification deposits on the area of leaflets within the human heart. It is progressive over time where it can affect the mechanism of the heart valve. To avoid the risk of surgery for vulnerable patients especially senior citizens, a new method has been introduced: Transcatheter Aortic Valve Implantation (TAVI), which places a synthetic catheter within the patient's valve. This entails a procedure of aortic annulus sizing, which requires manual measurement of the scanned images acquired from Computed Tomographic (CT) by experts. The step requires intensive efforts, though human error may still eventually lead to false measurement. In this research, image processing techniques are implemented onto cardiac CT images to achieve an automated and accurate measurement of the heart annulus. The image is first put through pre-processing for noise filtration and image enhancement. Then, a marker image is computed using the combination of opening and closing operations where the foreground image is marked as a feature while the background image is set to zero. Marker image is used to control the watershed transformation and also to prevent oversegmentation. This transformation has the advantage of fast computational and oversegmentation problems, which usually appear with the watershed transform can be solved with the introduction of marker image. Finally, the measurement of aortic annulus from the image data is obtained through morphological operations. Results affirm the approach's ability to achieve accurate annulus measurements compared to conventional techniques.

Aim To evaluate and compare the feedback of final year undergraduate dental students in eight Malaysian dental schools on the application of a new system for classifying root canal morphology in teaching and clinical practice. Methods One PowerPoint presentation describing two classification systems for root canal morphology (Oral Surgery Oral Medicine Oral Pathology, 1974 38, 456 and its supplemental configurations, International Endodontic Journal 2017, 50, 761) was delivered to final year undergraduate dental students in eight dental schools in Malaysia by two presenters (each presented to four schools). To examine students' feedback on the utility of each system, printed questionnaires consisting of six questions (five multiple choice questions and one open-ended question) were distributed and collected after the lecture. The questionnaire was designed to compare the classification systems in terms of accuracy, practicability, understanding of root canal morphology and recommendation for use in pre-clinical and clinical courses. The exact test was used for statistical analysis, with the level of significance set at 0.05 (P = 0.05). Results A total of 382 (out of 447) students participated giving a response rate of 86%. More than 90% of students reported that the new system was more accurate and more practical compared with the Vertucci system (P < 0.001). Overall, 97% of students reported the new system helped their understanding of root and canal morphology compared with the Vertucci classification (P < 0.001). Over 97% of students recommended the use of the new system in teaching, pre-clinical courses and clinical practice (P < 0.001). Except for two schools, no significant difference was detected between the responses of students for all questions at the different schools (P > 0.05). The students' responses for all questions were almost similar for both presenters (P > 0.05). Conclusions The new system of International Endodontic Journal 2017, 50, 761 for classifying root and canal morphology was favoured by final year undergraduate dental students in Malaysia. The new system has the potential to be included in the undergraduate endodontic curriculum for teaching courses related to root and canal morphology.

Aortic stenosis (AS) is a condition where the calcification deposit within the heart leaflets narrows the valve and restricts the blood from flowing through it. This disease is progressive over time where it may affect the mechanism of the heart valve. To alleviate this condition without resorting to surgery, which runs the risk of mortality, a new method of treatment has been introduced: Transcatheter Aortic Valve Implantation (TAVI), in which imagery acquired from real-time echocardiogram (Echo) are needed to determine the exact size of aortic annulus. However, Echo data often suffers from speckle noise and low pixel resolution, which may result in incorrect sizing of the annulus. Our study therefore aims to perform an automated detection and measurement of aortic annulus size from Echo imagery. Two stages of algorithm are presented - image denoising and object detection. For the removal of speckle noise, Wavelet thresholding technique is applied. It consists of three sequential steps; applying linear discrete wavelet transform, thresholding wavelet coefficients and performing linear inverse wavelet transform. For the next stage of analysis, several morphological operations are used to perform object detection as well as valve sizing. The results showed that the automated system is able to produce more accurate sizing based on ground truth.