Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.

Access, attitudes and training in information technologies and evidence-based medicine among medical students at University of Zimbabwe College of Health Sciences.

Background: The Medical Education Partnership Initiative, has helped to mitigate the digital divide in Africa. The aim of the study was to assess the level of access, attitude, and training concerning meaningful use of electronic resources and EBM among medical students at an African medical school. Methods: The study involved medical students at the University of Zimbabwe College of Health Sciences, Harare. The needs assessment tool consisted of a 21-question, paper-based, voluntary and anonymous survey. Results: A total of 61/67 (91%), responded to the survey. 60% of the medical students were ‘third-year medical students’. Among medical students, 85% of responders had access to digital medical resources, but 54% still preferred printed medical textbooks. Although 25% of responders had received training in EBM, but only 7% found it adequate. 98% of the participants did not receive formal training in journal club presentation or analytical reading of medical literature, but 77 % of them showed interest in learning these skills. Conclusion: Lack of training in EBM, journal club presentation and analytical reading skills have limited the impact of upgraded technology in enhancing the level of knowledge. This impact can be boosted by developing a curriculum with skills necessary in using EBM.