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۲۔ اعتراف

اعتراف

میں دو دیوتائوں کا بھگت

جنھوں نے میرا بخت چمکایا

پہلا دیوتا جس کا مندر

جیسا باہر ویسا اندر

جہاں محبت کی شمعیں جلتی ہیں

درد کے سنکھ بجتے ہیں

جس کی کشتِ سخن زرخیز ہے

جس میں گلاب و سمن مہکتے ہیں

اور مرغانِ خوش نوا چہکتے ہیں

میرے اندر کے تار بجتے ہیں

مجھے ذوقِ ادب کا خزانہ دیا

لکھنا، پڑھنا، بولنا سکھایا

اظہار و بیان کا سلیقہ سمجھایا

پھر میں ایک ایسے دیوتا کے سپرد ہوا

جس کا مندر دیوتائوں کاعجائب گھر

جہاں شعور و فکر کے دیپ جلتے ہیں

جہاں علم و سخن کے گَجر بجتے ہیں

جس نے میری تپسیا کو بھاگ لگائے

میرے لفظ و معانی کو راگ دیے

میری سوچ کو پر لگائے

لفظوں کے جنگل سے خیال و معنی کے پھول چننے کا سلیقہ سکھایا

مجھے تو دوئی راس آگئی

یعنی مجھے پیاس بھا گئی

Twelve Tips for Enhancing Student Learning Experience in the Operating Room

Student learning within the Operating Room (OR) is complex and challenging, especially for medical students who heavily rely upon structured learning plans. Medical students’ OR-based surgical learning experience is heterogenous, unstandardized, and inadequate for many reasons. There is a growing need to evaluate the learning modalities and models that we currently use for medical graduates’ OR-based learning process, create a balance between structured and opportunistic learning encounters and incorporate previously identified factors that have been known to influence the quality of OR-based learning positively. In continuation with our previous work on OR-based learning, here we argue for a structured OR-based learning plan that embodies appropriate learning models and teaching methodologies and focuses on a comprehensive plan that justifies a local needs analysis and addresses factors influencing the quality of OR-based student learning to produce enhanced learning outcomes.

Diagnostically Lossless Compression of Medical Images

Medical images are very important for diagnostics and therapy. However, digital imaging generates large amounts of data which need to be compressed, without loss of relevant information, to economize storage space and allow speedy transfer. In this research three techniques are implemented for medical image compression, which provide high compression ratios with no loss of diagnostic quality. Different image modalities are employed for experiments in which X-rays, MRI, CT scans, Ultrasounds and Angiograms are included. The proposed schemes are evaluated by comparing with existing standard compression techniques like JPEG, lossless JPEG2000, LOCOI and Huffman Coding. In a medical image only a small region is diagnostically relevant while the remaining image is much less important. This is called Region of Interest (ROI). The first approach compresses the ROI strictly losslessly and the remaining regions of the image with some loss. In the second approach an image is first compressed at a high compression ratio but with loss, and the difference image is then compressed losslessly. Difference image contain less data and is compressed more compactly than original. Third approach exploits inter-image redundancy for similar modality and same part of human body. More similarity means less entropy which leads to higher compression performance. The overall compression ratio is combination of lossy and lossless compression ratios. The resulting compression is not only strictly lossless, but also expected to yield a high compression ratio. These techniques are based on self designed Neural Network Vector Quantizer (NNVQ) and Huffman coding. Their clever combination is used to get lossless effect. These are spatial domain techniques and do not require frequency domain transformation. i An overall compression ratio of 6-14 is obtained for images with proposed methods. Whereas, by compressing same images by a lossless JPEG2K and Huffman, compression ratio of 2 is obtained at most. The main contribution of the research is higher compression ratios than standard techniques in lossless scenario. This result will be of great importance for data management in a hospital and for teleradiology.
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Asian Research Index Whatsapp Chanel

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