Medical Image Compression based on Polynomial Coding and Region of Interest
| المصدر: | مجلة جامعة الحسين بن طلال للبحوث |
|---|---|
| الناشر: | جامعة الحسين بن طلال - عمادة البحث العلمي والدراسات العليا |
| المؤلف الرئيسي: | Al-Khafaji, Ghadah (Author) |
| مؤلفين آخرين: | Sami, Anhar (Co-Author) |
| المجلد/العدد: | مج5, ملحق |
| محكمة: | نعم |
| الدولة: |
الأردن |
| التاريخ الميلادي: |
2019
|
| الصفحات: | 48 - 59 |
| DOI: |
10.36621/0397-005-985-004 |
| ISSN: |
2519-7436 |
| رقم MD: | 1035860 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EduSearch, AraBase, HumanIndex |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Medical Imaging | Image Compression | Polynomial Coding | Region of Interest
|
| رابط المحتوى: |
عدد مرات التحميل
2
| المستخلص: |
Medical imaging (MI) plays a major role in contemporary health care, both as a tool in primary diagnosis and as a guide for surgical and therapeutic procedures. The trend in MI is increasingly ubiquitous, where digital data do not suffer from aging and moreover are suited to archived, stored and retrieved quickly and reliably, but unfortunately there is huge byte consumptions. Image compression refers to reduce the amount of data in an image for storing or transmitting it in an efficient form. Generally, this can be achieved by removing redundant or irrelevant information and keeping only the relevant information. The medical image compression techniques, currently exploited the region of interest (ROI) concept, sometimes there’s important region of the image rather than the whole image, where segregate the required region from the image play a vital role. The first part of this paper exploits the traditional polynomial coding techniques modeling based to compress gray scale medical image losslessly. The Second part aims at enhancing the performance of the medical coding techniques, by incorporating the ROI of hybrid base, where lossless polynomial coding used for ROI portion and the scalar uniform quantizer for non-ROI portion, also the ROI utilized according to image nature which either of simple edge based one, or threshold complex feature based. The tested results shown are promising in terms of high compression ratio and preserving the quality perfectly, with efficiently separate the ROI portion by utilizing the edge and beneficial features of the object. |
|---|---|
| ISSN: |
2519-7436 |
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