پديد آورنده :
كريمي، نادر
عنوان :
فشرده سازي بدون اتلاف تصاوير پزشكي با حجم زياد
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر
صفحه شمار :
پانزده،178ص.: مصور،جدول،نمودار﴿رنگي﴾
يادداشت :
ص.ع.به فارسي و انگليسي
استاد راهنما :
شادرخ سماوي
استاد مشاور :
سعيد صدري، شهرام شيراني
توصيفگر ها :
پيشگويي , مدل سازي زمينه , RNAi , ماموگرافي , ريز آرايه
تاريخ نمايه سازي :
23/2/92
استاد داور :
شهره كسايي، محمدرضا يزدچي، محمدرضا احمدزاده
دانشكده :
مهندسي برق و كامپيوتر
كد ايرانداك :
ID511 دكتري
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
Lossless Compression of Large Volume Medical Images Nader Karimi n karimi@ec iut ac ir Date of Submission December 13 2012 Department of Electrical and Computer Engineering Isfahan University of Technology 84156 83111 Isfahan IranSupervisor Shadrokh Samavi samavi96@cc iut ac ir1st Advisor Saeid Sadri 2nd Advisor Shahram Shirani Department Graduate Program Coordinator Masood Omoomi Department of Electrical and Computer Engineering Isfahan University of Technology Department of Electrical and Computer Engineering McMaster University Abstract One of the common stages in medical studies is to use of modern imaging tools In methods such asRNAi process mammography and microarray experiments large amounts of image data are producedwhich demands for customized compression methods The main objective of this dissertation is to identifythe characteristics of these types of medical images and use them to provide efficient lossless compressionapproaches In the first step different views and perspectives of image compression are investigated and acomprehensive classification is presented General purpose lossless image compression methods are alsothoroughly studied and characteristics of major components and common stages in the design of thesemethods are extracted Then a new classification is proposed accordingly In the second step after a deepevaluation of extracted stages the more effective stages are identified and thoroughly analyzed In the thirdstep initially one of the most powerful prediction schemes is analyzed with a new perspective and itsstrengths and weaknesses are identified Also behavior of this predictor under different pixel formations ismodeled and solutions to improve its accuracy are provided After that based on the performed analyses new methods for lossless compression of RNAi mammography and microarray images are proposed Finally efficiencies of the proposed methods are compared with the standards special purpose and state of the art general purpose lossless compression methods The obtained results show the superiority of theproposed methods in comparison with the most powerful compression schemes KeywordsLossless Compression Prediction Context Modeling RNAi Images Mammography Images MicroarrayImages
استاد راهنما :
شادرخ سماوي
استاد مشاور :
سعيد صدري، شهرام شيراني
استاد داور :
شهره كسايي، محمدرضا يزدچي، محمدرضا احمدزاده
