000			03297nam a22003017a 4500
005			20160428124836.0
008			160428s2014 ua a|||e |||| 00| 0 eng d
040			_aOCUN _bara _cSA-RiAUC.
041	0		_aeng _heng.
082	0	4	_221 _a004.67 _bA M
100	0		_aAhmed Emad El-Din Fakhry
245	0	0	_aMedical Image Registration Using Modified Mutual Information And Partical Swarm Optimization / _cSubmitted By :Ahmed Emad El-Din Fakhry ; Supervised By Prof.Essam Hamed Atta , Prof.Ibraheem El-Emam.
246	0	0	_aمطابقة الصور بإستخدام المعلومات المتبادلة و امثلة أسراب الجسيمات .
260			_a[Cairo ] ; _b[Arab Academy For Sciemce And Technology & Maritime Transport] ; _c2014.
300			_a68 P ; _bIll ; _c30 Cm + _eCD.
500		3	_aFaculty Of Computer science.
502			_aA Thesis Submitted to The Collegy Of Computing & Information Technology In Partial Fulifillment Of The Requirements For The Award O Degree Of MASTER Of Science In Computer science
504			_aIncludes Bibliographic Referances.P 62 : 68.
520			_aan important aspect in medical image analysis, and is used in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from different modalities such as Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame of reference. Registered datasets can be used to provide information about the structure, function, and pathology of the organ or individual being imaged. _bIn this thesis a hybrid approach for medical images registration is developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified by using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is implemented by using the versatile Particle Swarm Optimization which is easy to implement and contains few parameters to adjust. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate that the proposed approach is accurate and effective, and show that the inclusion of both statistical and spatial image data improves the registration results obtained using only image intensity data.
530			_aIncludes CD copy for The Thesis .
546			_aIncludes Abstract in Arabic
650			_2qarmak _aMedical Image
650			_2qarmk _aComputing & Information Technology - _xComputer Science
700	0		_aEssam Hamed Atta , _eSupervisor
700	0		_a.Ibraheem El-Emam , _eSupervisor
710			_aArab Academy For Sciemce And Technology & Maritime Transport ; _bCollege Of Computing & Information Technology .
942			_2ddc _cTHE
999			_c57250 _d57250