000			02457cam a2200337 a 4500
003			EG-GiCUC
005			20250223031724.0
008			170520s2016 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.20.04.M.Sc.2016.Ma.N
100	0		_aMaria Fayez Halim Ibrahim
245	1	2	_aA new technique for clustering of medical images / _cMaria Fayez Halim Ibrahim ; Supervised Ehab Hassanein , Soha Safwat
246	1	5	_aتقنية جديدة لتقسيم الصور الطبية الى مجموعات
260			_aCairo : _bMaria Fayez Halim Ibrahim , _c2016
300			_a82 Leaves : _bcharts , facsimiles ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Systems
520			_aMedical images became a huge problem due to the fast growing size of the medical image repositories, thousands of medical images are produced daily. Medical image repositories need to be well organized using an efficient and fast tool to allow researches or medical experts to extract useful information in the right time and as fast as possible. Organizing large medical image repositories can help in many fields as in medical fields that can be useful in diagnosis and knowing the history of a patient and in the researching area as it can be mined easily and be a necessary step before many application as content based image retrieval and medical image classification application. The objective of this thesis is to implement a new efficient clustering technique for medical images. This technique contains three main methods, the first is to extract features using gray-level co-occurrence matrix and apply PCA for dimensionality reduction, and then k-means clustering is applied. The second method where the 2D wavelet transforms is applied as a feature extraction and feature selection is used to select most efficient attributes, then k-means clustering is applied. The final and proposed technique is to combine the two models and apply k-means clustering
530			_aIssued also as CD
653		4	_aClustering
653		4	_aK-means
653		4	_aMedical images
700	0		_aEhab Hassanein , _eSupervisor
700	0		_aSoha Safwat Labib , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aSamia _eCataloger
942			_2ddc _cTH
999			_c60945 _d60945