000			03094cam a2200325 a 4500
003			EG-GiCUC
008			190401s2018 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.13.03.M.Sc.2018.Wa.E
100	0		_aWafa{u2019}a Abdulhameed Abdullah Alolofi
245	1	0	_aEffects of scaling, noise, and compression on breast cancer detection in whole slide images / _cWafa{u2019}a Abdulhameed Abdullah Alolof ; Supervised Ahmed Mohamed Badawi , Muhammad Ali Rushdi , Mohammed Ahmed Islam
246	1	5	_aتأثير التكبير والضوضاء وضغط البيانات على الكشف عن سرطان الثدي في صور الشرائح الكاملة
260			_aCairo : _bWafa{u2019}a Abdulhameed Abdullah Alolof , _c2018
300			_a107 P. : _bcharts , facsimiles ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering
520			_aWhole slide imaging (WSI) is a recent technology introduced in medical pathology practices. WSI images are created using a computerized system that scans, changes, and stitches pathology specimen glass slides into digital images that have a multi-resolution pyramid construction of a huge gigabyte size. Therefore, digital whole slide imaging brings major challenges in data storage, transmission (telepathology), processing and interoperability.Whole-slide imaging enables histopathological analysis of biological tissues at very high levels of magnification, and hence the early detection of anomalies such as breast cancer can be achieved. In thiswork, we investigate the effects of scaling, compression, and noise on anomaly detection in whole slide imaging. ThusThus, we analyze the effects of image scale on anomaly detection performance. We propose a learning-based approach to find the scale mappings between WSI levels using partial least-square (PLS) regression.The learned scale mapping can be used to detect anomalies in lower-resolution images and small magnification hence reduce the computational cost of anomaly detection.Then we explore the effect of different levels of noise on anomaly detection. We simulate different scenarios where WSI images are contaminated with Gaussian noise and several de-noising algorithms were applied, namely de-noising with PLS, Block Matching 3D (BM3D) and the combination of PLS and BM3D. We show how these different de-noising techniques can help to reduce the noise severity on anomaly detection.Our results lead to useful conclusions on how to handle whole slide images under scaling, compression, and noise conditions
530			_aIssued also as CD
653		4	_aDigital pathology
653		4	_aVirtual microscopy
653		4	_aWhole-slide imaging
700	0		_aAhmed Mohamed Badawi , _eSupervisor
700	0		_aMohammed Ahmed Islam , _eSupervisor
700	0		_aMuhammad Ali Rushdi , _eSupervisor
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c71135 _d71135