000			04215nam a22002537a 4500
008			210831s2021 |||||||f mb|| 00| 0 eng d
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng
082			_a610
100	0		_aShaimaa Ali Shaheen Omer _9573
245	1		_aIMPROVING HEPATIC TUMORS CLASSIFICATION IN MULTIPHASE COMPUTED TOMOGRAPHY USING COMBINED REGIONAL AND SPATIO-TEMPORAL CLASSIFICATION APPROACHES / _cShaimaa Ali Shaheen Omer
260			_c2021
300			_a102 p. _bill. _c21 cm.
500			_aSupervisor: Sahar A. Fawzi
502			_aThesis (M.A.)—Nile University, Egypt, 2021 .
504			_a"Includes bibliographical references"
505	0		_aContents: List of Tables .............................................................................................................. vi List of Figures ............................................................................................................ vii List of Equations ....................................................................................................... xiii Abstract ..................................................................................................................... xiv Introduction ................................................................................................................... 1 Methodology ............................................................................................................... 28 Preprocessing ...................................................................................................29 The Model ........................................................................................................36 Feature Extraction........................................................................................................39 Spatial Approach ..............................................................................................39 Spatio-Temporal Approach ..............................................................................46 Regional Information .......................................................................................53 Results ......................................................................................................................... 61 Discussion and Future Work ....................................................................................... 86 References ...........................
520	3		_aAbstract: Liver cancer is the most frequently diagnosed cancer in Egypt and North Africa. Early diagnosis provides a better chance for a curative treatment. Dynamic contrast-enhanced computed tomography (DCE-CT) is the most commonly used technique to differentiate liver tissues. Since, it is available everywhere, accommodates less cost, and takes short scanning time. In this thesis, DCE-CT images are used to investigate the impact of regional-bases extraction of the spatio-temporal features on the liver lesions classification performance. Texture, density, and temporal feature set, and their combination along spatially partitioned ROI are investigated to characterize five hepatic pathologies from DCE-CT images. Decision tree ensembles (random forest) are employed in both feature selection and classification with ten folds cross-validation to classify a total of 180 ROI. Dataset includes normal tissues, cyst, hemangioma, metastatic and hepatocellular carcinoma (36 ROI for each liver pathology). The results suggest that normal liver tissues could easily be recognized from just the density features (accuracy = 99%), whereas texture features have the best performance in classifying HCC (accuracy = 96%). Combining all feature sets could overcome individual performance variations among them and attain consistent better results for all tumor types. Moreover, regional information improves the classification performance in all the classes' results. An overall accuracy of 96% is achieved for the five liver lesions classification.
546			_aText in English, abstracts in English and Arabic
650		4	_aInformatics-IFM _9266
655		7	_2NULIB _aDissertation, Academic _9187
690			_aInformatics-IFM _9266
942			_2ddc _cTH
999			_c9079 _d9079