IMPROVING HEPATIC TUMORS CLASSIFICATION IN MULTIPHASE COMPUTED TOMOGRAPHY USING COMBINED REGIONAL AND SPATIO-TEMPORAL CLASSIFICATION APPROACHES / (Record no. 9079)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04215nam a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210831s2021 |||||||f mb|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | EG-CaNU |
| Transcribing agency | EG-CaNU |
| 041 0# - Language Code | |
| Language code of text | eng |
| Language code of abstract | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Shaimaa Ali Shaheen Omer |
| 245 1# - TITLE STATEMENT | |
| Title | IMPROVING HEPATIC TUMORS CLASSIFICATION IN MULTIPHASE COMPUTED TOMOGRAPHY USING COMBINED REGIONAL AND SPATIO-TEMPORAL CLASSIFICATION APPROACHES / |
| Statement of responsibility, etc. | Shaimaa Ali Shaheen Omer |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 102 p. |
| Other physical details | ill. |
| Dimensions | 21 cm. |
| 500 ## - GENERAL NOTE | |
| General note | Supervisor: Sahar A. Fawzi |
| 502 ## - Dissertation Note | |
| Dissertation type | Thesis (M.A.)—Nile University, Egypt, 2021 . |
| 504 ## - Bibliography | |
| Bibliography | "Includes bibliographical references" |
| 505 0# - Contents | |
| Formatted contents note | Contents:<br/>List of Tables .............................................................................................................. vi<br/>List of Figures ............................................................................................................ vii<br/>List of Equations ....................................................................................................... xiii<br/>Abstract ..................................................................................................................... xiv<br/>Introduction ................................................................................................................... 1<br/>Methodology ............................................................................................................... 28<br/>Preprocessing ...................................................................................................29<br/>The Model ........................................................................................................36<br/>Feature Extraction........................................................................................................39<br/>Spatial Approach ..............................................................................................39<br/>Spatio-Temporal Approach ..............................................................................46<br/>Regional Information .......................................................................................53<br/>Results ......................................................................................................................... 61<br/>Discussion and Future Work ....................................................................................... 86<br/>References ........................... |
| 520 3# - Abstract | |
| Abstract | Abstract:<br/>Liver cancer is the most frequently diagnosed cancer in Egypt and North Africa. Early diagnosis provides a better chance for a curative treatment.<br/>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.<br/>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).<br/>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.<br/>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 ## - Language Note | |
| Language Note | Text in English, abstracts in English and Arabic |
| 650 #4 - Subject | |
| Subject | Informatics-IFM |
| 655 #7 - Index Term-Genre/Form | |
| Source of term | NULIB |
| focus term | Dissertation, Academic |
| 690 ## - Subject | |
| School | Informatics-IFM |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| 650 #4 - Subject | |
| -- | 266 |
| 655 #7 - Index Term-Genre/Form | |
| -- | 187 |
| 690 ## - Subject | |
| -- | 266 |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Main library | Main library | 08/31/2021 | 610 / S.A.I / 2021 | 08/31/2021 | 08/31/2021 | Thesis |