Automated Segmentation and Size Estimation of Brain Tumor from Mri Images (Record no. 67259)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02451nam a22001337a 4500 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Aamir Hussain |
| -- | 16MIAC01 |
| -- | Supervisor-Dr. Ehsan Ali Burio |
| 245 ## - TITLE STATEMENT | |
| Title | Automated Segmentation and Size Estimation of Brain Tumor from Mri Images |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Nawabshah |
| Name of publisher | QUEST |
| Year of publication | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 42 |
| 500 ## - GENERAL NOTE | |
| General note | <br/><br/><br/><br/><br/><br/><br/>ABSTRACT<br/><br/><br/>The segmentation of brain tum or using Magnetic Resonance Image (MRI) plays an important role m the medical image process. The mam reason behind this process is to<br/> <br/>separate the different types<br/> <br/>0f t'<br/>issues such as necrotic core, active cells and edema<br/> <br/>from normal brain tissue of h ·t<br/>w 1 e matter (WM), gray matter (GM) and cerebrospinal<br/>fluid (CSF). Presently the b f . . . .<br/>num er o patients of brain tumor are increasing<br/>significantly and the treatment of brain tumor is highly expensive. The rad10log1st an<br/>neurologist have to do a tough job for the detection and treatment of brain tumor in the patients. Ultimately they need fast and efficient treatment methods. A number of techniques to detect the brain tumor have been proposed in literature each having merits and demerits. In this study a new automated system is proposed for brain tumor detection in which features of MRI images are extracted by using segmentation and morphological operation. The proposed framework consists of three phases; in the first phase the input MRI image is preprocessed to remove the noise and sharpen the image for the purpose of better segmentation of the tumor. In the second phase, the threshold segmentation is applied to extract the tumor region and quantify the tumor area in terms of square per inch and the number of pixels reserved by the tumor regions present in the brain. Finally, in the third phase the post-processing is done to remove the false regions so that the extracted tumor is better understandable to the radiologist. It was revealed that the proposed technique offers tumor region by consuming less time in milliseconds and low computational cost and classifies the tumor region in the forefront for the considerable visual investigation that has a percentage difference of<br/>le than 1%.<br/><br/> <br/>Keywords: Magnetic Resonance operation.<br/> <br/>Image<br/> <br/>(MRI)•<br/> <br/>Segmentation, Morphological<br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Master of Engineering |
| -- | Industrial Automation & Control |
| -- | Department of Electronic Engineering |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://tinyurl.com/2s437hzj |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Thesis and Dissertation |
| Withdrawn status | Lost status | Home library | Current library | Date acquired | Accession Number | Koha item type |
|---|---|---|---|---|---|---|
| Research Section | Research Section | 26/08/2021 | MP/65-827 | Thesis and Dissertation | ||
| Research Section | Research Section | 17/08/2023 | MP/63-806 | Thesis and Dissertation |