Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Combined features in region of interest for brain tumor segmentation

Alqazzaz, Salma, Xianfang, Sun, Nokes, Len D. M., Yang, Hong, Yang, Yingxia, Xu, Ronghua, Zhang, Yanqiang and Yang, Xin 2022. Combined features in region of interest for brain tumor segmentation. Journal of Digital Imaging 10.1007/s10278-022-00602-1

Full text not available from this repository.

Abstract

Diagnosis of brain tumor gliomas is a challenging task in medical image analysis due to its complexity, the less regularity of tumor structures, and the diversity of tissue textures and shapes. Semantic segmentation approaches using deep learning have consistently outperformed the previous methods in this challenging task. However, deep learning is insufficient to provide the required local features related to tissue texture changes due to tumor growth. This paper designs a hybrid method arising from this need, which incorporates machine-learned and hand-crafted features. A semantic segmentation network (SegNet) is used to generate the machine-learned features, while the grey-level co-occurrence matrix (GLCM)-based texture features construct the hand-crafted features. In addition, the proposed approach only takes the region of interest (ROI), which represents the extension of the complete tumor structure, as input, and suppresses the intensity of other irrelevant area. A decision tree (DT) is used to classify the pixels of ROI MRI images into different parts of tumors, i.e. edema, necrosis and enhanced tumor. The method was evaluated on BRATS 2017 dataset. The results demonstrate that the proposed model provides promising segmentation in brain tumor structure. The F-measures for automatic brain tumor segmentation against ground truth are 0.98, 0.75 and 0.69 for whole tumor, core and enhanced tumor, respectively.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Engineering
ISSN: 1618-727X
Date of Acceptance: 3 February 2022
Last Modified: 13 Apr 2022 15:00
URI: https://orca.cardiff.ac.uk/id/eprint/147962

Actions (repository staff only)

Edit Item Edit Item