A low cost approach for brain tumor segmentation based on intensity modeling and 3D Random Walker. (September 2015)

• Record Type: Journal Article
• Authors: Kanas, Vasileios G.; Zacharaki, Evangelia I.; Davatzikos, Christos; Sgarbas, Kyriakos N.; Megalooikonomou, Vasileios
• Is Part Of: Biomedical signal processing and control. Volume 22(2015)
• Page Start: 19
• Page End: 30
• Publication Date: 2015-09
• Subjects: Tumor segmentation -- Outlier detection -- Random walks -- Brain neoplasms -- Magnetic resonance imaging (MRI); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.06.004
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An unsupervised, low cost, hybrid approach for brain tumor segmentation is proposed. Global intensity modeling and local intensity variation are incorporated. Applicability to different malignancy grades. Our approach requires only routine MRI. This study might provide a decision-support tool for neoplastic tissue segmentation. Abstract: Objective: Magnetic resonance imaging (MRI) is the primary imaging technique for evaluation of the brain tumor progression before and after radiotherapy or surgery. The purpose of the current study is to exploit conventional MR modalities in order to identify and segment brain images with neoplasms. Methods: Four conventional MR sequences, namely, T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid attenuation inversion recovery, are combined with machine learning techniques to extract global and local information of brain tissues and model the healthy and neoplastic imaging profiles. Healthy tissue clustering, outlier detection and geometric and spatial constraints are applied to perform a first segmentation which is further improved by a modified multiparametric Random Walker segmentation method. The proposed framework is applied on clinical data from 57 brain tumor patients (acquired by different scanners and acquisition parameters) and on 25 synthetic MR images with tumors. Assessment is performed against expert-defined tissue masks and is based on sensitivity analysis and Dice coefficient. Results: The results demonstrate that the proposed multiparametric framework differentiates neoplastic tissues with accuracy similar to most current approaches while it achieves lower computational cost and higher degree of automation. Conclusion: This study might provide a decision-support tool for neoplastic tissue segmentation, which can assist in treatment planning for tumor resection or focused radiotherapy.