A modified Seeded Region Growing algorithm for vessel segmentation in breast MRI images for investigating the nature of potential lesions

2nd International Conference on Mathematical Modeling in Physical Sciences 2013

物理学;数学

Glotsos, D.^1 ; Vassiou, K.^3,4 ; Kostopoulos, S.^1 ; Lavdas, El^2 ; Kalatzis, I.^1 ; Asvestas, P.^1 ; Arvanitis, D.L.^3 ; Fezoulidis, I.V.^4 ; Cavouras, D.^1

Department of Medical Instruments Technology, Technological Education Institute of Athens, Greece^1

Department of Medical Radiologic Technology, Technological Educational Institute of Athens, Athens, Greece^2

Department of Anatomy, Medical School, University of Thessaly, Viopolis, Larissa, Greece^3

Department of Radiology, University Hospital of Larissa, Viopolis, Larissa, Greece^4

关键词: Contrast agent;    Ge healthcares;    Magnetic Resonance Imaging (MRI);    Research results;    Seeded region growing algorithms;    Time interval;    Vascular network;    Vessel segmentation;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/490/1/012136/pdf
DOI  :  10.1088/1742-6596/490/1/012136

来源: IOP

【 摘 要 】

The role of Magnetic Resonance Imaging (MRI) as an alternative protocol for screening of breast cancer has been intensively investigated during the past decade. Preliminary research results have indicated that gadolinium-agent administrative MRI scans may reveal the nature of breast lesions by analyzing the contrast-agent's uptake time. In this study, we attempt to deduce the same conclusion, however, from a different perspective by investigating, using image processing, the vascular network of the breast at two different time intervals following the administration of gadolinium. Twenty cases obtained from a 3.0-T MRI system (SIGNA HDx; GE Healthcare) were included in the study. A new modification of the Seeded Region Growing (SRG) algorithm was used to segment vessels from surrounding background. Delineated vessels were investigated by means of their topology, morphology and texture. Results have shown that it is possible to estimate the nature of the lesions with approximately 94.4% accuracy, thus, it may be claimed that the breast vascular network does encodes useful, patterned, information, which can be used for characterizing breast lesions.