Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

Abstract

BackgroundIn recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images.This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain.MethodsThe study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system.ResultsThe quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain.Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related.ConclusionsResults indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use.

Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI

“…meningiomas and gliomas. For semi-automated segmentations, various approaches have been applied, such as region growing, random walker, non-negative matrix factorisation, fuzzy clustering and livewire algorithm [20][21][22][23][24]. Automated tumour volume definition has also been applied in post-radiation patients using an algorithm that is based on the Chan-Vese active contour method and patient-specific intensities [25].…”

Measurement of Apparent Diffusion Coefficient Values from Diffusion-Weighted MRI: A Comparison of Manual and Semiautomatic Segmentation Methods

“…Relatively similar T1 and T2 relaxation parameters among pathologic brain lesions, brain edema, and fibrotic tissue are obtained on T2WI, as compared with CE-T1WI (20). It is also known that the differences in signal intensities among the clusters are not sufficient to separate tumor components, such as solid, cystic, or hemorrhagic components, perilesional edema, and perilesional tumor infiltration, automatically on T2WI (10,22). According to the aforementioned reports, the T2WI-based semiautomatic segmentation method would be less reproducible in the groups with perilesional edema, with poorly demarcated inner margins of the necrotic portion, and with poorly demarcated outer tumor borders.…”

Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast

“…For those tumor types, the threshold-based segmentation method may be applied to T 1 -weighted or diffusion-weighted images, if the contrast between tumor and other tissue is more visible on those images. While the threshold-based method is likely to require re-definition of the set thresholds for each tumor type, automatic selection of clusters could possibly also be performed on the basis of features more specific to tumors in general, for example shape, size [18], and tissue homogeneity [9]. Recently, Linguraru et al [34] showed that liver tumors can be accurately detected on contrastenhanced computed tomography images by using a set of features that describe the intensity, shape, size, and homogeneity of identified objects of interest in the liver.…”

“…Hsieh et al [18] reported a method for automatic segmentation of meningiomas on the basis of T 1 and T 2 -weighted images. Although successful segmentation was achieved for most tumors, the method failed in approximately 20 % of cases.…”