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Review
. 2022 Jul 11:2022:1541980.
doi: 10.1155/2022/1541980. eCollection 2022.

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Khurram Ejaz  1 , Mohd Shafry Mohd Rahim  2 , Muhammad Arif  1 , Diana Izdrui  3 , Daniela Maria Craciun  3 , Oana Geman  3
Affiliations
Review

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Khurram Ejaz et al. Contrast Media Mol Imaging. .

Abstract

Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction. Segmentation can be performed in steps; firstly, the region is extracted from the image. Secondly, feature extraction performed, and better features are selected. They can be shape, texture, or intensity. Thirdly, clustering segments the shape of tumor, tumor has specified shape, and shape is detected by feature. Clustering consists of FCM, K-means, FKM, and their hybrid. To support the segmentation, we conducted three studies (region extraction, feature, and clustering) which are discussed in the first line of this review paper. All these studies are targeting MRI as a modality. MRI visualization proved to be more accurate for the identification of diseases compared with other modalities. Information of the modality is compromised due to low pass image. In MRI Images, the tumor intensities are variable in tumor areas as well as in tumor boundaries.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Process of medical image segmentation.
Figure 2
Figure 2
Architecture of MRI machine (source: [93]).
Figure 3
Figure 3
Image of T1 (BraTS17_13_2_1).
Figure 4
Figure 4
Image of FLAIR (BraTS17_13_2_1).
Figure 5
Figure 5
Image of T2 (BraTS17_13_2_1).
Figure 6
Figure 6
Image of T1CE (BraTS17_13_2_1).
Figure 7
Figure 7
Soft computing and image segmentation approaches.
Figure 8
Figure 8
Input T1 sequence image.
Figure 9
Figure 9
Image enhancement of Figure 8.
See this image and copyright information in PMC

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