3D multimodal k-means and morphological operations (3DMKM) segmentation of brain tumors from MR images

• Main Authors: George, Reuben, Chow, Li Sze, Lim, Kheng Seang
• Format: Conference or Workshop Item
• Published: IEEE 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.um.edu.my/46292/; https://doi.org/10.1109/IECBES54088.2022.10079510

Tumor segmentation algorithms can aid in prognosis and treatment, and are a better alternative to manual segmentation. This study combined thresholding, morphological operations and k-means segmentation to create a new algorithm called 3D multimodal k-means and morphological operations algorithm (3D-MKM) for segmenting tumors. This algorithm used the fast spoiled gradient (FSPGR), T2 weighted fast spin echo (T2-FSE), T2 weighted fluid-attenuated inversion recovery (T2-FLAIR) and contrast enhanced FSPGR (C-FSPGR) as input images. It adjusted the histograms of each sequence to highlight the tumor regions, then performed a thresholding on the T2FLAIR scan to obtain the region of interest (ROI) mask containing the tumor, edema and surrounding tissue. A multichannel view of the ROI was then made by combining the images from different sequences. The multichannel ROI was then segmented by the k-means algorithm into clusters. Next, the clusters were assembled into the enhancing tumor, non-enhancing tumor and edema masks, and further refined using morphological operations. The 3D-MKM algorithm was tested on 9 datasets. It demonstrated promising results in segmenting the entire lesion, with a Sorensen-Dice similarity coefficient of 0.88 +/- 0.05 and a Hausdorff distance of 12.08 +/- 7.07 mm from ground truth. Clinical Relevance- 3D-MKM is able to segment the enhancing tumor, nonenhancing tumor, and edema. The segmented portions of the tumor could be used to extract quantitative data for the study of brain tumors.