An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis

Abstract

Brain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work, Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for different k. Then, the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness function. The clustered boundaries of the tissue classes further enhanced by morphological operations. The FC technique is hybridized with the standard image post-processing techniques such as marker controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets (DRLS). This procedure is implemented on renowned BRATS challenge dataset of different modalities and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of the slices is performed using the metadata information present in the DICOM header. The validation of the segmentation procedures against the ground truth images authorizes that the segmented objects of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity coefficients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D visualization and tumor volume estimation are done using accessible DICOM information.

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

“…Kanniappan et al [126] segmented abnormal areas in brain MRI slides. They used fuzzy clustering to model a semi-automatic system for detecting normal and abnormal areas in each brain MRI slide.…”

Extraction and Evaluation of Corpus Callosum from 2D Brain MRI Slice: A Study with Cuckoo Search Algorithm

“…Fredo et al (2015) employed the reaction diffusion regularized level set (RDRLS) method to delineate CC [ 10 ]. Vachet et al (2012) implemented the deformable active Fourier contour model [ 15 ], and İçer (2013) discussed a two-step approach based on the Gaussian mixture model and FCM to extract the CC [ 16 ]. Li et al (2013) executed an automated two-step segmentation scheme by combining the mean shift clustering technique-based image improvement and geometric active contour (GAC) dependant segmentation of CC [ 26 ].…”

“…Due to its clinical significance, a substantial amount of CC assessment procedures has been proposed and discussed by researchers [ 15 , 16 ]. Normally, the CC region is best visible in the sagittal view of two-dimensional (2D) brain MRI.…”

“…The visibility of the CC is also approximately similar to other normal brain tissues and hence, the segmentation of CC from the MRI requires some complex procedures in comparison with the separation of other brain regions. Considering literature, procedures such as manual segmentation [ 6 ], level set scheme [ 10 ], active contour method [ 15 ], and fuzzy C -means [ 16 ] are applied to extract CC with possible accuracy. Most of these approaches consider a two-step procedure to separate the CC from the sagittal view MRIs.…”

Biomedical Image Processing and Classification

“…An efficient framework for enhancing and segmenting brain MRIs to identify a tumor is discussed in [21]. The hybridized fuzzy clustering and distance regularized level set (DRLS) technique effectively extracted the region of interest (ROI) in the brain slices.…”