access icon free Fast and Automatically Adjustable GRBF Kernel Based Fuzzy C-Means for Cluster-wise Coloured Feature Extraction and Segmentation of MR Images

Fuzzy C-means algorithm is a popular image segmentation algorithm and many researchers in the past have introduced several improved versions of it. However, they still lacked robustness for segmenting key regions of magnetic resonance (MR) human brain transversal images such as white matter, grey matter, and cerebro spinal fluid with an almost similar contouring of the edges. This study highlights a robust algorithm that is effective in four ways: (i) a distinct contrast between different regions of the human brain, (ii) reduce noise as a result of the contrast stretching procedure, (iii) efficient analysis using both grey scale segmented image and its colour segmented version, and (iv) striking a balance between time consumption and image segmentation results. These objectives have been achieved in two phases: first, MR image segmentation using fast and automatically adjustable Gaussian radial basis function kernel based fuzzy C-means (FAAGKFCM) algorithm which utilises a new objective function that incorporates a similarity measure having both spatial and grey level measure factor (Sij ), an adaptive factor (wi ) to remove discrepancy of grey scale image, and using a Gaussian radial basis function (GRBF) kernel-based distance metric in place of the traditional Euclidean distance metric. Second, converting FAAGKFCM segmented image into CIE L*a*b* colour space and successively performing cluster-wise feature extraction using hard k-means clustering.

Inspec keywords: image colour analysis; image segmentation; biomedical MRI; medical image processing; radial basis function networks; fuzzy set theory

Other keywords: fast and automatically adjustable Gaussian radial basis function kernel based fuzzy C-means algorithm; GRBF kernel based fuzzy C-means; magnetic resonance human brain transversal images; colour segmented version; grey scale segmented image; MR image segmentation; FAAGKFCM algorithm; cluster-wise coloured feature extraction

Subjects: Biology and medical computing; Algebra, set theory, and graph theory; Biomedical magnetic resonance imaging and spectroscopy; Medical magnetic resonance imaging and spectroscopy; Optical, image and video signal processing; Combinatorial mathematics; Combinatorial mathematics; Patient diagnostic methods and instrumentation; Computer vision and image processing techniques

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