Abstract
The medical image segmentation techniques are frequently used to diagnosis, tumor detection and determining anatomical structures in brain MRI image and further classifying the pathological regions for treatment planning in clinical analysis. Manual analysis of multi-spectral MRI images is erratic process due to wide-ranging of features and tissue types. Midst several segmentation techniques proposed in literature work, an automatic unified segmentation framework substantiate to be effective method for multi-spectral MRI images segmentation. The efficient and robust framework holds varied popular techniques to accomplish segmentation of multi-spectral MRI images. An Efficient and robust MRI segmentation framework is intended to conglomerate benefits of most popular SVM, Watershed and EM-GM techniques for automatic and accurate segmentation and diagnosis of tumor in multi-spectral MRI images. The SVM method is converts input space to high-dimensional space where multi-spectral MRI images image are typically linear and indivisible to compute best linear discriminant surface. The proposed Efficient and robust Framework has a comprehensive tumor analysis structure which comprise of three stages for identification, segmentation and extraction of disorder region in multi-spectral MRI images. The efficient and robust framework has automatic identification of disorder in MRI Images successively tumor region segmentation. The stage-1 is focused on identification of input MRI Images and classify MRI Images into normal or disorder MRI Images with SVM method. The stage-2 purpose is to segment the tumor with Watershed based technique in disorder in MRI Images that is detected in Stage-1. In stage-3 has an EM-GM for tumor region extraction and approximation of tumor region in actual image. To demonstration an effectiveness of the efficient and robust framework, the proposed framework is evaluated on simulated multi-spectral MRI images from standard open BraTS MRI dataset delivered by ground truth segmentation.
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Bhima, K., Neelakantappa, M., Dasaradh Ramaiah, K., Jagan, A. (2023). An Efficient and Automatic Framework for Segmentation and Analysis of Tumor Structure in Brain MRI Images. In: Mercier-Laurent, E., Fernando, X., Chandrabose, A. (eds) Computer, Communication, and Signal Processing. AI, Knowledge Engineering and IoT for Smart Systems. ICCCSP 2023. IFIP Advances in Information and Communication Technology, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-39811-7_6
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