The detection, segmentation, and extraction from Magnetic Resonance Imaging (MRI) images of contaminated tumor areas are significant concerns; however, a repetitive and extensive task executed by radiologists or clinical experts relies on their expertise. Image processing concepts can imagine the various anatomical structure of the human organ. Detection of human brain abnormal structures by basic imaging techniques is challenging. In this paper, a Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) has been proposed for brain tumor segmentation based on deep learning techniques. The present work proposes the separation of the whole cerebral venous system into MRI imaging with the addition of a new, fully automatic algorithm based on structural, morphological, and relaxometry details. The segmenting function is distinguished by a high level of uniformity between anatomy and the neighboring brain tissue. ELM is a type of learning algorithm consisting of one or more layers of hidden nodes. Such networks are used in various areas, including regression and classification. In brain MRI images, the probabilistic neural network classification system has been utilized for training and checking the accuracy of tumor detection in images. The numerical results show almost 98.51% accuracy in detecting abnormal and normal tissue from brain Magnetic Resonance images that demonstrate the efficiency of the system suggested.

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Spinal pathology treatment has become an urgent issue to be solved. How to effectively prevent and treat spinal pathology has become a research hotspot in the field of surgery. Aiming at the problem of too long volume rendering time caused by the trilinear interpolation sampling method in the reconstruction and visualization of the vertebra 3D model, an improved ray projection algorithm is proposed to quickly reconstruct a 3D vertebra model from medical CT vertebra images. This method first classifies CT data, assigns corresponding color values and opacity transfer functions to different types of data, and then uses inverse distance-weighted interpolation (IDWI) sampling to replace the trilinear interpolation sampling method for the voxel where the sampling point is located to accelerate the interpolation operation. The color value and opacity of the sampling points are obtained, and finally, the attributes of all the sampling points are synthesized and calculated to obtain the final rendering effect, and the reconstruction of the three-dimensional vertebra model is completed. Experimental results show that the proposed method not only can obtain higher quality rendered images but also has a certain improvement in rendering speed compared with traditional algorithms.