Background: Image reconstruction is the mathematical process which converts the signals obtained from the scanning machine into an image. The reconstructed image plays a fundamental role in the planning of surgery and research in the medical field. Discussion: This paper introduces the first comprehensive survey of the literature about medical image reconstruction related to diseases, presenting a categorical study about the techniques and analyzing advantages and disadvantages of each technique. The images obtained by various imaging modalities like MRI, CT, CTA, Stereo radiography and Light field microscopy are included. A comparison on the basis of the reconstruction technique, Imaging Modality and Visualization, Disease, Metrics for 3D reconstruction accuracy, Dataset and Execution time, Evaluation of the technique is also performed. Conclusion: The survey makes an assessment of the suitable reconstruction technique for an organ, draws general conclusions and discusses the future directions.

In this manuscript, the novel three dimensional (3D) image reconstruction approach based on affinity propagated clustering aided computerized Inherent Seeded Region Growing and Deep learned Marching Cubes Algorithm (ISRG-DMCA) is proposed. The major purpose of this manuscript is to divide the brain tumor based on Shapelets. Here, the information about the shape/depth that can be obtained in every two dimensional (2D) image on the image stack is handled to acquire a 3D reconstruction, which provides high accurate 3D view of tumor Region of Interest (ROI). Then, the 3D model is rendered with the help of the proposed Deep learned Marching Cubes Algorithm (Deep MCA) at 3D reconstruction technique. The performance of the proposed method is executed in MATLAB. The simulation results show that the proposed ISRG-DMCA algorithm attains a higher detection rate 14.117%, 5.435%, higher accuracy rate 9.556%, 26.41% and lower execution time 66.667%, 75%, compared with the existing methods, like Improved Marching Cubes Algorithm (IMCA), Improved CNN-CRF method, respectively. In the proposed ISRG-DMCA method, the volume of the tumor has a length of 2.56 mm. Finally, the simulation outcomes demonstrate that the proposed ISRG-DMCA method can be able to find the optimal solutions efficiently and accurately.

Three-dimensional reconstruction is the process of acquiring the volumetric information from two dimensions, converting and representing it in three dimensions. The reconstructed images play a vital role in the disease diagnosis, treatment and surgery. Brain surgery is one of the main treatment options following the diagnosis of brain damage. The risk associated with brain surgery is high. Reconstructed brain images help the surgeons to visualize the exact location of tumor, plan and perform the surgical procedures from craniotomy to tumor resection with high precision. This survey provides an overview of the three-dimensional reconstruction techniques in MRI brain and brain tumors. The triangle generation methods and support vector machine methods are briefly described. The advantages and disadvantages of each method is discussed. The comparison reveals that Immune Sphere Shaped Support Vector Machine is the best choice when execution time is considered and triangle mesh generation algorithm is the best when visual quality is considered.

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Abstract:The popularity of internet as a communication medium whether for personal or business requires anonymous communication in various ways. Businesses also have legitimate reasons to make communication anonymous and avoid the consequences of identity revelation. The problem of sharing privately held data so that the individuals who are the subjects of the data cannot be identified has been researched extensively. Researchers have understood the need of anonymity in various application domains: patient medical records, electronic voting, e-mail, social networking, etc. Another form of anonymity, as used in secure multiparty computation, allows multiple parties on a network to jointly carry out a global computation that depends on data from each party while the data held by each party remains unknown to the other parties. The secure computation function widely used is secure sum that allows parties to compute the sum of their individual inputs without mentioning the inputs to one another. This function helps to characterize the complexities of the secure multiparty computation. Another algorithm for sharing simple integer data on top of secure sum is built. The sharing algorithm will be used at each iteration of this algorithm for anonymous ID assignment (AIDA).