Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: The rapid development of communication technology due to the global spread of the Internet and the digital information revolution has given rise to a huge increase in the use and transmission of multimedia information (images, audio, and video). As a result, information security during storage and transmission has become a critical issue. For example, images are widely used in industrial processes. These images could contain private information, so they must be protected.Digital image scrambling, often used for image encryption and data hiding, reorders and changes the position of image pixels to break the relationship between adjacent pixels. 1 These methods include Advanced Encryption Standard, 2 Twice Interval Division, 3 Cat Chaotic Mapping, 4 Magic Cube, 5 and Arnold Transformation. 6 We propose a new scrambling method based on a 2D cellular automaton (CA).The ability to obtain complex global behavior from simple local rules makes CA an interesting platform for digital image scrambling. The most widely known example, the Game of Life (GL), is a 2D CA that produces large amounts of patterned data. The GL (which was designed by John Conway) can scramble the digital image by providing the complex behavior that would produce the most useful operationsIn this work, we analyze the GL's complex characteristics using digital image scrambling to decide whether the degree of scrambling is influenced by different GL configurations (such as the number of generations and boundary conditions). We also design various sets of 2D CA rules, variations on the GL rules, with different Lambda parameters around the critical value of Lambda. Our resulting model is simple and robust, and our tests show that the scrambling effects are good. Cellular AutomataCA are widely used in applications such as art (generated images and music), random number generation, pattern recognition, routing algorithms, and games. The application of CA in the area of digital image processing includes image enhancement, compression, encryption, and watermarking. 8 CA are dynamic, complex space and time discrete systems originally proposed by Stanislaw Ulam and John von Neumann in the 1940s as formal models for self-reproducing organisms. 7 They consist of a certain number of identical cells, each of which can take a finite number of states. The cells are distributed in space in a rectangular grid in one or more dimensions. At every time step, all the cells update their states synchronously by applying rules (transition function), which take as input the state of the cell under consideration and the states of its neighboring cells. The various CA models differ in the number of dimensions, the number of possible states, the neighborhood relationship, and the state update rules.In spite of their simple construction, CA can produce complex behavior and generate useful operations. Stephen Wolfram classified 1D CA into four broad categories: clas...

BACKGROUND AND PURPOSE: Cone-beam CT is being increasingly used in head and neck imaging. We compared cone-beam CT with multidetector CT to assess postoperative implant placement and delineate finer anatomic structures, image quality, and radiation dose used. MATERIALS AND METHODS:This retrospective multicenter study included 51 patients with cochlear implants and postoperative imaging via temporal bone cone-beam CT (n ¼ 32 ears) or multidetector CT (n ¼ 19 ears) between 2012 and 2017. We evaluated the visualization quality of single electrode contacts, the scalar position of the electrodes, cochlear walls, mastoid facial canal, metallic artifacts (using a 4level visual score), and the ability to measure the insertion angle of the electrodes. The signal-to-noise ratio and radiation dose were also evaluated.RESULTS: Cone-beam CT was more sensitive for visualizing the scalar position of the electrodes (P ¼ .046), cochlear outer wall (P ¼ .001), single electrode contacts (P , .001), and osseous spiral lamina (P ¼ .004) and had fewer metallic artifacts (P , .001). However, there were no significant differences between both methods in visualization of the modiolus (P ¼ .37), cochlear inner wall (P . .99), and mastoid facial canal wall (P ¼ .07) and the ability to measure the insertion angle of the electrodes (P . .99). The conebeam CT group had significantly lower dose-length product (P , .001), but multidetector CT showed a higher signal-to-noise ratio in both bone and air (P ¼ .22 and P ¼ .001).CONCLUSIONS: Cone-beam CT in patients with cochlear implants provides images with higher spatial resolution and fewer metallic artifacts than multidetector CT at a relatively lower radiation dose.

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Background The postoperative imaging assessment of Cochlear Implant (CI) patients is imperative. The main obstacle is that Magnetic Resonance imaging (MR) is contraindicated or hindered by significant artefacts in most cases with CIs. This study describes an automatic cochlear image registration and fusion method that aims to help radiologists and surgeons to process pre-and postoperative 3D multimodal imaging studies in cochlear implant (CI) patients. Methods and findings We propose a new registration method, Automatic Cochlea Image Registration (ACIR-v3), which uses a stochastic quasi-Newton optimiser with a mutual information metric to find 3D rigid transform parameters for registration of preoperative and postoperative CI imaging. The method was tested against a clinical cochlear imaging dataset that contains 131 multimodal 3D imaging studies of 41 CI patients with preoperative and postoperative images. The preoperative images were MR, Multidetector Computed Tomography (MDCT) or Cone Beam Computed Tomography (CBCT) while the postoperative were CBCT. The average root mean squared error of ACIR-v3 method was 0.41 mm with a standard deviation of 0.39 mm. The results were evaluated quantitatively using the mean squared error of two 3D landmarks located manually by two neuroradiology experts in each image and compared to other previously known registration methods, e.g. Fast Preconditioner Stochastic Gradient Descent, in terms of accuracy and speed. Conclusions Our method, ACIR-v3, produces high resolution images in the postoperative stage and allows for visualisation of the accurate anatomical details of the MRI with the absence of significant metallic artefacts. The method is implemented as an open-source plugin for 3D Slicer tool.

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