Three-Dimensional Modeling in Medical Image Processing by Using Fractal Geometry

Çalışkan, Abidin

doi:10.17706/jcp.12.5.479-485

Cited by 8 publications

(4 citation statements)

References 12 publications

(17 reference statements)

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“…Several works for 3D reconstruction from 2D images in medical imaging domain [14,7,15,16,17,18,19,20,21,22] exist, but they fail to give good accurate results with internal structure preservation very fast. While most methods suffice for CT images as the information is mostly of hard tissue structure and outlines, but for MRI have lacuna with preservation of the internal tissue structure.…”

Section: Related Workmentioning

confidence: 99%

3D reconstruction of spine image from 2D MRI slices along one axis

et al. 2020

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Magnetic Resonance Imaging (MRI) is a technology for non-invasive imaging of anatomical features in detail. It can help in functional analysis of organs of a specimen but it is very costly. In this work, methods for (i) virtual three-dimensional (3D) reconstruction from a single sequence of twodimensional (2D) slices of MR images of a human spine and brain along a single axis, and (ii) generation of missing inter-slice data are proposed. Our approach helps in preserving the edges, shape, size, as well as the internal tissue structures of the object being captured. The sequence of original 2D slices along a single axis is divided into smaller equal sub-parts which are then reconstructed using edge preserved kriging interpolation to predict the missing slice information. In order to speed up the process of interpolation, we have used multiprocessing by carrying out the initial interpolation on parallel cores. From the 3D matrix thus formed, shearlet transform is applied to estimate the edges considering the 2D blocks along the Z axis, and to minimize the blurring effect using a proposed mean-median logic. Finally, for visualization, the sub-matrices are merged into a final 3D matrix. Next, the newly formed 3D matrix is split up into voxels and marching cubes method is applied to get the approximate 3D image for viewing. To the best of our knowledge it is a first of its kind approach based on kriging interpolation and multiprocessing for 3D reconstruction from 2D slices, and approximately 98.89% accuracy is achieved with respect to similarity metrics for image comparison. The time required for reconstruction has also been reduced by approximately 70% with multiprocessing even for a large input data set compared to that with single core processing.

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Section: Related Workmentioning

confidence: 99%

3D reconstruction of spine image from 2D MRI slices along one axis

et al. 2020

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“…(Without the fractal antenna, mobile phones do not work.) It has been used in image processing and data compression [8,9], as well as in chemical systems and fracture mechanics studies [10,11]. It has also been used in architectural design to understand the intrinsic complexity and aesthetics of patterns and structures [12,13]-the list of its applications continues.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Fractals for Modeling and 3D Printing of Porous Structures

et al. 2021

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Porous structures exhibiting randomly sized and distributed pores are required in biomedical applications (producing implants), materials science (developing cermet-based materials with desired properties), engineering applications (objects having controlled mass and energy transfer properties), and smart agriculture (devices for soilless cultivation). In most cases, a scaffold-based method is used to design porous structures. This approach fails to produce randomly sized and distributed pores, which is a pressing need as far as the aforementioned application areas are concerned. Thus, more effective porous structure design methods are required. This article presents how to utilize fractal geometry to model porous structures and then print them using 3D printing technology. A mathematical procedure was developed to create stochastic point clouds using the affine maps of a predefined Iterative Function Systems (IFS)-based fractal. In addition, a method is developed to modify a given IFS fractal-generated point cloud. The modification process controls the self-similarity levels of the fractal and ultimately results in a model of porous structure exhibiting randomly sized and distributed pores. The model can be transformed into a 3D Computer-Aided Design (CAD) model using voxel-based modeling or other means for digitization and 3D printing. The efficacy of the proposed method is demonstrated by transforming the Sierpinski Carpet (an IFS-based fractal) into 3D-printed porous structures with randomly sized and distributed pores. Other IFS-based fractals than the Sierpinski Carpet can be used to model and fabricate porous structures effectively. This issue remains open for further research.

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“…Photometric stereo [1] is a classical computer vision technique for the 3D reconstruction of a surface. Çalışkan and Çevik [2] obtained 3D images from the 2D CT slices by using fractal geometry. Comlekciler et al [3, 4] presented a novel algorithm based on the binocular stereo vision to reduce the measurement error encountered frequently in the orthognathic surgery.…”

Section: Introductionmentioning

confidence: 99%