scholarly journals Overhaul of Three-D Image Based on Metamorphosis and Fabric Growing of Two-D Images

2020 ◽  
Vol 9 (3) ◽  
pp. 3466-3471
Keyword(s):  
Shape Analysis ◽  
Medical Image ◽  
Three Dimensional ◽  
Medical Image Processing ◽  
Physical Property ◽  
Initial Step ◽  
Practical Result ◽  
Normal Image ◽  
Segmented Image ◽  
Three Dimensional Models

Shape is a critical physical property of normal and artificial three-D images that describes their outside appearances. Understanding differences among shapes and displaying the inconstancy inside and outside the shape classes are considered for shape analysis, and are the major issues in numerous applications, from normal image visualization to medical imaging. During diagnosis in medical image processing it is impossible to analyze the diseased areas some time from three-D images. So for the purpose of diagnosing the diseased areas of three-D image, medical experts need two-D images. This paper addresses the overhaul of three dimensional models from two-D images. In the initial step the image is segmented using level set method. Later segmented image is extracted and registered for overhaul of three dimensional images using metamorphosis and fabric growing methods. The practical result shows the implementation of the suggested method.

The Surgical Workstation: Surgical Planning Using Generic Software

1993 ◽  
Vol 109 (3) ◽  
pp. 434-440 ◽  
Author(s):  
David A. Wiegand ◽  
Robert B. Page ◽  
David S. Channin
Keyword(s):  
Medical Image ◽  
Surgical Planning ◽  
Medical Image Analysis ◽  
Three Dimensional ◽  
Computer Software ◽  
Medical Image Processing ◽  
Imaging Systems ◽  
Computed Tomography Scans ◽  
Further Development ◽  
Head Neck

Computer software for rendering and display of three-dimensional data is becoming readily available for all types of computers. Such programs typically accept data from any source, compute a three-dimensional volume of data, and display it with a variety of rendering options. Although not specifically designed for medical image processing, these programs can provide very detailed and finely rendered images that are useful for surgical planning. We use one such program to display data from standard computed tomography scans, which gives us a photorealistic three-dimensional view of patient anatomy. This view can be modified to render tissues transparent, translucent, or opaque, and thus allows the surgeon to selectively enhance bony architecture, tumors, or other details. Images can be rotated, sliced, and displayed in the surgical position. Image animation can be added to facilitate the display of complex anatomic relationships. Our experience with this technology suggests that such programs can provide the basis for personal surgical workstations for medical image analysis and surgical planning. Further development of such generic imaging systems should allow this useful technology to become widely available for surgical planning and education. We discuss our experience with a typical generic imaging workstation. (OTOLARYNGOL HEAD NECK SURG 1993;109:434-40.)

scholarly journals IMPLEMENTATION METHOD ON MEDICAL IMAGE COMPRESSION SYSTEM: A REVIEW

2017 ◽  
Vol 79 (7) ◽  
Author(s):  
Azlan Muharam ◽  
Afandi Ahmad
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Medical Image ◽  
Rapid Development ◽  
Three Dimensional ◽  
Medical Image Processing ◽  
Subjective Test ◽  
Medical Image Compression ◽  
Research Issues ◽  
Software And Hardware

The rapid development of medical imaging and the invention of various medicines have benefited mankind and the whole community. Medical image processing is a niche area concerned with the operations and processes of generating images of the human body for clinical purposes.  Potential areas such as image acquisition, image enhancement, image compression and storage, and image based visualization also include in medical image processing analysis. Unfortunately, medical image compression dealing with three-dimensional (3-D) modalities still in the pre-matured stage. Along with that, very limited researchers take a challenge to apply hardware on their implementation. Referring to the previous work reviewed, most of the compression method used lossless rather than lossy. For implementation using software, MATLAB and Verilog are the famous candidates among researchers. In term of analysis, most of the previous works conducted objective test compared with subjective test. This paper thoroughly reviews the recent advances in medical image compression mainly in terms of types of compression, software and hardware implementations and performance evaluation. Furthermore, challenges and open research issues are discussed in order to provide perspectives for future potential research. In conclusion, the overall picture of the image processing landscape, where several researchers more focused on software implementations and various combinations of software and hardware implementation.  

scholarly journals An advanced shape-fitting algorithm applied to quadrupedal mammals: improving volumetric mass estimates

2015 ◽  
Vol 2 (8) ◽  
pp. 150302 ◽  
Author(s):  
Charlotte A. Brassey ◽  
James D. Gardiner
Keyword(s):  
Body Mass ◽  
Three Dimensional ◽  
Physical Property ◽  
Skeletal Structure ◽  
Shape Fitting ◽  
Alpha Shapes ◽  
Fundamental Physical Property ◽  
Three Dimensional Models ◽  
The Individual ◽  
Ground Sloth

Body mass is a fundamental physical property of an individual and has enormous bearing upon ecology and physiology. Generating reliable estimates for body mass is therefore a necessary step in many palaeontological studies. Whilst early reconstructions of mass in extinct species relied upon isolated skeletal elements, volumetric techniques are increasingly applied to fossils when skeletal completeness allows. We apply a new ‘alpha shapes’ ( α -shapes) algorithm to volumetric mass estimation in quadrupedal mammals. α -shapes are defined by: (i) the underlying skeletal structure to which they are fitted; and (ii) the value α , determining the refinement of fit. For a given skeleton, a range of α -shapes may be fitted around the individual, spanning from very coarse to very fine. We fit α -shapes to three-dimensional models of extant mammals and calculate volumes, which are regressed against mass to generate predictive equations. Our optimal model is characterized by a high correlation coefficient and mean square error ( r 2 =0.975, m.s.e.=0.025). When applied to the woolly mammoth ( Mammuthus primigenius ) and giant ground sloth ( Megatherium americanum ), we reconstruct masses of 3635 and 3706 kg, respectively. We consider α -shapes an improvement upon previous techniques as resulting volumes are less sensitive to uncertainties in skeletal reconstructions, and do not require manual separation of body segments from skeletons.

3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

2011 ◽  
pp. 885-894
Author(s):  
Guang Li ◽  
Deborah Citrin ◽  
Robert W. Miller ◽  
Kevin Camphausen ◽  
Boris Mueller ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Registration ◽  
Medical Image ◽  
Medical Information ◽  
Three Dimensional ◽  
Medical Image Processing ◽  
Emerging Technology ◽  
Time Image ◽  
Over Time

Image registration, segmentation, and visualization are three major components of medical image processing. Three-dimensional (3D) digital medical images are three dimensionally reconstructed, often with minor artifacts, and with limited spatial resolution and gray scale, unlike common digital pictures. Because of these limitations, image filtering is often performed before the images are viewed and further processed (Behrenbruch, Petroudi, Bond, et al., 2004). Different 3D imaging modalities usually provide complementary medical information about patient anatomy or physiology. Four-dimensional (4D) medical imaging is an emerging technology that aims to represent patient motions over time. Image registration has become increasingly important in combining these 3D/4D images and providing comprehensive patient information for radiological diagnosis and treatment.

3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

2008 ◽  
pp. 1-9 ◽  
Author(s):  
Guang Li ◽  
Deborah Citrin ◽  
Robert W. Miller ◽  
Kevin Camphausen ◽  
Boris Mueller ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Registration ◽  
Medical Image ◽  
Medical Information ◽  
Three Dimensional ◽  
Medical Image Processing ◽  
Emerging Technology ◽  
Time Image ◽  
Over Time

Image registration, segmentation, and visualization are three major components of medical image processing. Three-dimensional (3D) digital medical images are three dimensionally reconstructed, often with minor artifacts, and with limited spatial resolution and gray scale, unlike common digital pictures. Because of these limitations, image filtering is often performed before the images are viewed and further processed (Behrenbruch, Petroudi, Bond, et al., 2004). Different 3D imaging modalities usually provide complementary medical information about patient anatomy or physiology. Four-dimensional (4D) medical imaging is an emerging technology that aims to represent patient motions over time. Image registration has become increasingly important in combining these 3D/4D images and providing comprehensive patient information for radiological diagnosis and treatment.

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