Image analysis in medical imaging: recent advances in selected examples

Dougherty, Geoff

doi:10.2349/biij.6.3.e32

Cited by 25 publications

(9 citation statements)

References 69 publications

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“…A proposal to maintain the integrity and authenticity of multi-frame DICOM images using digital signature proposed in [17]. Some advances of medical image analysis with examples are discussed in [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Randomized Key-based GMO-BCS Image Encryption for Securing Medical Image

Singh*¹,

Singhal²,

Nath³

et al. 2019

IJRTE

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An essential security requirement while transmitting and receiving medical images is to maintain confidentiality and authorization of these medical images. This paper contains a proposal of an enhanced lossless image encryption algorithm that provides security to Digital Imaging and Communications in Medicine (DICOM) images by producing a random key with using enhanced group modulo based bit circular shift (GMO-BCS) technique. Random key production is the backbone of this technique to provide robust security of medical images that transfer over a network. In the encryption process, we randomly generate a key for each and every pixel of the DICOM image. Group theory is used in this process to create circular shifting in 8-bit pixel values while the security enhancement employs the random key for encryption. This technique is more suitable for medical image encryption either by direct transmission or multimedia app-based transmission under telemedicine and others

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Section: Literature Reviewmentioning

confidence: 99%

Randomized Key-based GMO-BCS Image Encryption for Securing Medical Image

Singh*¹,

Singhal²,

Nath³

et al. 2019

IJRTE

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“…Nevertheless, it is an important technique in computer vision due to its capability in a range of different applications such as biomedical image analysis [9,16], industrial inspection [28,27] and satellite image analysis [7,37]. Due to the possible complexity and diversity of textures, it is impossible to provide a universal definition of textures [22].…”

Section: Introductionmentioning

confidence: 99%

Texture segmentation using different orientations of GLCM features

Rampun

Strange

Zwiggelaar

2013

Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Application

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This paper describes the development of a new texture based segmentation algorithm which uses a set of features extracted from Grey-Level Co-occurrence Matrices. The proposed method segments different textures based on noise reduced features which are effective texture descriptor. Each of the features is processed including normalisation and noise removal. Principal Component Analysis is used to reduce the dimensionality of the resulting feature space. Gaussian Mixture Modelling is used for the subsequent segmentation and false positive regions are removed using morphology. The evaluation includes a wide range of textures (more than 80 Brodatz textures) and in comparison (both qualitative and quantitative) with state of the art techniques very good segmentation results have been obtained.

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“…[4][5][6] This is used as preliminary assessing and prognosticating tool on bone quality in case of dental implants. [7][8][9] The purpose of this study is to evaluate if edentulous sites had different bone quality compared to sites with teeth. This study is an attempt on digital orthopantomographs (OPGs) using box counting/ tile counting method of FA.…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of bone densities in edentulous and dentulous areas by fractal analysis on panoramic radiographs - A retrospective study

Palla¹,

Rangdhol²,

Jeelani³

et al. 2017

JCRI

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Aim: Aim of the study was to evaluate the difference in bone trabecular pattern in terms of fractal dimensions (FD), in edentulous and dentulous areas on contralateral sides of the same patient's radiograph. Methodology: A retrospective study using box counting method of fractal analysis was performed on pre-processed 97 digital orthopantomographs (OPGs), after subjecting to inclusion and exclusion criteria with the fractal three software. Results: The mean FD of dentulous and edentulous regions was 1.812 ± 0.085 and 1.728±0.105 (P < 0.005), respectively, calculated by paired sample tests. Higher age group (Group 3) had lower FD values than other two groups (P > 0.01). Female sex had lower FD values than males (P > 0.01). Conclusion: The tooth loss (edentulous state) is associated with low FD, compared to tooth bearing areas. The method can be used to as a quick tool, to evaluate and compare the bone quality when prosthetic treatments are to be planned.

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Image analysis in medical imaging: recent advances in selected examples

Cited by 25 publications

References 69 publications

Randomized Key-based GMO-BCS Image Encryption for Securing Medical Image

Randomized Key-based GMO-BCS Image Encryption for Securing Medical Image

Texture segmentation using different orientations of GLCM features

Comparative evaluation of bone densities in edentulous and dentulous areas by fractal analysis on panoramic radiographs - A retrospective study

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