Semantic Analysis of 3D Anatomical Medical Images for Sub-image Retrieval

Venkatraghavan, Vikram; Ranjan, Sohan

doi:10.1007/978-3-642-28460-1_13

Cited by 2 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GLCMs were also used with gradient features in Kovalev et al (1999Kovalev et al ( , 2001Kovalev et al ( , 2003a, Antel et al (2003), Kovalev and Kruggel (2007), Petrou (2009), Nunzio et al (2011), Kocinski et al (2012), and Suoranta et al (2013) as well as 3-D Gabor filters in Madabhushi et al (2003) and windowed Fourier in Kontos et al (2009b). Venkatraghavan and Ranjan (2012) Most of the combination rules consisted of feature concatenation along with feature selection or reduction (e.g., correlationbased feature selection, principal component analysis). Multiple classifier systems are used in Madabhushi et al (2003).…”

Section: Combination Of Texture Featuresmentioning

confidence: 99%

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

203

146

View full text Add to dashboard Cite

a b s t r a c tThree-dimensional computerized characterization of biomedical solid textures is key to large-scale and high-throughput screening of imaging data. Such data increasingly become available in the clinical and research environments with an ever increasing spatial resolution.In this text we exhaustively analyze the state-of-the-art in 3-D biomedical texture analysis to identify the specific needs of the application domains and extract promising trends in image processing algorithms. The geometrical properties of biomedical textures are studied both in their natural space and on digitized lattices. It is found that most of the tissue types have strong multi-scale directional properties, that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. The information modeled by the various image processing techniques is analyzed and visualized by displaying their 3-D texture primitives. We demonstrate that non-convolutional approaches are expected to provide best results when the size of structures are inferior to five voxels. For larger structures, it is shown that only multi-scale directional convolutional approaches that are non-separable allow for an unbiased modeling of 3-D biomedical textures. With the increase of high-resolution isotropic imaging protocols in clinical routine and research, these models are expected to best leverage the wealth of 3-D biomedical texture analysis in the future. Future research directions and opportunities are proposed to efficiently model personalized image-based phenotypes of normal biomedical tissue and its alterations. The integration of the clinical and genomic context is expected to better explain the intra class variation of healthy biomedical textures. Using texture synthesis, this provides the exciting opportunity to simulate and visualize texture atlases of normal ageing process and disease progression for enhanced treatment planning and clinical care management.

show abstract

Section: Combination Of Texture Featuresmentioning

confidence: 99%

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

203

146

View full text Add to dashboard Cite

show abstract

“…Our work relates to image classification techniques that attempt to separate and label different structures based on automatically derived image features from CT and PET-CT data [8][9][10]. Venkatraghavan et al [8] used Gabor filtered CT slices with speeded up image feature extraction (SURF -a fast implementation of scale-invariant feature transform (SIFT)) for use in organ localisation.…”

Section: A Related Workmentioning

confidence: 99%

“…Venkatraghavan et al [8] used Gabor filtered CT slices with speeded up image feature extraction (SURF -a fast implementation of scale-invariant feature transform (SIFT)) for use in organ localisation. However, this investigation only considered 'healthy' structures thereby bypassing the complexity from abnormal structures.…”

Section: A Related Workmentioning

confidence: 99%

“…Our method is based on region-level statistics and spatial features (RSS) and histogram of oriented gradients (HOG) features [7] extracted from PET, CT and combined data type PET-CT. Our method differs from traditional methods [8][9][10] in that it uses all the available features and there is an automated 'selective' use of the image features.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of thresholded regions based on selective use of PET, CT and PET-CT image features

Kim

Feng

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is the preferred image modality for lymphoma diagnosis. Sites of disease generally appear as foci of increased FDG uptake. Thresholding methods are often applied to robustly separate these regions. However, its main limitation is that it also includes sites of FDG excretion and physiological FDG uptake regions, which we define as FEPU - sites of FEPU include the bladder, renal, papillae, ureters, brain, heart and brown fat. FEPU can make image interpretation problematic. The ability to identify and label FEPU sites and separate them from abnormal regions is an important process that could improve image interpretation. We propose a new method to automatically separate and label FEPU sites from the thresholded PET images. Our method is based on the selective use of features extracted from data types comprising of PET, CT and PET-CT. Our FEPU classification of 43 clinical lymphoma patient studies revealed higher accuracy when compared to non-selective image features.

show abstract

Semantic Analysis of 3D Anatomical Medical Images for Sub-image Retrieval

Cited by 2 publications

References 16 publications

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Classification of thresholded regions based on selective use of PET, CT and PET-CT image features

Contact Info

Product

Resources

About