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2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection.

Raptis S, Koutsouris D - Int J Biomed Imaging (2010)

Bottom Line: Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT) or region growing.Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions, representing almost 2% of the 2D volume, good speed versus accuracy/time trade-off.Results show the potentials of our approach in terms of time for detection ROC analysis and accuracy of vessel pixel (VP) detection.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., H/Y Building-Zografou Campus, 15773 Athens, Greece.

ABSTRACT
The paper addresses the fine retinal-vessel's detection issue that is faced in diagnostic applications and aims at assisting in better recognizing fine vessel anomalies in 2D. Our innovation relies in separating key visual features vessels exhibit in order to make the diagnosis of eventual retinopathologies easier to detect. This allows focusing on vessel segments which present fine changes detectable at different sampling scales. We advocate that these changes can be addressed as subsequent stages of the same vessel detection procedure. We first carry out an initial estimate of the basic vessel-wall's network, define the main wall-body, and then try to approach the ridges and branches of the vasculature's using fine detection. Fine vessel screening looks into local structural inconsistencies in vessels properties, into noise, or into not expected intensity variations observed inside pre-known vessel-body areas. The vessels are first modelled sufficiently but not precisely by their walls with a tubular model-structure that is the result of an initial segmentation. This provides a chart of likely Vessel Wall Pixels (VWPs) yielding a form of a likelihood vessel map mainly based on gradient filter's intensity and spatial arrangement parameters (e.g., linear consistency). Specific vessel parameters (centerline, width, location, fall-away rate, main orientation) are post-computed by convolving the image with a set of pre-tuned spatial filters called Matched Filters (MFs). These are easily computed as Gaussian-like 2D forms that use a limited range sub-optimal parameters adjusted to the dominant vessel characteristics obtained by Spatial Grey Level Difference statistics limiting the range of search into vessel widths of 16, 32, and 64 pixels. Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT) or region growing. Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions, representing almost 2% of the 2D volume, good speed versus accuracy/time trade-off. Results show the potentials of our approach in terms of time for detection ROC analysis and accuracy of vessel pixel (VP) detection.

No MeSH data available.


Related in: MedlinePlus

(a–f) Indicative cross section profiles encountered in 1D cross the line of CL: (a) mixed pattern that can be modelled both as double peaked and single peaked GS-MF for different peak values and spreads, (b,e) clear single peaked patterns, (c,f) single sided patterns modelling wall transition, (d) very shallow and narrow width vessel for  pixel width.
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fig6: (a–f) Indicative cross section profiles encountered in 1D cross the line of CL: (a) mixed pattern that can be modelled both as double peaked and single peaked GS-MF for different peak values and spreads, (b,e) clear single peaked patterns, (c,f) single sided patterns modelling wall transition, (d) very shallow and narrow width vessel for pixel width.

Mentions: In an effort to capture even fainter details, we tested our maps using a more complex and computationally expensive dual-mode [two-peaked], double-sided GS-MF that is not used in [10] which models background/foreground transition pattern (i.e., background/foreground, CL area, and foreground/background), on both vessel walls. Also, the spatial dependence can accommodate zero crossings. These results are given in Figures 1(c) and 1(h) and show more vessel pixels than the results with single mode GS-MF as in Figures 1(c) and 1(g). Some single (Figures 6(b) and 6(e)) or partial double peaked (Figures 6(a), 6(c), 6(d), and 6(f)) are also shown where and can be modeled using spatially modulated kernels in 2D. (Figure 5(b))* show some examples of the dual model kernels for a sampled range of spreads and orientations. These can sufficiently emulate and at low cost the simple wall model individually on either vessel side as a single-sloped wall pattern [dark/light]. The double-sided single mode kernel model is less robust and often cannot capture much information as it assumes a more uniform background that it really is.


2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection.

Raptis S, Koutsouris D - Int J Biomed Imaging (2010)

(a–f) Indicative cross section profiles encountered in 1D cross the line of CL: (a) mixed pattern that can be modelled both as double peaked and single peaked GS-MF for different peak values and spreads, (b,e) clear single peaked patterns, (c,f) single sided patterns modelling wall transition, (d) very shallow and narrow width vessel for  pixel width.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2913788&req=5

fig6: (a–f) Indicative cross section profiles encountered in 1D cross the line of CL: (a) mixed pattern that can be modelled both as double peaked and single peaked GS-MF for different peak values and spreads, (b,e) clear single peaked patterns, (c,f) single sided patterns modelling wall transition, (d) very shallow and narrow width vessel for pixel width.
Mentions: In an effort to capture even fainter details, we tested our maps using a more complex and computationally expensive dual-mode [two-peaked], double-sided GS-MF that is not used in [10] which models background/foreground transition pattern (i.e., background/foreground, CL area, and foreground/background), on both vessel walls. Also, the spatial dependence can accommodate zero crossings. These results are given in Figures 1(c) and 1(h) and show more vessel pixels than the results with single mode GS-MF as in Figures 1(c) and 1(g). Some single (Figures 6(b) and 6(e)) or partial double peaked (Figures 6(a), 6(c), 6(d), and 6(f)) are also shown where and can be modeled using spatially modulated kernels in 2D. (Figure 5(b))* show some examples of the dual model kernels for a sampled range of spreads and orientations. These can sufficiently emulate and at low cost the simple wall model individually on either vessel side as a single-sloped wall pattern [dark/light]. The double-sided single mode kernel model is less robust and often cannot capture much information as it assumes a more uniform background that it really is.

Bottom Line: Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT) or region growing.Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions, representing almost 2% of the 2D volume, good speed versus accuracy/time trade-off.Results show the potentials of our approach in terms of time for detection ROC analysis and accuracy of vessel pixel (VP) detection.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., H/Y Building-Zografou Campus, 15773 Athens, Greece.

ABSTRACT
The paper addresses the fine retinal-vessel's detection issue that is faced in diagnostic applications and aims at assisting in better recognizing fine vessel anomalies in 2D. Our innovation relies in separating key visual features vessels exhibit in order to make the diagnosis of eventual retinopathologies easier to detect. This allows focusing on vessel segments which present fine changes detectable at different sampling scales. We advocate that these changes can be addressed as subsequent stages of the same vessel detection procedure. We first carry out an initial estimate of the basic vessel-wall's network, define the main wall-body, and then try to approach the ridges and branches of the vasculature's using fine detection. Fine vessel screening looks into local structural inconsistencies in vessels properties, into noise, or into not expected intensity variations observed inside pre-known vessel-body areas. The vessels are first modelled sufficiently but not precisely by their walls with a tubular model-structure that is the result of an initial segmentation. This provides a chart of likely Vessel Wall Pixels (VWPs) yielding a form of a likelihood vessel map mainly based on gradient filter's intensity and spatial arrangement parameters (e.g., linear consistency). Specific vessel parameters (centerline, width, location, fall-away rate, main orientation) are post-computed by convolving the image with a set of pre-tuned spatial filters called Matched Filters (MFs). These are easily computed as Gaussian-like 2D forms that use a limited range sub-optimal parameters adjusted to the dominant vessel characteristics obtained by Spatial Grey Level Difference statistics limiting the range of search into vessel widths of 16, 32, and 64 pixels. Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT) or region growing. Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions, representing almost 2% of the 2D volume, good speed versus accuracy/time trade-off. Results show the potentials of our approach in terms of time for detection ROC analysis and accuracy of vessel pixel (VP) detection.

No MeSH data available.


Related in: MedlinePlus