Image Enhancement based Improved Multi-scale Hessian Matrix for Coronary Angiography

Chen, Kaifeng; Yin, Qinye; Xiao, Jia; Lu, Mingyu

doi:10.5120/ijca2015906196

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Chen and his team have implemented a CCA enhancement method based on an improved multi-scale Hessian matrix combined with morphological top-hat operation [15]. In order to calculate Hessian matrix, it is required to obtain the derivative images of the angiography images to be processed.…”

Section: E Visual Assessment Of Motion Stabilization Methodsmentioning

confidence: 99%

“…Further, it has the capability of reducing the noise as an added advantage. An improved multi-scale Hessian matrix combined with morphological top-hat operation has been implemented in resent research study to enhance angiography [15]. The main objective of this study was to suppress non-vascular structure and improve the profile of small tiny blood vessels recorded in angiography.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Reduction of Visual Disruptions in Coronary Cine-Angiography for Better Diagnostic Viewing

Kulathilake

Wajiramali

Chathurika

et al. 2018

Int J on Adv. in ICT for Emerging Countries

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Coronary angiography is an invasive medical imaging modality which is widely used as a preliminary diagnostic method in Interventional Cardiology for detection of luminal obstructions in Coronary Arteries (CA). Noise, nonuniform illumination, low contrast and epicardial motion are considered as the reported visual degradations which are automatically recorded in Coronary angiography. Further, those factors form certain unpredictable obstructions during the angiography based disease diagnosis. In this study, it has been proposed a frequency filtering based frame enhancement method for surmounting those problems and improving the visual quality of the angiograms. The proposed method is based on homomorphic butter worth high pass filter and empirically validated contrast stretching technique. Further, the optical flow based frame stabilization method has been applied in the later stages of the proposed method for correcting the epicardial deformations. The results of the study clearly emphasized the visual improvement obtained from this method. The enhanced angiography frames produced in this study can be recommended for reliable stenosis detection. Further, these enhanced frames can be further used for quantitative stenosis assessment.

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Section: E Visual Assessment Of Motion Stabilization Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Reduction of Visual Disruptions in Coronary Cine-Angiography for Better Diagnostic Viewing

Kulathilake

Wajiramali

Chathurika

et al. 2018

Int J on Adv. in ICT for Emerging Countries

View full text Add to dashboard Cite

show abstract

“…Hessian‐matrix can be applied in many fields [36–38]. The Hessian‐matrix of an image is defined as:

\begin{equation} H(x,y,\sigma ) = {\left[ { \def\eqcellsep{&}\begin{array}{*{20}{c}}{L_{xx}}(x,y,\sigma )&\quad {{L_{xy}}(x,y,\sigma )}\\[7pt] {{L_{xy}}(x,y,\sigma )}&\quad {{L_{yy}}(x,y,\sigma )} \end{array} } \right]}, \end{equation}

where

{L_{xx}}(x,y,\sigma )

is the convolution of the Gaussian second order derivative

{{\partial ^2}}/{\partial {x^2}}g(\sigma )

with image I at point

(x,y)

, and similarly for

{L_{xy}}(x,y,\sigma )

and

{L_{yy}}(x,y,\sigma )

.…”

Section: The Proposed Approachmentioning

confidence: 99%

“…Hessian-matrix can be applied in many fields [36][37][38]. The Hessian-matrix of an image is defined as:…”

Section: Multi-scale Fractional-order Hessian Matrixmentioning

confidence: 99%

HessHist: A Hessian‐matrix weighted histogram for image contrast enhancement

Fan

Zong

Tang

et al. 2022

IET Image Processing

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For image contrast enhancement operation, it is a keypoint to obtain more natural enhanced results and keep more details without distortion. In this paper, a novel image Hessian-matrix weighted histogram for image contrast enhancement is proposed, which can improve the contrast of smooth regions and simultaneously restrain the contrast of texture regions. In the proposed method, the multi-scale fractional-order Hessian-matrix is firstly utilized to detect and quantify the texture information of the input image, which explores the regions that should be contrasted or should be restrained. Then, the strong texture regions are suppressed by a designed suppress function. Finally, the information on unsuppressed regions and suppressed texture regions will be count by a histogram, which is termed as Hessian-matrix weighted Histogram (HessHist) in this paper. According to HessHist, the corresponding cumulative distribution function will realize the contrast enhancement operation on the input image. For real-time application, the integral images are introduced for fast computation of the HessHist. Experimental results show that the proposed HessHist-based image enhancement algorithm preserves more details of input image without distortion, and is competitive with state-of-the-art image enhancement algorithms in both subjective visual perception and objective evaluation metrics.

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3D orientation field transform

Yeung,

Cai,

Liang

et al. 2024

Pattern Anal Applic

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Vascular structure enhancement is very useful in image processing and computer vision. The enhancement of the presence of the structures like tubular networks in given images can improve image-dependent diagnostics and can also facilitate tasks like segmentation. The two-dimensional (2D) orientation field transform has been proved to be effective at enhancing 2D contours and curves in images by means of top-down processing. It, however, has no counterpart in 3D images due to the extremely complicated orientation in 3D against 2D. Given the rising demand and interest in handling 3D images, we experiment with modularising the concept and generalise the algorithm to 3D curves. In this work, we propose a 3D orientation field transform. It is a vascular structure enhancement algorithm that can cleanly enhance images having very low signal-to-noise ratio, and push the limits of 3D image quality that can be enhanced computationally. This work also utilises the benefits of modularity and offers several combinative options that each yield moderately better enhancement results in different scenarios. In principle, the proposed 3D orientation field transform can naturally tackle any number of dimensions. As a special case, it is also ideal for 2D images, owning a simpler methodology compared to the previous 2D orientation field transform. The concise structure of the proposed 3D orientation field transform also allows it to be mixed with other enhancement algorithms, and as a preliminary filter to other tasks like segmentation and detection. The effectiveness of the proposed method is demonstrated with synthetic 3D images and real-world transmission electron microscopy tomograms ranging from 2D curve enhancement to, the more important and interesting, 3D ones. Extensive experiments and comparisons with existing related methods also demonstrate the excellent performance of the proposed 3D orientation field transform.

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Image Enhancement based Improved Multi-scale Hessian Matrix for Coronary Angiography

Cited by 8 publications

References 13 publications

Reduction of Visual Disruptions in Coronary Cine-Angiography for Better Diagnostic Viewing

Reduction of Visual Disruptions in Coronary Cine-Angiography for Better Diagnostic Viewing

HessHist: A Hessian‐matrix weighted histogram for image contrast enhancement

3D orientation field transform

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