Edge enhancement

About: Edge enhancement is a research topic. Over the lifetime, 2324 publications have been published within this topic receiving 30962 citations.

Partial Differential Equation-Based Enhancement and Crack Detection

Abstract: This paper presents an effective partial differential equation- (PDE-) based preprocessing algorithm for automated image-based crack detection. The proposed formulation combines various relevant and multiple processes such as contrast and selective edge enhancement in addition to edge-preserving smoothing to enhance the image prior to detection. The approach is adaptive and controlled by reliable image metrics to determine the stopping time of the PDE ensuring optimum results for various images. Additionally, a simplified thresholding algorithm based on local global maximum gradient matching is used to extract the crack features from the image. The proposed scheme does not require arbitrary or manually tuned parameters nor a large dataset for training to obtain good results. Experiments indicate that the proposed approach performs better when compared to several other algorithms in the literature.

Low dose cone-beam computed tomography reconstruction via hybrid prior contour based total variation regularization (hybrid-PCTV).

Abstract: Background Previously, we developed a prior contour based total variation (PCTV) method to use edge information derived from prior images for edge enhancement in low-dose cone-beam computed tomography (CBCT) reconstruction. However, the accuracy of edge enhancement in PCTV is affected by the deformable registration errors and anatomical changes from prior to on-board images. In this study, we develop a hybrid-PCTV method to address this limitation to enhance the robustness and accuracy of the PCTV method. Methods Planning-CT is used as prior images and deformably registered with on-board CBCT reconstructed by the edge preserving TV (EPTV) method. Edges derived from planning CT are deformed based on the registered deformation vector fields to generate on-board edges for edge enhancement in PCTV reconstruction. Reference CBCT is reconstructed from the simulated projections of the deformed planning-CT. Image similarity map is then calculated between reference and on-board CBCT using structural similarity index (SSIM) method to estimate local registration accuracy. The hybrid-PCTV method enhances the edge information based on a weighted edge map that combines edges from both PCTV and EPTV methods. Higher weighting is given to PCTV edges at regions with high registration accuracy and to EPTV edges at regions with low registration accuracy. The hybrid-PCTV method was evaluated using both digital extended-cardiac-torso (XCAT) phantom and lung patient data. In XCAT study, breathing amplitude change, tumor shrinkage and new tumor were simulated from CT to CBCT. In the patient study, both simulated and real projections of lung patients were used for reconstruction. Results were compared with both EPTV and PCTV methods. Results EPTV led to blurring bony structures due to missing edge information, and PCTV led to blurring tumor edges due to inaccurate edge information caused by errors in the deformable registration. In contrast, hybrid-PCTV enhanced edges of both bone and tumor. In XCAT study using 30 half-fan CBCT projections, compared with ground truth, relative errors (REs) were 1.3%, 1.1% and 0.9% and edge cross-correlation were 0.66, 0.68 and 0.71 for EPTV, PCTV and hybrid-PCTV, respectively. Moreover, in the lung patient data, hybrid-PCTV avoided the wrong edge enhancement in the PCTV method while maintaining enhancements of the correct edges. Conclusions Hybrid-PCTV further improved the robustness and accuracy of PCTV by accounting for uncertainties in deformable registration and anatomical changes between prior and onboard images. The accurate edge enhancement in hybrid-PCTV will be valuable for target localization in radiation therapy.

Unsharp masking based on the multiscale gradient planes

Abstract: In this paper, control of emphasis by the multiscale brightness gradient is introduced for unsharp masking. This provides an improved image emphasis method for realizing effective emphasis for any image with superposed Gaussian noise and low-contrast images. Unsharp masking is a method of obtaining an enhanced image by applying a high-pass filter to blurred images so that the obtained high-pass components are superposed on the original image. However, unsharp masking has the deficiency that the noise is also enhanced due to its principle of operation when noise is superposed on the blurred image. In order to alleviate this problem, this paper proposes a characteristic quantity of image edges (edge information) using the amplitude shift between the scales of the multiscale brightness gradient, and applies it to the control of the emphasis of unsharp masking. The proposed edge information takes a positive value on the image edge and the amplitude is proportional to the smoothness and brightness difference of the edges. Since the proposed edge information takes a positive value at the smoothed edges regardless of the variance of the superposed Gaussian noise and the image contrast, it is possible to reduce the effect of noise in the emphasis result by controlling the image emphasis by discriminating positive or negative values. In the experiment, the proposed edge information is introduced into unsharp masking using a fuzzy rule and into third-order unsharp masking. The effectiveness of the proposed edge information is confirmed by image emphasis. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 87(4): 40–54, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10113