Half-unit weighted bilinear algorithm for image contrast enhancement in capsule endoscopy

Computational and Mathematical Methods in Medicine

Pedersen

Hovde

2017

Self Cite

This paper proposes an advanced method for contrast enhancement of capsule endoscopic images, with the main objective to obtain sufficient information about the vessels and structures in more distant (or darker) parts of capsule endoscopic images. The proposed method (PM) combines two algorithms for the enhancement of darker and brighter areas of capsule endoscopic images, respectively. The half-unit weighted-bilinear algorithm (HWB) proposed in our previous work is used to enhance darker areas according to the darker map content of its HSV's component V. Enhancement of brighter areas is achieved thanks to the novel threshold weighted-bilinear algorithm (TWB) developed to avoid overexposure and enlargement of specular highlight spots while preserving the hue, in such areas. The TWB performs enhancement operations following a gradual increment of the brightness of the brighter map content of its HSV's component V. In other words, the TWB decreases its averaged weights as the intensity content of the component V increases. Extensive experimental demonstrations were conducted, and, based on evaluation of the reference and PM enhanced images, a gastroenterologist (Ø.H.) concluded that the PM enhanced images were the best ones based on the information about the vessels, contrast in the images, and the view or visibility of the structures in more distant parts of the capsule endoscopy images.

…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Equation ( 2 ) is the result of applying a constant half-unit to weights CW n in ( 1 ):

Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advanced Image Enhancement Method for Distant Vessels and Structures in Capsule Endoscopy

Computational and Mathematical Methods in Medicine

Pedersen

Hovde

2017

Self Cite

“…It is based on performing interpolation in two directions. As can be seen, in Figure 1's example, the bilinear interpolation algorithm uses four nearest pixels or samples in Equation 1 to estimate or approximate the missing pixel value at ( , ) P x y , [11], [12].…”

Section: Bilinear Interpolation and Rescale Functionmentioning

confidence: 99%

Effects of rescaling bilinear interpolant on image interpolation quality

Optoelectronic Imaging and Multimedia Technology V

Schmidt

2018

Self Cite

Rescaling bilinear (RB) interpolant's pixels is a novel image interpolation scheme. In the current study, we investigate the effects on the quality of interpolated images. RB determines the lower and upper bounds using the standard deviation of the four nearest pixels to find the new interval or range that will be used to rescale the bilinear interpolant's pixels. The products of the rescaled-pixels and corresponding distance-based-weights are added to estimate the new pixel value, to be assigned at the empty locations of the destination image. Effects of RB on image interpolation quality were investigated using standard full-reference and non-reference objective image quality metrics, particularly those focusing on interpolated images features and distortion similarities. Furthermore, variance and mean based metrics were also employed to further investigate the effects in terms of contrast and intensity increment or decrement. The Matlab based simulations demonstrated generally superior performances of RB compared to the traditional bilinear (TB) interpolation algorithm. The studied scheme's major drawback was a higher processing time and tendency to rely on the image type and/or specific interpolation scaling ratio to achieve superior performances. Potential applications of rescaling based bilinear interpolation may also include ultrasound scan conversion in cardiac ultrasound, endoscopic ultrasound, etc.

“…In image interpolation, the need for quality assessment has become greater due to easy degradation of image interpolation quality and wide use of interpolation algorithms in many engineering areas/fields [7], [8], [9], [10]. Current methods for image quality assessment include subjective image quality assessment (which requires human judges or observers) and objective image quality assessment (which does not require human judges or observers) [23].…”

Section: Introductionmentioning

confidence: 99%

Aliasing artefact index for image interpolation quality assessment

Optoelectronic Imaging and Multimedia Technology V

Schmidt

2018

Self Cite

A preliminary study of a non-reference aliasing artefact index (AAI) metric is presented in this paper. We focus on the effects of combining a full-reference metric and interpolation algorithm. The nearest neighbor algorithm (NN) is used as the gold standard against which test-algorithms are judged in terms of aliased structures. The structural similarity index (SSIM) metric is used to evaluate a test image (i.e. a test-algorithm's image) and a reference image (i.e. the NN's image). Preliminary experiments demonstrated promising effects of the AAI metric against state-of-the-art non-reference metrics mentioned. A new study may further develop the studied metric for potential applications in image quality adaptation and/or monitoring in medical imaging.