Color Enhancement in Endoscopic Images Using Adaptive Sigmoid Function and Space Variant Color Reproduction

Computer vision-based solutions to overcome the limitations of wireless capsule endoscopy

Endoscopic investigation plays a critical role in the diagnosis of gastrointestinal (GI) diseases. Since 2001, Wireless Capsule Endoscopy (WCE) has been available for small bowel exploration and is in continuous development. Over the last decade, WCE has achieved impressive improvements in areas such as miniaturisation, image quality and battery life. As a result, WCE is currently a very useful alternative to wired enteroscopy in the investigation of various small bowel abnormalities and has the potential to become the leading screening technique for the entire gastrointestinal tract. However, commercial solutions still have several limitations, namely incomplete examination and limited diagnostic capacity. These deficiencies are related to technical issues, such as image quality, motion estimation and power consumption management. Computational methods, based on image processing and analysis, can help to overcome these challenges and reduce both the time required by reviewers and human interpretation errors. Research groups have proposed a series of methods including algorithms for locating the capsule or lesion, assessing intestinal motility and improving image quality.In this work, we provide a critical review of computational vision-based methods for WCE image analysis aimed at overcoming the technological challenges of capsules. This article also reviews several representative public datasets used to evaluate the performance of WCE techniques and methods. Finally, some promising solutions of computational methods based on the analysis of multiple-camera endoscopic images are presented.

A Method for Local Contrast Enhancement of Endoscopic Images Based on Color Tensor Transformation into a Matrix of Color Vectors’ Modules Using a Sliding Window

A new method aimed at endoscopic color images’ local contrast enhancement is proposed, based on local sliding histogram equalization with adaptive threshold limitation, color distortions correction, and image brightness preservation. For this, the original RGB image, represented as a tensor of size M × N × 3, is transformed into a matrix of size M × N, composed by the color vectors’ modules. As a result of local contrast enhancement, the obtained color vectors are symmetrical in respect of the input ones, because they satisfy the requirement for invariance after rotation. To enhance the local contrast, recursive local histogram equalization with adaptive thresholding is applied to each matrix element. This threshold divides the histogram into two regions of equal areas. A new metric for local contrast enhancement evaluation based on the mean square difference entropy is proposed. The presented new method is characterized by low computational complexity, due to the lack of direct and inverse color conversion and the possibility for adaptive local contrast enhancement, which is essential for accurate medical diagnosis based on endoscopic images analysis. In addition, the presented method performs both the correction of color distortions and the brightness preservation of each pixel.

Endoscopic image luminance enhancement based on the inverse square law for illuminance and retinex

BACKGROUND

In a single-port robotic system where the 3D endoscope possesses two bending segments, only point light sources can be integrated at the tip due to space limitations. However, point light sources usually provide non-uniform illumination, causing the endoscopic images to appear bright in the centre and dark near the corners.

METHODS

Based on the inverse square law for illuminance, an initial luminance weighting is first proposed to increase the image luminance uniformity. Then, a saturation-based model is proposed to finalise the luminance weighting to avoid overexposure and colour discrepancy, while the single-scale retinex (SSR) scheme is employed for noise control.

RESULTS

Via qualitative and quantitative comparisons, the proposed method performs effectively in enhancing the luminance and uniformity of endoscopic images, in terms of both visual perception and objective assessment.

CONCLUSIONS

The proposed method can effectively reduce the image degradation caused by point light sources.

A q-Extension of Sigmoid Functions and the Application for Enhancement of Ultrasound Images

This paper proposes the q-sigmoid functions, which are variations of the sigmoid expressions and an analysis of their application to the process of enhancing regions of interest in digital images. These new functions are based on the non-extensive Tsallis statistics, arising in the field of statistical mechanics through the use of q-exponential functions. The potential of q-sigmoids for image processing is demonstrated in tasks of region enhancement in ultrasound images which are highly affected by speckle noise. Before demonstrating the results in real images, we study the asymptotic behavior of these functions and the effect of the obtained expressions when processing synthetic images. In both experiments, the q-sigmoids overcame the original sigmoid functions, as well as two other well-known methods for the enhancement of regions of interest: slicing and histogram equalization. These results show that q-sigmoids can be used as a preprocessing step in pipelines including segmentation as demonstrated for the Otsu algorithm and deep learning approaches for further feature extractions and analyses.

Evaluation of Reconstruction Parameters for 2-D Comb-Push Ultrasound Shear Wave Elastography

Shear wave elastography (SWE) is a noninvasive ultrasound imaging modality used in the assessment of the mechanical properties of tissues such as the liver, kidney, skeletal muscle, thyroid, and the breast. Among the methods used to perform SWE is the comb-push ultrasound shear elastography method. This method uses multiple focused ultrasound beams to generate push beams with acoustic radiation force. Applying these push beams generates propagating shear waves. The propagation motion is measured with ultrafast ultrasound imaging. The shear wave motion data are directionally filtered, and a 2-D shear wave velocity (SWV) algorithm is applied to create group velocity maps. This algorithm uses a moving window and a specified patch for performing cross-correlations of time-domain signals. We performed a parametric study of how the choice of the patch and window size affected the reconstruction of the SWV in homogeneous and inclusion phantoms. We quantified the mean velocity and coefficient of variation in the homogeneous phantoms. We measured the contrast-to-noise ratio and bias in the inclusion phantoms. In each of these cases, we found that particular combinations of the patch and window provided optimal values of these evaluation metrics for the phantoms tested. This study provides a basis to construct algorithms to produce optimal SWV reconstructions for various clinical applications.

Automated pterygium detection method of anterior segment photographed images

BACKGROUND AND BJECTIVE

Pterygium is an ocular disease caused by fibrovascular tissue encroachment onto the corneal region. The tissue may cause vision blurring if it grows into the pupil region. In this study, we propose an automatic detection method to differentiate pterygium from non-pterygium (normal) cases on the basis of frontal eye photographed images, also known as anterior segment photographed images.

METHODS

The pterygium screening system was tested on two normal eye databases (UBIRIS and MILES) and two pterygium databases (Australia Pterygium and Brazil Pterygium). This system comprises four modules: (i) a preprocessing module to enhance the pterygium tissue using HSV-Sigmoid; (ii) a segmentation module to differentiate the corneal region and the pterygium tissue; (iii) a feature extraction module to extract corneal features using circularity ratio, Haralick's circularity, eccentricity, and solidity; and (iv) a classification module to identify the presence or absence of pterygium. System performance was evaluated using support vector machine (SVM) and artificial neural network.

RESULTS

The three-step frame differencing technique was introduced in the corneal segmentation module. The output image successfully covered the region of interest with an average accuracy of 0.9127. The performance of the proposed system using SVM provided the most promising results of 88.7%, 88.3%, and 95.6% for sensitivity, specificity, and area under the curve, respectively.

CONCLUSION

A basic platform for computer-aided pterygium screening was successfully developed using the proposed modules. The proposed system can classify pterygium and non-pterygium cases reasonably well. In our future work, a standard grading system will be developed to identify the severity of pterygium cases. This system is expected to increase the awareness of communities in rural areas on pterygium.

CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

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

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.

Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function

Wireless capsule endoscopy (WCE) plays an important role in the diagnosis of gastrointestinal (GI) diseases by capturing images of human small intestine. Accurate diagnosis of endoscopic images depends heavily on the quality of captured images. Along with image and frame rate, brightness of the image is an important parameter that influences the image quality which leads to the design of an efficient illumination system. Such design involves the choice and placement of proper light source and its ability to illuminate GI surface with proper brightness. Light emitting diodes (LEDs) are normally used as sources where modulated pulses are used to control LED's brightness. In practice, instances like under- and over-illumination are very common in WCE, where the former provides dark images and the later provides bright images with high power consumption. In this paper, we propose a low-power and efficient illumination system that is based on an automated brightness algorithm. The scheme is adaptive in nature, i.e., the brightness level is controlled automatically in real-time while the images are being captured. The captured images are segmented into four equal regions and the brightness level of each region is calculated. Then an adaptive sigmoid function is used to find the optimized brightness level and accordingly a new value of duty cycle of the modulated pulse is generated to capture future images. The algorithm is fully implemented in a capsule prototype and tested with endoscopic images. Commercial capsules like Pillcam and Mirocam were also used in the experiment. The results show that the proposed algorithm works well in controlling the brightness level accordingly to the environmental condition, and as a result, good quality images are captured with an average of 40% brightness level that saves power consumption of the capsule.

Frontiers of robotic endoscopic capsules: a review

Digestive diseases are a major burden for society and healthcare systems, and with an aging population, the importance of their effective management will become critical. Healthcare systems worldwide already struggle to insure quality and affordability of healthcare delivery and this will be a significant challenge in the midterm future. Wireless capsule endoscopy (WCE), introduced in 2000 by Given Imaging Ltd., is an example of disruptive technology and represents an attractive alternative to traditional diagnostic techniques. WCE overcomes conventional endoscopy enabling inspection of the digestive system without discomfort or the need for sedation. Thus, it has the advantage of encouraging patients to undergo gastrointestinal (GI) tract examinations and of facilitating mass screening programmes. With the integration of further capabilities based on microrobotics, e.g. active locomotion and embedded therapeutic modules, WCE could become the key-technology for GI diagnosis and treatment. This review presents a research update on WCE and describes the state-of-the-art of current endoscopic devices with a focus on research-oriented robotic capsule endoscopes enabled by microsystem technologies. The article also presents a visionary perspective on WCE potential for screening, diagnostic and therapeutic endoscopic procedures.