- Research Article
- Open Access
Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging
© Mirko Arnold et al. 2010
- Received: 30 April 2010
- Accepted: 2 December 2010
- Published: 13 December 2010
Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing specular highlights. For many visual analysis algorithms, these distinct and bright visual features can become a significant source of error. In this article, we propose two methods to address this problem: (a) a segmentation method based on nonlinear filtering and colour image thresholding and (b) an efficient inpainting method. The inpainting algorithm eliminates the negative effect of specular highlights on other image analysis algorithms and also gives a visually pleasing result. The methods compare favourably to the existing approaches reported for endoscopic imaging. Furthermore, in contrast to the existing approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems.
- Colour Channel
- Endoscopic Image
- Colour Balance
- Image Analysis Algorithm
- Representative Colour
Due to reduced patient recovery time and mortality rate, minimally invasive medical procedures have become increasingly common in today's healthcare practice. Consequently, technological research related to this class of medical procedures is becoming more widespread. Since many minimally invasive procedures are guided through optical imaging systems, it is a commonly investigated question, what kind of sensible information may be automatically extracted from these image data and how this information may be used to improve guidance systems or procedure analysis and documentation. Research topics in this context are, among others, robot-assisted guidance and surgery [1–7], automated documentation [8–10] or registration of the optically acquired images or videos to image data obtained from preprocedure X-ray, computed tomography (CT), magnetic resonance imaging (MRI) and other medical image acquisition techniques [11–15].
A key technological advancement that has contributed to the success of minimally invasive procedures is video endoscopy. Endoscopy is the most commonly used method for image-guided minimally invasive procedures, for example, colonoscopy, bronchoscopy, laparoscopy, rhinoscopy. An endoscope is a flexible tube fitted with a camera and an illumination unit at the tip. Depending on the type of procedure the tube is inserted into the human body through either a natural orifice or a small incision. During the procedure, the performing physician can observe the endoscopic video data in real-time on a monitor.
Our area of research is the analysis of endoscopic video data, in particular from colonoscopy procedures. Colonoscopy is a video endoscopy of the large intestine and the currently preferred method for colorectal cancer screening. Common topics in colonoscopic imaging research are, among others, the detection of polyps and colorectal cancer [17–20], temporal segmentation and summarisation of colonoscopy procedures [21–23], image classification [24–26], image quality enhancement  and automated procedure quality assessment [28, 29].
Segmentation of specular highlights may be beneficial in many of these topics. An example is the automatic detection of colorectal polyps. Colorectal polyps can develop into cancer if they are not detected and removed. Figure 1(c) shows an example of a typical colonic polyp. Texture is one of the important characteristics that are used in their detection. The specular highlights on the polyp can affect texture features obtained from the polyp surface and may therefore impede robust detection. A negative effect of specular highlights was also reported by Oh et al. , in the context of the detection of indistinct frames in colonoscopic videos. The term indistinct refers to blurry images that occur when the camera is too close to the intestinal mucosa or is covered by liquids.
In this paper, we propose: (a) a method for segmentation of specular highlights based on nonlinear filtering and colour image thresholding and (b) an efficient inpainting method that alters the specular regions in a way that eliminates the negative effect on most algorithms and also gives a visually pleasing result. We also present an application of these methods in improvement of colour channel misalignment artefacts removal.
For many applications, the segmentation will be sufficient, since the determined specular areas can simply be omitted in further computations. For others, it might be necessary or more efficient to inpaint the highlights. For example the colour misalignment artefacts as shown in Figure 1(b) is a major hindrance in many processing algorithms, for example, automated polyp detection. In order to remove these artefacts the endoscope camera motion needs to be estimated. Feature point detection and matching are two pivotal steps in most camera motion estimation algorithm. Due to the invariance of positions in different colour channels of the images similar to the one shown in Figure 1(b), the specular highlights creates a major problem for any feature matching algorithm and consequently for the camera motion estimation algorithm.
The paper is organised as follows. Section 2 takes a look at related work in segmentation of specular highlights, before the proposed approach is explained in detail in Section 3. The evaluation of the segmentation method is presented in Section 4. The proposed inpainting approach is described in Section 5 along with a brief look at the literature on the topic. In Section 6 we show how removal of specular highlights facilitates better performance of other processing algorithms with the example of colour channel misalignment artefacts. Section 7 concludes the paper and gives an outlook on future work.
There exist a number of approaches to segment specular highlights in images, usually either by detecting grey scale intensity jumps [30, 31] or sudden colour changes [32, 33] in an image. This can be seen as detecting the instances, when the image properties violate the assumption of diffuse reflection. The problem is also closely related to the detection of defects in still images or videos, which has been studied extensively (for an overview, see ).
The segmentation and inpainting of specular highlights was found to be beneficial in the context of indistinct frame detection in colonoscopic videos . Furthermore, Cao et al. , detected specular highlights to facilitate the segmentation process in their algorithm for better detection of medical instruments in endoscopic images. However, this approach inherently detects only specular highlights of a specific size.
After this step, the image was segmented into regions of similar colour and texture using the image segmentation algorithm presented in , which involves colour quantisation and region growing and merging at multiple scales. Within those regions, relatively bright pixels were found using (1) with the same saturation threshold and a value threshold , computed for each region using the 75th percentile and the interquartile range of the values in that region. The union of the set of the absolutely bright pixels as computed in the first step and the set of the relatively bright pixels as obtained through the second step are considered as the set of the specular highlight pixels.
A disadvantage of this method is the high computational cost of the segmentation algorithm. Another issue is the choice of the colour space. Many endoscopy units nowadays use sequential RGB image acquisition. In this technique, the colour image is composed of three monochromatic images taken at different time instances under subsequent red, green and blue illumination. While this allows for an increase in image resolution, it has the disadvantage that fast camera motion leads to misalignment of the colour channels (Figure 1(b)). Consequently, specular highlights can appear either white or highly saturated red, green or blue. The fact that the method presented in  only detects specular highlights by thresholding the value and saturation channels, makes it less applicable to sequential RGB systems. In Section 4 we evaluate the proposed method against the one proposed by Oh et al. which we implemented as described in .
The proposed segmentation approach comprises two separate modules that make use of two related but different characteristics of specular highlights.
3.1. Module 1
The first module uses colour balance adaptive thresholds to determine the parts of specular highlights that show a too high intensity to be part of the nonspecular image content. It assumes that the colour range of the nonspecular image content is well within the dynamic range of the image sensor. The automatic exposure correction of endoscope systems is generally reliable in this respect, so the image very rarely shows significant over- or underexposure. In order to maintain compatibility with sequential RGB imaging systems, we need to detect specular highlights even if they only occur in one colour channel. While this suggests 3 independent thresholds for each of the 3 colour channels, we set one fixed grey scale threshold and compute the colour channel thresholds using available image information.
3.2. Module 2
The second module compares every given pixel to a smoothed nonspecular surface colour at the pixel position, which is estimated from local image statistics. This module is aimed at detecting the less intense parts of the specular highlights in the image. Looking at a given pixel, the underlying nonspecular surface colour could be estimated as a colour representative of an area surrounding the pixel, if it was known that this area does not contain specular highlights or at least which pixels in the area lie on specular highlights. Although we do not know this exactly, we can obtain a good estimate using global image thresholding and an outlier resilient estimation of the representative colour. Once this representative colour is computed, we determine the class of the current pixel from its dissimilarity to this colour.
The algorithm is initialised by an image thresholding step similar to the one in the first module: Using a slightly lower threshold , pixels with high intensity are detected using the condition in (3). The pixels meeting this condition are likely to belong to specular highlights, which is one part of the information we need. The actual computation of the representative colour is performed by a modified median filter. Similar nonlinear filters have been successfully used in defect detection in images and video (see, e.g., [37, 38]), which is a closely related problem. The median filter was chosen for its robustness in the presence of outliers and its edge preserving character, both of which make it an ideal choice for this task.
We incorporate the information about the location of possible specular highlights into the median filter by filling each detected specular region with the centroid of the colours of the pixels in an area within a fixed distance range from the contour of the region. We isolate this area of interest by exclusive disjunction of the masks obtained from two different dilation operations on the mask of possible specular highlight locations. For the dilation we use disk shaped structuring elements with radii of 2 pixels and 4 pixels, respectively. The same concept of filling of the specular highlights is also used in the proposed image inpainting method, which is described in Section 5.
At this point the outputs of the first and second module are joined by logical disjunction of the resulting masks. The two modules complement each other well: The first module uses a global threshold and can therefore only detect the very prominent and bright specular highlights. The less prominent ones are detected by the second module by looking at relative features compared to the underlying surface colour. With a higher dynamic range of the image sensor, the second module alone would lead to good results. However, since the sensor saturates easily, the relative prominence of specular highlights becomes less intense the brighter a given area of an image is. It is these situations in which the first module still allows detection.
In the presence of strong noise it can happen that single isolated pixels are classified as specular highlights. These are at this stage removed by morphological erosion. The final touch to the algorithm is a slightly stronger dilation of the resulting binary mask, which extends the specular regions more than it would be necessary to compensate for the erosion. This step is motivated by the fact that the transition from specular to nonspecular areas is not a step function but spread due to blur induced by factors such as motion or residues on the camera lens. The mask is therefore slightly extended to better cover the spread out regions.
In order to evaluate the proposed algorithm a large ground truth dataset was created by manually labelling a set of 100 images from 20 different colonoscopy videos. Since negative effects of specular highlights on image analysis algorithms are mostly due to the strong gradients along their contours, the gradient magnitudes were computed using a Sobel operator and overlayed on the images. This allowed the manual labelling to be very precise on the contours. Great care was taken in including the contours fully in the marked specular regions.
In order to compare the performance of the proposed algorithm with the state of the art, we implemented the approach proposed by Oh et al. as described in , which was also proposed for detection of specular highlights in endoscopic images. Both methods were assessed by their performance to classify the pixels of a given image into either specular highlight pixels or other pixels.
Using the aforementioned data set, we evaluated both methods using a cross-validation scheme where in each iteration the images of one video were used as the test set and the rest of the images were used as the training set. For each iteration we optimised the parameters of both the method in  and the proposed one using the training set and tested their performance on the test set. At any point no information about the test image was used in the optimizing process of the parameters. We chose two different cost scenarios to measure optimal performance: scenario A assigned equal costs (unit per misclassified pixel) to missed specular highlights and falsely detected specular highlights; scenario B assigned twice the cost to missed specular highlights (2 units per missed specular highlight pixel).
Performance of the algorithm for equal costs of false positives and false negatives. Compared to the method in  with dilation the proposed method achieves a cost reduction of 28.16%.
Method of Oh et al.
Method of Oh et al. with Dilation
Performance of the algorithm for doubled costs of false negatives. Compared to the method in  with dilation the proposed method achieves a cost reduction of 31.03%.
Method of Oh et al.
Method of Oh et al. with Dilation
It can be seen that the proposed method outperforms the one presented in  substantially with a cost reduction of 28.16% and 31.03% for cost scenario A and B, respectively. Furthermore, the proposed algorithm was able to process 2.34 frames per second on average on a 2.66 GHz Intel Core2Quad system—a speed improvement of a factor of 23.8 over the approach presented in , which is heavily constrained by its image segmentation algorithm. It took 10.18 seconds on average to process an image. The results are visually depicted in Figure 6.
While the parameters were optimised for each iteration of the cross-validation scheme, they varied only marginally. For images with similar dimensions (in the vicinity of ) to the ones used in this study, we recommend to use the following parameters for cost scenario A (cost scenario B): , , , median filter window size , , . The size of the structuring element for the dilation in the postprocessing step should be 3 and 5 for cost scenario A and B, respectively.
Image inpainting is the process of restoring missing data in still images and usually refers to interpolation of the missing pixels using information of the surrounding neighbourhood. An overview over the commonly used techniques can be found in  or, for video data, in .
For most applications in automated analysis of endoscopic videos, inpainting will not be necessary. The information about specular highlights will be used directly (in algorithms exploiting this knowledge), or the specular regions will simply be excluded from further processing. However, a study by Vogt et al. , suggests that well-inpainted endoscopic images are preferred by physicians over images showing specular highlights. Algorithms with the intention of visual enhancement may therefore benefit from a visually pleasing inpainting strategy, as well as algorithms working in the frequency domain. Vogt et al. also  proposed an inpainting method based on temporal information and can be only used for a sequence of frames in a video and not for isolated individual images.
An inpainting method was reported by Cao et al. in . The authors replaced the pixels inside a sliding rectangular window by the average intensity of the window outline, once the window covered a specular highlight. The approach can not be used universally, as it is matched to the specular highlight segmentation algorithm presented in the same paper.
In , along with their specular highlight segmentation algorithm, the authors also reported an image inpainting algorithm, where they replaced each detected specular highlight by the average intensity on its contour. A problem with this approach is that the resulting hard transition between the inpainted regions and their surroundings may again lead to strong gradients.
In order to prevent these artefacts, in the proposed algorithm, the inpainting is performed on two levels. We first use the filling technique presented in Section 3, where we modify the image by replacing all detected specular highlights by the centroid colour of the pixels within a certain distance range of the outline (see above for details). Additionally, we filter this modified image using a Gaussian kernel ( ), which results in a strongly smoothed image free of specular highlights, which is similar to the median filtered image in the segmentation algorithm.
which can be interpreted as a logistic decay function in a window from to , mapped to a distance range from 0 to . The constant can be used to introduce a skew on the decay function. In the examples in this paper, we use the parameters , , and .
with for all pixel locations .
Sequential RGB image acquisition systems are very commonly used in endoscopy. In these systems the images corresponding to the red (R), the green (G) and the blue (B) colour channels are acquired at different time instances and merged to form the resulting video frame. However, an inherent technological shortcoming of such systems is: whenever the speed of the camera is high enough such that it moves significantly in the time interval between the acquisition instances of the images corresponding to two colour channels, they get misaligned in the resulting video frame, compare, Figure 1(b). This channel misalignment gives the images an unnatural, highly colourful, and stroboscopic appearance, which degrades the overall video quality of the minimally invasive procedures. Moreover, in endoscopic images, the colour is an invariant characteristic for a given status of the organ . Malignant tumors are usually inflated and inflamed. This inflammation is usually reddish and more severe in colour than the surrounding tissues. Benign tumors exhibit less intense colours. Hence the colour is one of the important features used both in clinical and automated detection of lesions . Consequently, removal of these artefacts is of high importance both from the clinical and the technical perspectives.
Compute the Kullback-Leibler divergence, , between the intensity histograms of the colour channels, denoted as: , , for all . is the intensity histogram corresponding to colour channel . Choose the colour channels and , for which the is minimum.
Compute the homography ( ) between the chosen colour channels and , through feature matching. Assume linearity of motion and compute the homography between consecutive colour channels, , .
Align all the colour channels by using the inverse homography, , .
Performance of the colour channel misalignment artefact removal algorithm in images before and after removing specular highlights. SR: percentage of images where the colour channels were successfully realigned. USRND: percentage of images where the colour channels were not successfully realigned, however they were not distorted. USRD: percentage of images where the colour channels were not successfully realigned and they were also distorted. Dataset 1: 50 colonoscopy video frame with colour channel misalignment. Dataset 2: Dataset 1 after specular highlights are removed by the proposed algorithm.
In this paper, we have presented methods for segmenting and inpainting specular highlights. We have argued that specular highlights can negatively affect the perceived image quality. Furthermore, they may be a significant source of error, especially for algorithms that make use of the gradient information in an image. The proposed segmentation approach showed a promising performance in the detailed evaluation. It performed favourably to the approach presented in  and avoids any initial image segmentation, thus resulting in significantly shorter computation time (a reduction by a factor of 23.8 for our implementation). Furthermore, in contrast to other approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems. In the sequential RGB endoscope, a very common problem is the colour channel misalignment artefacts. We developed a simple algorithm to remove these artefacts and tested it using colonoscopy video frames before and after removing specular highlight. A substantial improvement in the performance was observed when specular highlights are removed. The performance of the proposed inpainting approach was demonstrated on a set of images and compared to the inpainting method proposed in .
When using inpainting in practice, it is important to keep the users informed that specular highlights are being suppressed and to allow for disablement of this enhancement. For example, while inpainting of specular highlights may help in detecting polyps (both for human observers and algorithms) it could make their categorisation more difficult, as it alters the pit-pattern of the polyp in the vicinity of the specular highlight. Also, as it can be seen in the second row of Figure 7, inpainting can have a blurring effect on medical instruments. Explicit detection of medical instruments may allow to prevent these artefacts and will be considered in future studies.
Future work will also include a clinical study into whether endoscopists prefer inpainted endoscopic videos over standard ones. We will further investigate to what degree other image analysis algorithms for endoscopic videos benefit from using the proposed methods as preprocessing steps.
This work has been supported by the Enterprise Ireland Endoview project CFTD-2008-204. Thea authors would also like to acknowledge the support from National Development Plan, 2007-2013, Ireland.
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