An Automatic Bleeding Detection Technique in Wireless Capsule Endoscopy from Region of Interest T. Ghosh1, 3, S. A. Fattah1*, S. K. Bashar1, C. Shahnaz1, K. A. Wahid2, W.-P. Zhu4, and M. O. Ahmad4 1
Bangladesh University of Engineering and Technology, Bangladesh, 2University of Saskatchewan, Saskatchewan, Canada, 3 Pabna University of Science and Technology, Bangladesh, 4Concordia University, Montreal, Canada * E-mail:
[email protected]
Abstract— Wireless capsule endoscopy (WCE) is a painless operative video technology to detect small intestine diseases, such as bleeding. Instead of using the most common RGB (red, green, blue) color scheme, in this paper, YIQ (luminance-Y, chrominance-IQ: in phase-I and quadrature-Q) color scheme is used for analyzing WCE video frames, which corresponds better to human color response characteristics. Analyzing the behavior of each of the four YIQ spaces, first, a region of interest is determined depending on the Q value of the pixels and some morphological operations. Next, instead of considering three spaces of YIQ color model separately, a new composite space Y.I/Q is proposed to capture intrinsic information about the luminance and chrominance of images. Four statistical measures, namely mean, median, skewness and minima of the pixel values in composite space within the ROI are computed as features. It is exhibited that use of composite space lower computational complexity as well as offers noticeably better discrimination between bleeding and non-bleeding pixels. For the purpose of classification, support vector machine (SVM) classifier is employed. Satisfactory bleeding detection performance result is achieved in terms of accuracy, sensitivity and specificity from severe experimentation on several WCE videos which is collected from a publicly available database. Also it is observed that proposed method over performs with comparing some of the existing methods. Keywords— Wireless capsule endoscopy; bleeding detection; YIQ color domain; SVM classifier.
I. INTRODUCTION Bleeding detection in gastrointestinal (GI) tract is one of the important tasks for diagnosing different GI diseases from clinical and physician point of view [1]. The main body of GI tract cannot be reached by traditional endoscopies because of its respective limitations. However, wireless capsule endoscopy (WCE) has been proven to be the best choice of investigation for visualizing the entire small bowel [2]. The problem of WCE lies in its reviewing process which usually takes two hours to complete [3]. Furthermore, there may be some bleeding regions and abnormal characters which cannot be recognized by naked eyes due to their size or distribution. All these problems motivate researchers to develop the computer aided intelligent bleeding detection technology to reduce the burden of physicians. With its gradually wide applications, some efforts have been made to detect bleeding images form the WCE videos so as to decrease the burden of doctors. Suspected blood indicator (SBI) is a technique for bleeding detection from WCE images but its sensitivity and specificity are found not very satisfactory [4]. In [5], color
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histogram based bleeding detection scheme is introduced, which involves high algorithmic complexity. A super pixel based automatic bleeding detection method is developed in [6], which is computationally complex. In [7], probabilistic neural network (PNN) based bleeding image identification scheme is proposed. The method proposed in [8] utilizes color statistical features extracted from histogram probability. In [9], bleeding region growing is demonstrated where initial bleeding frame is marked manually. In [10] and [11], R to G pixel intensity ratio and different statistical measures are employed to detect bleeding frames. Furthermore histogram values of RGB-indexed images are incorporated as features in [12]. Most of the methods described above are utilizing the RGB color scheme, whereas there exists two other color models, namely YUV and YIQ, which are widely used in video processing and TV broadcasting [13], [14]. Here Y stands for the luminance. The other two components (U, V or I, Q) correspond to the chrominance. Although U and V nicely define the color differences, they do not align with the desired human perceptual color sensitivities. Hence, in NTSC, I and Q are used instead. The objective of this paper is to develop an efficient bleeding detection scheme from WCE videos in YIQ color domain. First a method for region of interest (ROI) detection is developed based on Q value and morphological operation to identify possible bleeding zones. Different statistical characteristics of Y, I and Q spaces in discriminating bleeding frames in WCE videos are investigated. A composite space using Y, I, and Q spaces is proposed for extracting features from extracted ROI. In order to classify bleeding and non-bleeding WCE images from extracted features, the support vector machine (SVM) classifier is employed. Bleeding detection performance is tested using leave one out cross validation technique on publicly available large WCE video database. II.
PROPOSED METHOD
A. Region of Interest Detection RGB color space is most widely used in bleeding detection because of common understanding that the color of bleeding belongs to specific shades of red. One problem in using RGB space is that it contains not only the color information but also the color intensity i.e. the RGB values are different for light blue, dark blue and navy blue. So from an individual RGB component it is
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very difficult to identify the color. To overcome this problem YIQ color scheme is chosen in this paper, where there is a brightness component Y (luminance) and two chrominance components which are used in quadrature amplitude modulation: I (in-phase) and Q (quadrature) [13], [14]. In the proposed method, first region of interest (ROI) is identified and then that ROI is used for feature extraction followed by bleeding image classification. One major advantage in ROI selection is that the computational cost involved in feature extraction and classification will be drastically reduced.
(a) p
g
(b)
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From a given RGB color image, first the pixel values are converted into YIQ color space. The relation between RGB and YIQ is given by [15] 0 .114 ⎤ ⎡ R ⎤ ⎡Y ⎤ ⎡0.299 0 .587 ⎢ I ⎥ = ⎢0.596 − 0.275 − 0.321⎥ ⎢G ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢⎣Q ⎥⎦ ⎢⎣0.212 − 0 .523 0.311 ⎥⎦ ⎢⎣ B ⎥⎦
(1) (c)
In YUV color model, U and V components can be thought of as X and Y coordinates within the color space. On the other hand in YIQ color model, I and Q can be thought of as a second pair of axes on the same graph, rotated 33°. Therefore IQ and UV represent different coordinate systems on the same plane. The eye is more sensitive to changes in the orange-blue (I) range than in the purple-green range (Q). As a result, less bandwidth is required for Q than that for I. Eye is most sensitive to Y, next to I, and next to Q. Analyzing different WCE images, it is also found that the Q value is very small and mostly negligible for non-bleeding areas. Thus setting a threshold value Q = 0, will be able to identify bleeding or bleeding like pixels. Presence of bleeding like pixels in a bleeding zone may appear as outliers. In order to get rid of such non-bleeding (or bleeding like) pixels, a pre-processing step based on morphological operation is performed which consists of morphological dilation and opening operations. In the resulting pre-processed image, a region of interest (ROI) appears clearly like a mask, which contains possible bleeding region. This region will be later used for feature extraction and classification between bleeding and non-bleeding images. In Fig. 1, starting from an original WCE image corresponding output images obtained after Q value thresholding and morphological operations are shown. It is clearly observed that the simple Q value thresholding provides a reasonable estimate of the ROI (suspected bleeding zones) with some additional scattered pixels. Morphological operation can successfully eliminate these undesirable pixels. Sufficient margin in threshold value is kept to avoid chance of missing desired bleeding pixels. It is to be mentioned that for non-bleeding image, only few pixels will be selected based on non-zero Q values and thus the corresponding value in Y.I/Q space will be larger. Hence, it would be sufficient to perform feature extraction only on the selected region. B. Feature Extraction Each of the four spaces of the YIQ model is first individually investigated to visualize its capacity to discriminate between
(d)
Fig. 1 (a) Original WCE image, (b) after Q value thresholding, (c) after morphological operation and (d) selected ROI.
bleeding and non-bleeding zones. In comparison to the magnitude of Y and I values, the value of Q is very small for non-bleeding pixels. As a result, instead of considering the YIQ spaces separately, a composite space Y.I/Q is proposed, which provides significantly higher values for non-bleeding pixels. Such a new composite space not only reduces the computational complexity but also offers better separation between the two classes. Different statistical measures of the pixel values in the composite space can be considered as potential features. In this paper, four statistical measures are utilized as features, namely mean, median, skewness and minima. It is to be noted that unlike conventional methods, feature extraction is carried out only in the extracted ROI. 1) Mean and minima: The mean is the arithmetic average of a set of values and is obtained by dividing the summation of all elements by the total number of elements. In a WCE image of size × , the RGB to YIQ transformation provides × values in all the four spaces and the proposed Y.I/Q composite space can be computed. For the × values of pixels in composite space (, ), mean is calculated as
1 (, ) ̅ = ×
(2)
In YIQ color scheme, Q value is very small for non-bleeding pixels and thus I/Q value is comparatively larger for them. Hence it is expected that in the non-bleeding region of a WCE image in composite space, comparatively higher value of mean will be obtained. The smallest value of a data set is called minima. It is expected that in the bleeding region of composite space, comparatively lower value of minima will be obtained. 2) Median: The median is the numerical value separating the higher half of a data sample or a population from the lower half. The median of the × finite values of composite space (, ), median can be found by arranging all the values from
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(a) Mean for bleeding
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where ̅ is the mean value of the dataset. It is difficult to comment on the skewness feature in terms of separation between two classes of the dataset. However, along with the mean, skewness is considered to overcome some critical classification cases. 0
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In Fig. 2, considering 100 bleeding and 100 non-bleeding WCE images, behavior of different statistical features computed from the proposed composite space is shown with the help of histograms. This experimental results closely follow what is generally expected for bleeding and non-bleeding images. The histograms are found very much concentrated within some specific region, which is obviously very advantageous with respect to the case of widely spreaded histograms. As expected for bleeding images, mean, median and minima values are found lower in comparison to those obtained by non-bleeding images. In first two cases, histograms for bleeding zones are found almost non overlapping with peaks showing significantly different locations. As expected in case of skewness, overlapping histograms are obtained.
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Fig. 2. Histogram of 100 bleeding and 100 non-bleeding WCE images, (a) mean of bleeding (b) mean of non-bleeding, (c) median of bleeding, (d) median of non-bleeding, (e) skewness of bleeding, (f) skewness of non-bleeding, (g) minima of bleeding, (h) minima of non-bleeding.
lowest value to highest value and picking the middle one. It is also expected that in the bleeding region of composite space, comparatively lower value of median will be obtained. 3) Skewness: Skewness is a measure of degree of asymmetry of a distribution. In general, an understanding of the skewness of the dataset indicates whether deviations from the mean are going to be positive or negative. If the left tail is more pronounced than the right tail, the dataset is said to have negative skewness. If the reverse is true, it has positive skewness. For the × values of the composite space (, ), skewness is computed as
γ1 =
μ3 , σ3
SUPPORT VECTOR MACHINE (SVM) CLASSIFIER
In the proposed method, the support vector machine (SVM) is used to classify the test WCE image. The key component in SVM learning is to identify a set of representative training vectors deemed to be the most useful for shaping the (linear or nonlinear) decision boundary. These training vectors are called support vectors, which need to lie right on the marginal hyper-planes.
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(h) Minima for non-bleeding
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Considering a training dataset which consists of color texture features of N images xi, where each M dimensional feature vector xi = xi(n), n = 1, …, M is associated with a teacher value or class label. Given a discriminant function f(x) = f(w, x), the objective is to find an M dimensional decision vector w = [w1 w2 … wM]T so that f(xi) can best match with teacher value yi, with all the training dataset taken into consideration. Considering 2 class problem with teacher values +1 and −1, in the basic SVM, all the training vectors xi satisfy the following inequalities: wT xi + b ≥ +1, for all positive xi wT xi + b ≤ −1, for all negative xi An error term is defined as εi ≡ wT xi + b – yi . The main objective here is to create a maximum margin to separate the two opposite classes. Considering the kernel function K(x, y) and empirical vector a, the discriminant function is defined as N
f ( x) =
(3)
where σ is the standard deviation of the dataset and is the third central moment of the dataset which can be computed as
∑ a K ( x , x) + b. i
i
(5)
i =1
A nonlinear kernel function can also be adopted as the inner product and in some cases becomes more effective for supervised classification.
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TABLE I
Overlap Percentage
110
ROI DETECTION ACCURACY
100
No of images
90
50
COMPARISON RESULT OF D IFFERENT METHODS
70 60 50 0
10
20 30 Image Number
40
50
IV. SIMULATION AND EXPERIMENTAL RESULT In this section, the experimental results are presented to show the efficiency of the proposed method considering 1000 color WCE images selected from 15 WCE videos which are publicly available and very widely used [16]. 200 images of them show a sign of bleeding and other 800 as non-bleeding. These images have 576 x 576 pixels. After removing the dark edge, it becomes 512 x 512 pixels. ROI is extracted by Q value thresholding and for each pixel of every suspected bleeding area (Y.I)/Q domain is composed. Then from that pixel mean, median, skewness and minima are calculated. These four features are used as a feature vector to form training matrix. For classification SVM classifier is used with radial basis function (rbf) kernel. Whole method is implemented using one fold cross validation (leave one out) method. In the proposed method the ROI detection accuracy plays an important role, it is highly desirable that original bleeding zone should overlap with extracted ROI. Extreme observation has been made to justify ROI detection accuracy, in this regards 50 images are investigated. Original bleeding zone and region of interest overlap percentage of 50 images is presented and it is clearly shown that ROI overlap percentage is close to 98%. Table I represents the average overlap percentage alone with number of 100% overlap images. There are four cases about the detection result of bleeding image and non-bleeding images. The bleeding image will be possibly detected as non-bleeding image which is called false nonbleeding recognition (Fnb). Similar way the non-bleeding images will be detected as bleeding images which is called false bleeding recognition (Fb). The other two cases are the true bleeding recognition (Tb) and the true non-bleeding recognition (Tnb). To assess the capability of the bleeding detection method, sensitivity and specificity [17] are ideal criterions which are calculated as following. ∑ ∑ ∑
(6)
Method name
Features
Accuracy
Sensitivity
Specificity
Uniform LBP [3]
10
90.60%
83.50%
92.38%
6
81.70%
85.00%
80.87%
4
91.80%
88.50%
92.63%
3
90.40%
70.00%
96.00%
4
93.90%
93.50%
94%
Histogram probability method in [8] Intensity ratio feature method [10] Statistical features method [11] Proposed method
Fig. 3 Original bleeding zone and region of interest overlap percentage
Sensitivity =
Average overlap percentage 96.89%
TABLE III
Image value Average value
80
No of 100% overlap images 33
Specificity = Accuracy =
∑ ∑ ∑
∑ ∑ ∑ ∑ ∑ ∑
(7) (8)
For the purpose of comparison, the result obtained by the proposed method is compared with those obtained by the methods proposed in histogram probability [8], RGB Pixel Intensity Ratio [10], statistical feature method [11] and the uniform local binary pattern (LBP) feature compared in [3]. It is to be mentioned that the LBP features are extracted independently from RGB color space. The comparison results are demonstrated in Table II. It is clearly observed that the proposed method exhibits the best performance in terms of all performance indices. Sensitivity is the most important performance index in bleeding detection, which represents the true bleeding image detection accuracy. It can easily be observed that the sensitivity obtained by the proposed method is extremely satisfactory. V.
CONCLUSION
An efficient scheme is proposed in this paper for bleeding detection from WCE videos in YIQ domain. Instead of directly using all four spaces of YIQ color model, a new composite color space Y.I/Q is proposed and four statistical features are considered in this domain, namely mean, median, and skewness. An ROI detection scheme is introduced utilizing Q value thresholding and some morphological operations. Unlike conventional methods, feature extraction is carried out only within the ROI, which reduces computational burden. It is found that extracting features in the new composite space within ROI provides excellent feature quality to distinguish between bleeding and non-bleeding images especially for mean and median features. The proposed scheme provides high accuracy and sensitivity with less computational complexity.
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[9]
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