Automated Edge Detection Technique for Pap Smear Images ...Automated Edge Detection Technique for...

Automated Edge Detection Technique for Pap Smear Images Using Moving K-Mean

s Clustering and Modified Seed Based Region Growing Algorithm

International Journal of The Computer, the Internet and Management Vol. 13.No.3 (September-December, 2005) pp 45-59

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Automated Edge Detection Technique for Pap Smear Images Using Moving K-Means Clustering and Modified Seed Based

Region Growing Algorithm

Nor Ashidi Mat Isa

Control and Electronic Intelligent System (CELIS) Research Group, School of Electrical & Electronic Engineering,

Universiti Sains Malaysia, Engineering Campus Seri Ampangan, 14300 Nibong Tebal, Seberang Perai Selatan, Malaysia.

E-mail: [email protected], [email protected]

Abstract In previous studies, conventional seed

based region growing (SBRG) has successfully been used to detect the edges of certain regions of interest on digital images. The SBRG algorithm offers several advantages over other conventional edge detection algorithms based on gradient decision; the edges of regions found are perfectly thin and fully connected, and the algorithm is very stable with respect to noise. However, two parameters of the SBRG algorithm, which are threshold value and initial seed point location, must be determined manually. Thus, it is time-consuming and the edge detection performance is highly subjective to the user. Besides that, the SBRG algorithm cannot avoid trapped seed point problem, which causes incomplete edge detection process. The SBRG algorithm also can only detect the edge of one region of interest in one time. To avoid those problems, the current study proposed an automated edge detection technique. The proposed technique consists of moving k-means clustering and SBRG algorithm. However, the current study modified the SBRG algorithm to enhance its

capability in edge detection process. The modified seed based region growing (MSBRG) algorithm is able to detect edges of more than one regions of interest as well as differentiate those edges and can avoid incomplete edge detection process as compared to conventional SBRG algorithm. In the proposed automated edge detection technique, firstly, moving k-means clustering algorithm is used to find the thresholds values automatically. After that, based on the thresholds values, the proposed MSBRG algorithm is applied to detect the edges of regions of interest. Then, the proposed technique is applied to Pap smear images to detect the cytoplasm and nucleus edges of cervical cells. The results obtained show that the proposed automated edge detection technique produce better edge detection performance as compared to conventional SBRG, Cubic Spline, Frei Chen, Kirsch, Laplacian, Prewitt, Roberts, Robinson and Sobel algorithms.

Keywords- Modified seed based region growing (MSBRG), moving k-means, edge detection, Pap smear images, medical imaging.

Nor Ashidi Mat Isa

1. Introduction Edge can be defined as an abrupt grey

level change between two neighbouring pixels (Galbiati, 1990, Jain, 1989). Thus, it characterizes object boundaries. Fan et al. (2001) stated that an alternative edge characteristic is that the colors (or grey level) of the neighbouring pixels are significantly different, even though their brightness values are very similar. Therefore, the study claimed that both the brightness and changes in the grey level between neighbouring pixels should be exploited for more efficient edge extraction.

Many robust and complex edge

detection techniques have been proposed (Fan et al., 2001, Rughooputh & Rughooputh, 1999, Anoraganingrum, 1999). Conventionally, there are two categories of isotropic edge detectors; gradient operators and second derivative operators. Gradient operators, such as Prewitt, Roberts and Sobel operators, detect the edges by looking for the maximum and minimum in the first derivative of the luminance of an image. The second derivative operators, such as Laplacian operator, search for zero-crossings in the second derivative of the luminance of an image to find the edges.

Although both gradient and second

derivative operators are simple and easy to be implemented, those operators have several disadvantages such as the borders of regions found are thick and partially connected, and those operators are unstable with respect to noise. Therefore, seed based region growing (SBRG) algorithm has been proposed by Romberg et al. (1997) as edge detection method. The study proved that unlike gradient and second derivative methods, the borders (edges) of regions found by SBRG algorithm are perfectly thin and connected. Thus, the size and shape of the regions will not be corrupted. The SBRG

algorithm is also very stable with respect to noise. This algorithm has successfully been used either as region segmentation or edge detection technique in several previous studies (Ngah et al., 2002, Venkatachalam et al., 2002, Fan et al., 2001, Tuduki et al., 2000, Ooi et al., 2000, Lim et al., 1999, Justice & Stokely, 1997, Venturi, 1993, Belstaff, 1993).

However, in the conventional SBRG

algorithm, user must manually place a seed point within the image and select the segmentation threshold. These processes must be repeated until the satisfactory results are obtained. Thus, this method is time-consuming and the results obtained are highly subjective to the user. The conventional SBRG algorithm finds the edges of the regions of interest by using region growing concept from a seed point. Once the seed point does not fulfil certain conditions, the region growing will be stopped even though the whole image is not considered to be grown yet. This makes the SBRG algorithm in a risk of facing trapped seed point problem, which can cause incomplete edge detection process. Besides that, the conventional SBRG algorithm requires the user to input only one segmentation threshold in one time. Thus, only the edges of one region will be detected. Detection of edges of another regions needs another segmentation threshold input by the user. Therefore, the purpose of this study is to develop an automated edge detection technique to avoid those problems.

2. Automated Edge Detection

Technique As mentioned in the previous section,

the automated edge detection technique is proposed to detect the edges of the regions of interest on the digital images automatically. As shown in Figure 1, the proposed technique consists of two algorithms, which

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Automated Edge Detection Technique for Pap Smear Images Using Moving K-Means Clustering and Modified Seed Based Region Growing Algorithm

are moving k-means clustering, and the novel modified seed based region growing (MSBRG) algorithm.

Original digital image

Edge detected digital image

Moving k-means clustering algorithm

Modified seed based region growing

(MSBRG) algorithm Figure 1: Block diagram of the proposed automated edge detection technique As shown in Figure 1, firstly, the

original digital image is fed into the moving k-means clustering algorithm. Moving k-means clustering algorithm is used to find the thresholds values automatically. Then, by using these thresholds values, the proposed MSBRG algorithm is applied to detect the edges of the regions of interest.

2.1 Moving K-Means Clustering Algorithm

As shown in Figure 1, the first process

in the proposed automated edge detection technique is applying moving k-means clustering algorithm to determine the clusters values. These clusters will be used as threshold values by the proposed MSBRG algorithm. Previous studies have proved that clustering algorithms were able to cluster the digital image into several regions of interest (Pappas, 1992, Pappas & Jayant, 1989). While, Mat-Isa et al. (2002), Ghafar et al. (2002), Razaee et al. (2000), Chen et al. (1998) and Pham et al. (1997) have successfully used the clustering algorithms to cluster the medical images.

Most of the studies on digital images

used k-means and fuzzy c-means (FCM) clustering algorithms as clustering techniques. However, both clustering algorithms did not always produce good performance due to dead centre and centre

redundancy problems. Beside that, they were not able to avoid the centres from being trapped in the local minimum. In 2000, Mashor proposed moving k-means clustering algorithm (a modified version of k-means clustering). The moving k-means clustering algorithm has successfully reduced those problems. Therefore, the current study proposed the moving k-means clustering algorithm to find the clusters values.

Consider a digital image with lb NN ×

pixels (where b and l are number of row and column of the image respectively) to be clustered into nc clusters. Let p(x,y) is a pixel to be clustered and Cj is the j-th cluster (centre) (x = 1, 2, …, Nb , y = 1, 2, …, Nl and j = 1, 2, …, nc). Based on original moving k-means clustering algorithm (Mashor, 2000), the algorithm of using the moving k-means clustering to find the clusters values can be implemented as:

1. Initialise the clusters and α0, and set αa = αb = α0 (where α0 is a small constant value, 0 0

13< <α and should be

chosen to be inversely proportional to the number of centres).

2. Assign all pixels to the nearest cluster

and calculate the centre positions

using: ∑∑∈ ∈

=j jcy cxj

j yxpn

C ;),(1 (1)


47

Nor Ashidi Mat Isa

where cnj ...,,2,1= rNx ,...,2,1= cNy ,...,2,1= 3. Check the fitness of each cluster using

equation: ( ) ( ) ;),(

2∑∑∈ ∈

−=j jCy Cx

jj CyxpCf (2)

4. Find Cs and Cl, the cluster that has the smallest and the largest value of f(•).

5. If f(Cs) > αa f(Cl), (5.1) Assign the members (pixels) of Cl

to Cs if lCyxp <),( , where lCyx ∈, , and leave the rest of the members (pixels) to Cl.

(5.2) Recalculate the positions of Cs and Cl according to:

⎪⎪⎭

⎪⎪⎬

⎫

=

=

∑∑

∑∑

∈ ∈

∈ ∈

l l

s s

cy cxll

cy cxss

yxpn

C

yxpn

C

;),(1

;),(1

(3)

Note: Cs will give up its members (pixels) before step (5.1) and, ns and nl in Equation (3) are the number of the new members (pixels) of Cs and Cl respectively, after the reassigning process in step (5.1) 6. Update aα according to

caaa n/ααα −= and repeat step (4) and (5) until f(Cs) ≥ αaf(Cl).

7. Reassign all pixels to the nearest centre

and recalculate the centre positions using Equation (1).

8. Update aα and bα according to

0αα =a and cbbb n/ααα −= respectively, and repeat step (3) to (7) until f(Cs) ≥ αb f(Cl).

2.2 Modified Seed Based Region Growing Algorithm After finding the thresholds values

using moving k-means clustering algorithm, the MSBRG algorithm, a modified version of the SBRG algorithm is proposed to detect the edges of the regions of interest. Consider the problem of detecting n edges of interested regions of a digital image. Let jβ is the j-th threshold value, which is used to detect the j-th edge of region of interest ( ). Based on the considerations, the algorithm of the proposed MSBRG can be implemented as:

nj ...,,3,2,1=

i- Implement three pre-processing algorithms to the image, namely median filter, histogram normalization and histogram equalization.

ii- Choose NN × neighbourhood as shown in Figure 2 (for where N must be an odd number equal or greater than 3).

5=N

Figure 2: Location of the seed pixel and its

55× neighbourhood.

iii- Take the clusters values, which determine using moving k-means clustering algorithm in the earlier process and sort the clusters in ascending order ( ). nccc <<< ...21

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iv- Set value for each threshold according to:

njforc jj ...,,3,2,1, ==β (4) v- Examine all pixels in the image. Set

the first pixel with grey level value higher than nβ as initial seed location, . ),(0 yxp

Note: The initial seed location must be located at the centre of all its NN × neighbours, as shown in Figure 2.

vi- Take the first threshold, 1β (where ). 1=j

vii- Calculate the mean of grey level, x (which is known as region mean) and the standard deviation, σ for NN × neighbourhood according to equation (5) and (6) respectively.

n

xx

n

ii∑

== 1_

, (5)

11

2_

−

⎟⎠⎞

⎜⎝⎛ −

+=∑=

m

xxn

ii

σ (6)

where xi is grey level value for i-th pixel and m is total of pixels in the image. viii- Compare each neighbour pixel with

the initial seed pixel. Add a pixel to a region if it qualified for the region through either one of the two conditions listed below:

a. If the gradient of the neighbour pixel is less than 95% of the equalized histogram and its grey level value is more or equal to the preselected threshold, βj.

b. If the gradient of the neighbour pixel is more than or equal to 95% of the equalized histogram and the grey level of the pixel is not more than or equal to one standard deviation away from the region mean.

ix- Set the neighbour pixel, which is added to the region, as new seed location.

x- Repeat step (viii) to (x) until the region cannot grow anymore or all the pixels have been considered.

xi- Change the grey level of the pixel that cannot grow anymore with value of β . j

xii- Repeat step (viii) to (xii) for each threshold.

xiii- Set next pixel with grey level more than nβ as a new initial seed location, if the pixel is not been grown yet.

),(0 yxp

xiv- Repeat step (vii) to (xiv) for each pixel that has potential as initial seed location.

The MSBRG algorithm is a

modification of the conventional SBRG algorithm. The modifications that have been made are listed below:

1 In the conventional SBRG algorithm, user must determine the threshold value manually before region growing process is implemented. Thus, it is time-consuming and the edge detection performance is highly subjective to the user. However, in the proposed automated edge detection technique, the thresholds values are determined automatically in the earlier process using moving k-means clustering algorithm where the clusters will be taken as threshold values in the MSBRG algorithm. This modification is done in steps (iii) and (iv).

2 The proposed MSBRG algorithm also determines the initial seed point location automatically. The modification is done in step (v).

3 The growing process in the proposed MSBRG algorithm can be done more than once. This ensures the proposed automated edge detection technique


49

Nor Ashidi Mat Isa

can detect edges of more than one region. This is done by setting the moving k-means clustering to find two or more thresholds values depend on how many regions to be detected. Then, as mentioned in step (iv), the MSBRG algorithm will set all the clusters as the threshold values of the regions of interest. Step (xii) in the algorithm, allow the proposed MSBRG to do growing process for each thresholds, where by using the same initial seed point location, the image will be regrown to find all of the edges of the regions of interest.

4 In the conventional SBRG algorithm, user only determines one initial seed point location, which is static. However, in the proposed MSBRG algorithm, the initial seed point location is dynamic. The proposed MSBRG algorithm allows the growing process to begin from all pixels in the image, which are possible to be assigned as initial seed point location. In the current study, the new pixel will be assigned as next initial seed point location if it fulfils two conditions; the pixel has higher

grey level than nβ and the pixel is not added to the region yet, as mentioned in steps (xiii) to (xiv). The modification is important to ensure complete growing process on the whole image, where all pixel in the image will be considered to be grown. In the conventional SBRG algorithm, once the region growing process is stopped, other pixels will not be considered to be assigned as initial seed point location or to be grown although those pixels are not added into the region yet. This will produce incomplete edge detection process of the image.

5 The proposed MSBRG algorithm also has the ability to differentiate the types of detected regions of interest. This is done by giving different grey level value for each type of detected edges as mentioned in step (xi).

For the growing process, there are three

possible ways for seed pixel to grow as shown in Figure 3(a), (b) and (c) respectively. In the current study, the seed pixel was grown towards its eight surrounding neighbours.

Figure 3: The seed pixel growing towards (a) its 4 adjacent neighbours (b) its 4 diagonal

neighbours and (c) its 8 surrounding neighbours.

(a) (b) (c)

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3. Methodology In the current study, the proposed

automated edge detection technique was tested to detect the edges of cervical cells on Pap smear images. Papanicolaou test or better known as Pap test is the most popular method to detect the presence of abnormal cells arising from the cervix. Based on Pap smear images, cytopathologists differentiate between abnormal and normal cervical cells based on several morphologies. One of them is the change in nucleocytoplasmic ratio. The cytoplasm size decreases but the nucleus size increases from normal cells to HSIL cells through LSIL cells (Crum, 1994). This phenomena increases the nucleus-to-cytoplasm ratio. However, some studies proved that sometimes the Pap test is not effective (Othman et al., 1997, Kuie, 1996, Hislop et al., 1994). The determination of abnormal cervical cells can sometimes be missed in certain situation. Generally, the accuracy of Pap test depends on the quality of the Pap smear samples. The heavily stained Pap smear may be masked by menstrual blood, vaginal discharge, air artefacts etc, thus obscuring the abnormal cervical cells. Sometimes, overexposing or underexposing to the microscope light may also blur the Pap smear images. Thus, the cytopathologists may have difficulty in extracting the nucleocytoplasmic ratio of cervical cells due to these problems. Therefore, the current study will apply the proposed automated edge detection technique on Pap smear images to detect the edges of cytoplasm and nucleus of cervical cells. In the current study, the detected nucleus and cytoplasm edges will have 255 and 0 of grey level value respectively. This will provide clearer segmented Pap smear images to assist cytopathologists for better nucleocytoplasmic ratio extraction.

4. Results Three Pap smear images, namely Pap1,

Pap2 and Pap3 have been used for edge detection testing. For those images, the proposed automated edge detection technique will perform edge detection process to detect the edges of nucleus and cytoplasm. The current study also compares the edge detection performance produced by the proposed technique to 9 conventional edge detection algorithms; namely, conventional SBRG, Cubic Spline, Frei Chen, Kirsch, Laplacian, Prewitt, Roberts, Robinson and Sobel. Figure 4, 5 and 6 show the edge detection results for Pap1, Pap2 and Pap3 respectively. For each figure, image (a) shows the original Pap smear image. Image (b), (c), (d), (e), (f), (g), (h) and (i) represent the result for edge detection process using Cubic Spline, Frei Chen, Kirsch, Laplacian, Prewitt, Roberts, Robinson and Sobel respectively. For Figure 4, image (j) and (k) represent the result for nucleus and cytoplasm detection process of Pap1 respectively using conventional SBRG, while image (l) represents the result for edge detection process using the proposed technique. For Figure 5 and 6, image (j) represents the result for nucleus detection process using conventional SBRG, while image (k) and (l) represent the results for cytoplasm detection process using conventional SBRG. Image (m) represents the result for edge detection process using the proposed automated edge detection technique.


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Nor Ashidi Mat Isa

(a) Original Pap1 image (b) Cubic Spline (c) Frei Chen

(d) Kirsch (e) Laplacian (f) Prewitt

(g) Roberts (h) Robinson (i) Sobel

(j) SBRG (nucleus edge) (k) SBRG (cytoplasm edge) (l) The proposed technique Figure 4: Pap1 image after applying various edge detection processes.

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A

B

(j) SBRG (nucleus edge) (k) SBRG (cytoplasm edge 1) (l) SBRG (cytoplasm edge 2)


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Nor Ashidi Mat Isa

(m) The proposed technique

Figure 5: Pap2 image after applying various edge detection processes.




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C

D

(j) SBRG (nucleus edge) (k) SBRG (cytoplasm edge 1) (l) SBRG (cytoplasm edge 2)

(m) The proposed technique

Figure 6: Pap3 image after applying various edge detection processes.

5. Discussion The results in Figure 4 to 6 show that

conventional edge detection algorithm based on gradient and second derivative operators produced poor edge detection performance. Cubic Spline, Frei Chen, Kirsch, Laplacian, Prewitt, Roberts, Robinson and Sobel algorithms are very unstable with respect to noise. As shown in Figure 4(a), almost all background regions of original Pap1 image are highly affected by unwanted noise. The edges of these unwanted noise regions have been detected by those algorithms as part of the regions of interest. The edges of the changes of grey level in the nucleus and cytoplasm regions have also been detected. These problems corrupt the original size and shape of the nucleus and cytoplasm structures. Those problems also occur in

Pap2 and Pap3 images, as shown in Figure 5 and 6 respectively. Furthermore, all conventional algorithms based on gradient and second derivative operators create thick edges and the edges are partially connected. Consequently, the original size and shape of nucleus and cytoplasm structures will be corrupted.

The results obtained in Figure 4(j) and

(k) for Pap1 image, Figure 5(j) to (l) for Pap2 image and Figure 6(j) to (l) for Pap3 image show that the SBRG algorithm produces better edge detection performance than conventional algorithms based on gradient and second derivative operators. The SBRG algorithm is more stable with respect to noise. Less edges of unwanted noise regions are detected as part of the regions of interest. The SBRG algorithm has


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Nor Ashidi Mat Isa

successfully detected only the edges of the nucleus and cytoplasm structures and ignoring any changes of grey level in both structures. The SBRG also create perfectly thin and fully connected edges. Those advantages obtained by the SBRG algorithm could maintain the original size and shape of the nucleus and cytoplasm structures.

However, the SBRG algorithm shows

several disadvantages. The results obtained for Pap1 image show that the SBRG algorithm only detects one type of edges in one time. Results in Figure 4(j) and (k) show that the SBRG detect nucleus and cytoplasm edges separately. The same results are also obtained for Pap2 and Pap3 images. The detection of nucleus edges of Pap2 image are shown in Figure 5(j), while the detection of cytoplasm edges of Pap2 image are shown in Figure 5(k) and (l). For Pap3 image, the detection of nucleus edges are shown in Figure 6(j), while the detection of cytoplasm edges are shown in Figure 6(k) and (l).

Let consider the detection of cytoplasm

edges for Pap2 image. As shown in Figure 5(k), the seed point location was set at point A. The result obtained shows that the edge detection process has successfully been done only at the top of the Pap2 image, while the other parts were untouched. If the seed point location was set at point B, as shown in Figure 5(l), the growing process was done only at the bottom of the Pap2 image. The same problems occur in Pap3 image, as shown in Figure 6(k) and (l), which the seed point location was set at point C and D respectively. These results proved that the SBRG algorithm also could not avoid the incomplete edge detection process problem.

Table 1 shows the threshold values of

nucleus and cytoplasm for Pap1, Pap2 and Pap3 images, which were set in the SBRG algorithm. From Table 1, it clearly shows that the threshold values of nucleus and

cytoplasm have different value for different Pap smear images. Besides that, in the SBRG algorithm, user must manually input both threshold values. The processes must be repeated until the satisfactory results are obtained. Thus, this method is time-consuming and the results obtained are highly subjective to the user.

Table 1: Threshold values of nucleus

and cytoplasm for Pap1, Pap2 and Pap3 images.

Image Nucleus threshold

Cytoplasm threshold

Pap1 162 211 Pap2 141 215 Pap3 128 213

Generally, the results obtained in the

previous section favor the proposed automated edge detection technique as a better edge detection technique than other conventional algorithms. As shown in Figure 4(l) for Pap1, Figure 5(m) for Pap2 and Figure 6(m) for Pap3, the proposed technique is able to detect nucleus and cytoplasm edges automatically and simultaneously. Furthermore, those edges were successfully differentiated. The results also prove that the proposed technique ensure a complete edge detection process of the Pap smear images. The proposed technique creates perfectly thin and fully connected edges, and thus, maintain the original size and shape of nucleus and cytoplasm structures. These properties of the proposed automated edge detection technique provides clearer segmented Pap smear images to assist cytopathologists for better nucleocytoplasmic ratio extraction.

However, in case of Pap1 image as

shown in Figure 4(m), the proposed automated edge detection technique was quite unstable with respect to noise. Edges of some unwanted noise regions in the background regions were detected as

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cytoplasm edges. The same result also obtained for Pap3 image where some nucleus edges were failed to be detected as shown in Figure 6(m). But, the effects were much less as compared to conventional edge detection techniques. Overall, the impressive results, which obtained in the previous section, proved that the proposed automated edge detection technique has good capability to be used as automatic edge detection technique.

6. Conclusion

The current study proposed automated

edge detection technique, which consists of moving k-means clustering and modified seed based region growing (MSBRG) algorithm. In the proposed technique, firstly, moving k-means clustering algorithm is used to determine the clusters, which will be used as thresholds by the proposed MSBRG algorithm. Then, the proposed MSBRG algorithm is used to detect the edges of interested regions of digital images automatically. As compared to conventional SBRG algorithm, the proposed MSBRG algorithm offers several advantages. The seed point location and thresholds are determined automatically. Besides that, seed point location is dynamic, which ensure a complete edge detection process of the image. The MSBRG algorithm is also able to detect edges of more than one regions of interest and able to differentiate those regions.

The suitability and capability of the

proposed automated edge detection technique has been tested using Pap smear images. The results obtained in Section 4, showed that the proposed technique has successfully detect the edges of the nucleus and cytoplasm of cervical cells automatically. The proposed technique maintains the original size and shape of both structures. As compared to conventional edge detection technique, such as gradient

and second derivative operators, the proposed technique produced much better edge detection performance of Pap smear images. The proposed technique created thinner nucleus and cytoplasm edges, and those edges are fully connected. The proposed technique is also more stable with respect to noise. As compared to SBRG algorithm, the proposed technique determines the nucleus and cytoplasm edges automatically, ensure a complete edge detection process and able to detect edges of both structures simultaneously. Furthermore, the proposed technique is able to differentiate both regions. This will provide clearer segmented Pap smear images to assist cytopathologists for better nucleocytoplasmic ratio extraction.

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