Unsupervised Colour Segmentation of White Blood Cell for Acute Leukaemia Images A.S.Abdul Nasir, M.Y.Mashor
H.Rosline
Electronic & Biomedical Intelligent Systems (EBItS) Research Group, School of Mechatronics Engineering, University Malaysia Perlis, 02600 Ulu Pauh, Perlis, Malaysia. Email:
[email protected]
Department of Haematology, School of Medical Sciences, University Science Malaysia, Kubang Kerian, Kelantan, Malaysia. Email:
[email protected]
Abstract—Colour image segmentation has becoming more popular for computer vision due to its important process in most medical analysis tasks. One of the main tasks is the segmentation of white blood cell (WBC) where the WBC composition reveals important diagnostic information of a patient. In this paper, the combination between linear contrast technique and colour segmentation based on HSI (Hue, Saturation, Intensity) colour space were used in order to obtain a fully segmented abnormal WBC and nucleus of acute leukaemia images. The unsupervised segmentation technique namely k-means clustering algorithm is used to ease the segmentation process. By implementing the proposed segmentation technique, the fully segmented WBC which consists of cytoplasm and nucleus regions can be achieved by using the combination of linear contrast technique and segmentation based on H component image. Meanwhile, the fully segmented nucleus can be obtained by applying the segmentation based on S component image. The combinations between linear contrast technique and segmentation based on HSI colour space have produced a better effect on improving the accuracy of WBC segmentation with segmentation accuracies of 99.02% and 99.05% for segmented WBC and nucleus, respectively. Keywords-Acute leukaemia; colour segmentation; contrast; HSI colour space; k-means clustering
I.
linear
INTRODUCTION
Cancer is one of the major health problems in Malaysia. Leukaemia is a blood cancer that causes more deaths than any other cancers among children and young adults under the age of 20 [1]. There are two major types of acute leukaemia namely Acute Lymphoblastic Leukaemia (ALL) and Acute Myelogenous Leukaemia (AML) [2]. Leukaemia could be cured if it is detected and treated at the early stage. Generally in leukaemia diagnosis, specific morphological features such as size and shape of abnormal white blood cell (blast) would be observed by haematologists in order to differentiate the types of acute leukaemia [2]. Cytoplasm and nucleus regions formed a blast and contain important information to be observed by haematologists. By extraction and observation of morphological features of acute leukaemia, haematologists would be able to ascertain the samples as either ALL or AML. Currently, the microscopic investigation of blood cells is performed manually by haematologists through visual
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identification under the microscope. However, the manual recognition method is time consuming and effortful [3]. The first task to be performed for morphological analysis of acute leukaemia images is the segmentation of the abnormal WBC. Segmentation of an image refers to the separation of regions with similar characteristics. There are several segmentation methods that have been proposed for blood cells recognition of leukaemia images [4][5]. Khashman and AlZgoul [6] proposed a novel method for segmenting singleblood-cell of leukaemia images into two sub-regions namely the cytoplasm and nucleus regions. The processes for segmenting the infected cell include bimodal thresholding, dilation, region filling and boundary tracing, filtering and elimination of unwanted objects, and finally restoration of the cytoplasm and nucleus regions. This method had successfully segmenting the infected cell with 98.33% of overall ratio for correct segmentation. Some of the proposed segmentation techniques could be applied if the images showed high contrast background that is suitable to define the region of interest. Since the cytoplasm region tends to have similar saturation value with the red blood cell (RBC), this will result on elimination of the cytoplasm area when applying the manual segmentation method [5]. Efficient segmentation for obtaining a fully segmented WBC could be achieved if the pixel value for both cytoplasm and nucleus regions are extremely contrast with the RBC region. Thus, the current study will utilize the potential use of the combination between linear contrast technique and colour image segmentation using HSI colour space in order to obtain the fully segmented WBC and nucleus of acute leukaemia image. II.
METHODOLOGY
In this paper, the proposed method will focus on two image processing parts for obtaining the fully segmented abnormal WBC and nucleus of acute leukaemia images. The first part is to improve the image quality by applying contrast enhancement techniques on acute leukaemia images. The second part will focus on colour image segmentation based on HSI colour space. The procedures used to develop the image
processing techniques are illustrated by the flow chart in Figure 1. Original image
Segmented WBC
Linear contrast technique
Saturation component
Hue component
K-means clustering
K-means clustering
Median filter
Median filter
Region growing
Region growing
Retrieve the colour based from linear contrast image
Retrieve the colour based from linear contrast image
Segmented nucleus
Segmented WBC (a)
implementing this algorithm, each R, G and B colour space will be distributed linearly over the whole histogram so that the dynamic range of the histogram is fulfill (0 – 255). C. Segmentation of White Blood Cell Using HSI Colour Space and K-Means Clustering The colour image segmentation for WBC is performed based on the HSI colour space in order to utilize the colour contents in an image. Since the hue, saturation and intensity are independent of one another, each of this colour space can be processed separately without worrying the correlation between them. On the other hand, if the RGB colour space is used instead, the colour of the segmented image will change correspondingly when a few pixel values are changed [8]. According to the fact, the H component in HSI colour space contains most of the WBC information while the S component contains the structure information of the WBC nucleus [9]. Based on this, colour segmentation based on H and S components image is proposed. Thus, the WBC segmentation can be divided into two image processing parts. The first part is to apply the colour segmentation based on the H component image in order to obtain a fully segmented WBC. The conversion from RGB image to H component image can be carried out according to the following equation [10]:
(b)
Hue =
Figure 1. The proposed image processing steps for (a) white blood cell and (b) nucleus segmentation.
A. Image Acquisition The first step is to acquire the image of acute leukaemia blood samples. The samples were obtained from Hospital University Science Malaysia (HUSM). Both ALL and AML slide images were captured at 40X magnification with 800 x 600 resolution and saved in bitmap (*.bmp) format. B. Contrast Enhancement for Acute Leukaemia Using Linear Contrast Technique Linear contrast technique is used to increase the contrast level and brightness level of the image. The technique is based on the original brightness and contrast level of the images to be adjusted. The linear contrast algorithm is defined in (1) [7].
outRGB ( x , y ) =
inRGB(x,y) outRGB(x,y) aRGB bRGB
⎡ ( inRGB ( x , y ) − aRGB ) ⎤ 255 * ⎢⎣ bRGB − aRGB ⎥⎦
(1)
: The original RGB value of the pixel : The new RGB value of the pixel : Minimum RGB value : Maximum RGB value
Based on (1), linear contrast technique will consider each range of RGB (Red, Green, Blue) colour space in the image. Thus, the range of each colour space will be used for contrast stretching process to represent each range of colour. This will give each colour space a set of min and max values. By
θ
θ
if B ≤ G
360° − θ
if B > G
(2)
1 ⎧ [(R − G ) + (R − B )] ⎫⎪ ⎪ −1 2 = cos ⎨ 1 ⎬ ⎪ (R − G )2 + (R − B )(G − B ) 2 ⎪ ⎭ ⎩
[
]
(3)
The second part is to apply the colour segmentation based on the S component image, where the conversion from RGB image to S component image can be carried out by using the following equation [10]: Saturation = 1 −
3 R+G+B
min (R , G , B )
The k-means is a clustering method which is one of most popular unsupervised learning algorithms due to simplicity. Here, the k-means clustering has been used image segmentation. K-means clustering is based minimizing the objective function, J as in (5): n
J =
i =1 j =1
the its for on
2
k
∑∑
(4)
xi − cj (5)
where n is the number of data, k is the number of cluster, xi is the i-th sample and cj is the j-th centre of the cluster. In this
study, each data will be clustered into 3 groups for all analyses. The algorithm is composed of the following steps: Step 1: Step 2: Step 3: Step 4:
Select randomly k points into the space represented by the pixels that are being clustered. These points represent the initial cluster centre, cj. Assign each data to the nearest centre. When all data have been assigned, recalculate the new centre position. Repeat step 2 and 3 until the centres are no longer move. This will produce a separation of the object into group from which the metric to be minimized can be calculated. III.
(c) Figure 3. Resultant segmented nucleus after applying (a) S component, (b) kmeans clustering and (c) median filter and region growing.
RESULTS
The results obtained after applying the combination between linear contrast technique and colour segmentation using HSI colour space are shown as below: (a)
(b)
(a)
(b)
(c)
(d)
(c)
(d)
(e)
(f)
Figure 4. (a) Original ALL image and resultant image after applying (b) linear contrast, (c) k-means clustering on H component image, (d) median filter and region growing, (e) k-means clustering on S component image and (f) median filter and region growing.
IV. (e)
(f)
Figure 2. (a) Original AML image and resultant image after applying (b) linear contrast, (c) H component on original image, (d) H component on linear contrast image, (e) k-means clustering and (f) median filter and region growing.
(a)
(b)
This research is supported by Ministry of Higher Education, Malaysia under the Fundamental Research Grant Scheme (FRGS)
DISCUSSION
In this study, the combinations of linear contrast technique and colour image segmentation using HSI colour space have been applied on Acute Lymphoblastic Leukaemia and Acute Myelogenous Leukaemia images. Here, the acute leukaemia images have been segmented using the unsupervised k-means clustering algorithm in order to recognize the significance of the hue and saturation components of HSI colour space on image segmentation. The qualities of fully segmented WBC and nucleus were determined based on quantitative evaluation by comparing the resultant segmented image with manual segmentation image. The analyses had been conducted using 50 ALL and 50 AML images. The two acute leukaemia samples are shown in Fig. 2-4. Fig. 2(a) represents the original AML image. The implementation result of linear contrast technique on AML image is shown in Fig. 2(b). Based on the
resultant image, the contrast of the nucleus, cytoplasm and background regions in AML image has been improved significantly. In order to perform the WBC segmentation, the hue component information had been extracted from both original and linear contrast images. Fig. 2(c) represents the H component image that had been extracted from original image. Meanwhile, Fig. 2(d) represents the H component image that had been extracted from linear contrast image. Based on these resultant H component images, there are slightly difference appearances of WBC, nucleus and background regions that can be seen between these two figures. Based on Fig. 2(c), the nucleus and RBC regions tend to have similar hue pixel values. Thus, it would be difficult to segment the WBC from the background. However, this situation does not appear in Fig. 2(d). Based on Fig. 2(d), each WBC, RBC and background regions tend to have difference range of hue pixel values. Thus, the image can be divided into high H region and low H region. The WBC has been appeared as the brighter part of the image (high H region), while the RBC has been appeared as the darker part of the image (low H region). The formation of different hue pixel values between these three regions is due to the effect of extracting the H component information from the linear contrast image. As a result, the shape of a complete WBC which cover both nucleus and cytoplasm regions can be seen easier than before the implementation of the linear contrast technique. Since the hue pixel values of the WBC are different from the RBC and background regions, thus it would be easier to segment the WBC from the background. For this purpose, kmeans clustering algorithm had been applied in order to perform the WBC segmentation. Fig. 2(e) shows the resultant image obtained after performing the clustering process. Then, the resultant image will be processed using N x N (N=7) median filter and region growing in order to produce a cleaner and clearer image. During applying the region growing algorithm, any regions which are less than 1000 pixels are considered as non-WBC and will be eliminated from the image. The application of median filter and region growing algorithms had resulted on the final segmented image as shown in Fig. 2(f). In order to obtain a segmented nucleus, the S component information had been extracted from segmented WBC image as shown in Fig. 3(a). Then, the same k-means clustering algorithm had been applied on S component image in order to segment the nucleus more effectively. Fig. 3(b) shows the resultant image obtained after performing the clustering process. Then, the image will be processed using median filter and region growing. These had resulted on the final segmented nucleus as shown in Fig. 3(c). The implementation results for obtaining a fully segmented WBC and nucleus on ALL image are shown in Fig. 4(a)-(f). For quantitative evaluation, the proposed method had successfully segmented the 100 acute leukaemia images with segmentation accuracies of 99.02% and 99.05% for segmented WBC and nucleus, respectively.
Thus, the fully segmented WBC and nucleus can be achieved by implementing the proposed segmentation technique. V.
CONCLUSION
The results show that the proposed combination methods between linear contrast technique and colour image segmentation using HSI colour space work successfully in the segmentation of acute leukaemia images. The significant changes between the WBC and background regions can easily be seen after applying the linear contrast technique. The fully segmented WBC can be achieved by using the combination of linear contrast technique and segmentation based on H component image. For nucleus segmentation, these can be achieved by applying the segmentation based on S component image. The results have proved the fact that the H component in HSI colour space contains most of the WBC information, while the S component contains the structure of nucleus. Hence, the resultant images would become useful to haematologists for further analysis of acute leukaemia. ACKNOWLEDGMENT The authors gratefully acknowledge and thank the team members of acute leukaemia research and University Science of Malaysia (USM). We also would like to acknowledge the Malaysian Government for providing the financial support of Fundamental Research Grant Scheme under the Ministry of Higher Education. REFERENCES [1]
G. C. C. Lim, S. Rampal, and H. Yahaya, “Cancer incidence in peninsular malaysia,” The Third Report of the National Cancer Registry, Malaysia, 2008. [2] S. Miwa, Atlas of Blood Cells. Tokyo, Japan: Bunkodo Co., Ltd., 1998. [3] G. P. M. Priyankara, O. W. Seneviratne, R. K. O. H Silva, W. V. D Soysa and C. R. De Silva, “An extensible computer vision application for blood cell recognition and analysis”, 2006. [4] C. Pan, Y. Fang, X. G. Yan, and C. X. Zheng, “Robust segmentation for low quality cell images from blood and bone marrow,” International Journal of Control, Automation, and Systems, vol. 4, no. 5, pp. 637-644, 2006. [5] A. N. A. Salihah, M. Y. Mashor, N. H. Harun, A. A. Abdullah, and H. Rosline, “Improving colour image segmentation on acute myelogenous leukaemia images using contrast enhancement techniques,” in 2010 IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010), Kuala Lumpur, Malaysia, 2010. [6] A. Khashman and E. Al-Zgoul, “Image segmentation of blood cells in leukemia patients,” Recent Advances in Computer Engineering and Applications, pp. 104-109, 2010. [7] N. Mustafa, N. A. M. Isa, M. Y. Mashor, and N. H. Othman, “Colour contrast enhancement on preselected cervical cell for ThinPrep® images,” in Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), Kaoshing, Taiwan, 2007. [8] C. Y. Huang and M. J. Wu, “Image Segmentation,” Final Report, University of Winconsin-Madison, 2006. [9] J. Wu, P. Zeng, Y. Zhou, and C. Olivier, “A novel color image segmentation method and its application to white blood cell image analysis,” in Proceedings of 8th IEEE International Conference on Signal Processing (ICSP 2006), Beijing, China, 2006. [10] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed., Prentice Hall, 2007.
