Pseudo Color Features Extraction Technique for Cervical Cancer of Pap Smear

Images

Siti Noraini Sulaiman1,*

, Nor Ashidi Mat Isa2,

Imaging and Intelligent Systems Research Team

(ISRT)

School of Electrical and Electronic Engineering,

Engineering Campus, Universiti Sains Malaysia,

14300 Nibong Tebal, Penang, MALAYSIA.

E-mail: 1siti.noraini.sulaiman@hotmail.com

2ashidi@eng.usm.my

Nor Hayati Othman

Clinical Research Platform,

School of Medical Sciences, Universiti Sains

Malaysia, 16150 Kubang Kerian, Kelantan,

MALAYSIA

E-mail: hayati@kb.usm.my

Norhayati Mohamad Noor *Faculty of Electrical Engineering, Universiti Teknologi MARA, MALAYSIA.

E-mail: hayati005@ppinang.uitm.edu.my

Abstract— Cervix cancer is the most common gynecological

malignancy and second most common cancer among female in

Malaysia after breast cancer. The objective of this study is to

extract the size of nucleus and cytoplasm, as well as gray level

values of cervical cells from ThinPrep images so that accurate

value of those parameters can easily be obtained. An

alternative approach of extracting features for Pap smear

cytology images i.e., by using Seed Based Region Growing

technique and Pseudo Coloring is proposed in this study. The

technique is called Pseudo Color Feature Extraction (PCFE). A

correlation test is applied between data extracted using the

proposed algorithm and data extracted manually by

cytotechnologists. The technique operates well on cervical cells

images with correlation values approaching 1.0 which indicates

a strong positive correlation. The strongest relationship is the

size of cytoplasm with correlation factor of 0.988595 and the

next strongest relationship is its gray level with correlation

factor of 0.981534. Such results indicate that the proposed

technique is suitable and has high capability to be used as an

image extraction technique for extracting cervical cells

features as well as acts as a filter and a segmentation tool. This

would assist cytopathologists and cytotechnologists in the

cervical cancer screening process by providing accurate value

of size and gray level of nucleus and cytoplasmic features.

Keywords- Pseudo Color Features Extraction (PCFE), cervical

cells, features extraction, Pap smear, medical imaging.

I. INTRODUCTION

Cancer of the cervix continues to be a significant public health problem among women around the world. It is the most common gynecological malignancy and second most common cancer among females in Malaysia after breast cancer [1]. Worldwide, carcinoma of the cervix is the second most common neoplasm amongst female, accounting for nearly 12% of all female and causing approximately 250,000 deaths per annum [2]. Unfortunately, almost 80% of women diagnosed with cervical cancer are already in the advanced stage, during their first time check up. Cervical cancer has no

early symptoms like any other types of cancer which cause pain or noticeable lumps. However, most of cervical cancer takes 10 to 15 years to develop from normal to advance stage. Hence, the incidence and mortality related to this disease can be significantly reduced through early detection and treatment.

It is believed that the most promising way to decrease the number of patient suffering from this cancer and death is through early detection. The earlier the cancer can be detected, the better the chances of treatment will work and more treatment options can be provided. The cervical cancer prevention strategies that are used at present utilize the Visual Inspection With Acetic Acid (VIA) and Papanicolaou (Pap) Test [3]. In Malaysia, Pap test is the most popular screening method. It is a medical procedure in which a sample of cells from women’s cervix is collected and smeared on a microscope slide. The cells are examined under a microscope by cytotechnologist in order to look for any abnormal feature associated with pre-malignant or malignant changes. These features include irregularly shaped cells nuclei or small or deformed cells [4-6]. However, Pap test does not always produce good result due to several reasons, which include some of the images being blurred, highly affected by unwanted noise[7-9] and too low in contrast or over contrast. These problems can hide and obscure the important cervical cells morphologies, hence increasing false diagnosis rate.

Recently, a new technique, the liquid-based cytology (LBC), has emerged to improve the quality of smears for Cytopathologic evaluation. Unlike the conventional Pap smear preparation, specimens are collected in suspension of cells in liquid media to produce a thin layer of cells on the slide. LBC is designed to produce a more representative sample of the specimens, with reduced obscuring background and a monolayer, cleaner, and clearer smears. LBC does not only reduce the proportion of specimens classified as technically unsatisfactory for evaluation, but also provides representative cell suspensions for human

314978-1-4244-8136-1/10/$26.00 c©2010 IEEE

Papillomavirus (HPV), Chlamydia, and other molecular biological tests. This technique improves the quality of smears, leading to a better diagnosis. However, it is much more expensive to be used as a routine screening test [10].

On the other hand, some researchers focus on enhancing the original Pap smear images by applying image processing algorithm to reduce the problem and provides better quality of Pap smear images for easier screening process by Cythopathologists[11-13]. Most researchers apply edge detection technique [14, 15], contrast enhancement technique [16, 17], statistical wavelet analysis [18] and several segmentation techniques [6, 13] to find the solutions. Although those studies have proven that the image processing algorithm is useful in removing unwanted noise and improving brightness and enhancing contrast of the images, but they merely focused on gray level images.

In addition, researchers explore another method of highlighting or enhancing an image by using color images (including pseudo color images), as the human eyes are more sensitive to color variation than the variation of gray levels [19]. Therefore, color images are widely being used in many applications but in medical imaging, gray level images are still popular. Pseudo color is a method of assigning colors to gray values according to a table of function. There are many researchers who apply pseudo coloring technique in their works. For examples, Thorsten et al. [20] proposed a setup of examination of different pseudo color scales based on self organizing maps. It leads to an extended color scale which simultaneously gives a comprehensive display of the signal space together with the color codes. Tang et al. [21] used pseudo-color and principal components transform to represent and segment their mammogram and MRI images. Tjahjadi and Bowen [22] examined the effectiveness in displaying monochrome images as color images and presented a pseudo-color enhancement technique for enhancing features in x-ray microtomographs of biological and inorganic materials.

The current study proposes an alternative approach of extracting features for Pap smear cytology images i.e. by using Seed Based Region Growing (SBRG)[23] algorithm based on pseudo color image. The technique is called Pseudo Color Feature Extraction (PCFE) which will be used to extract the size of nucleus and cytoplasm of cervical cells as well as their gray level.

This paper is organized as follows: Section II provides general information of pap smear analysis. Section III describes in detail the proposed method. Section IV presents the results obtained for the proposed method while in Section V discussion on the result obtained is provided. Finally, Section VI concludes the work of this paper.

II. PAP SMEAR ANALYSIS

Pap smear analysis and reports are all based on a medical terminology system. There are two types of reporting systems used. The old system reported on five classes of results, which were Class I to Class V representing Normal, Cervical Intraepithelial Neoplasia (CIN) I, CIN II, CIN III and Cancer. The new and commonly used system is called The Bethesda System (TBS). TBS, it categorizes the Pap

smear analysis into two categories; normal and abnormal. The abnormal cervical cells are classified into two types; low-grade squamous intraepithelial lesion ((LSIL) which is called CIN I in the old system. Similarly, under the old system, the high-grade squamous intraepithelial lesion ((HSIL) is called CIN II, CIN III, or CIS)[24].

Cytopathologies differentiate both types of abnormal cells and normal cells based on several morphologies, one of it being the nucleocytoplasmic ratio. The cytoplasm size decreases while the nucleus size increases from normal cells to HSIL cells through LSIL cells [5, 25]. Moreover, the abnormal cervical cells also show changes in color (gray level) of nucleus and cytoplasm [4, 5]. The gray levels for the cells structures become darker from normal cells to HSIL through LSIL cells. The determination of abnormal cervical cells sometimes can be missed in certain situation. Generally, the accuracy of Pap test depends on the quality of the Pap smear samples. There are several factors that contribute to a low quality Pap smear samples such as overexposing or underexposing to the microscope light during the slide preparations process or the samples themselves are heavily stained with menstruation blood or vaginal discharge and etc. that may lead to difficulty in preparation of the slide. Thus, the cytopathologists may face difficulty in extracting the important morphologies from the samples. Therefore, the current study will propose an alternative approach of extracting features for Pap smear cytology images that can be used to extract the size of nucleus and cytoplasm of cervical cells as well as their gray level.

III. METHODS

A. Pseudo Coloring Segmentation

A pseudo-color image is derived from a grayscale image by mapping each pixel value to a color according to a table or function [19, 26]. Most pseudo color techniques perform a gray level to color transformations. The algorithm used to perform pseudo coloring is a function of;

( ) ( )( )yxfTyxc ,, = (1)

Where ( )yxf , is a gray level image and ( )yxc , is a color

image. In RGB inputs case, it is usually expressed in terms

of its red, green and blue components; ( ) [ ]BGR

cccyxc ,,, = .

The transformation function T produces a three-channel output [27].

( )( )

( ) ( )

( ) ( ) <≤−

−=

+<≤

=

− LyxfN

LNc

NiN

Liyxf

N

Lic

yxfT

N

i

,1if

2,.....1,0

1,if

,

1

(2)

There are several ways of finding pseudo coloring transformations. While one method is based on the intensity quantization, another is based on random image threshold. Nonetheless, this study concentrates on the latter approach. In this case, a set of thresholds is defined by the user. The algorithm transformation is given by function (2) where L, is the total number of gray level, N is the total number of

2010 10th International Conference on Intelligent Systems Design and Applications 315

(a) (b) (c)

region with different color and i is number of region defined by user. It must be noted that all transformations are piece-wise non-linear operator [27].

The threshold is chosen based on the pattern of nucleus, cytoplasm and background of Pap smear image. The outcome from this technique has shown three segmented regions on the image. There are darker (nucleus), medium (cytoplasm) and lighter (background) shade of gray; mapped as red, green and blue respectively.

B. Pseudo Color Feature Extraction Method

This study proposes the combination of Pseudo Coloring and Seed Based Region Growing method which is called Pseudo Color Features Extraction to provide an alternative approach for extracting features of Pap smear cytology images. The proposed PCFE implementation involves two stages. In the first stage, Pseudo Coloring algorithm is applied to the image. This is done in order to segment the image into desired regions and simultaneously get the threshold values for SBRG. In the second stage, SBRG method is used to grow regions out from an image. The main objectives of PCFE algorithm are:

i) To extract the size of nucleus and cytoplasm as well as its gray level values so that accurate value of those parameters can easily be obtained.

ii)To segment the image into three colored regions, hence it can effectively distinguish the nucleus and cytoplasm from the rest of the image. In addition to this, it is able to filter any unwanted noise in the image by changing the pixel value of the noise with the value of region where it belongs.

Figure 1: Location of initial seed pixel (point) and its 11×11 neighborhood

As a start, suppose we have an image that has to be segmented into three regions, namely the red (nucleus), green (cytoplasm) and blue (background) regions by utilizing the Pseudo Coloring method. Therefore, three minimum values for each region is set to get a pseudo color image by using equation (2). It must be noted that the value for red and green will afterward be used as threshold values for the growing condition.

Then, a selected seed pixel is located within a red area of the image to be grown. The chosen seed is at the centre of its [N × N] neighboring pixels. Fig. 1 shows the location of initial seed pixel and its [11 × 11] neighborhood. Next, the seed pixel’s value is compared with its neighboring pixels. If some growing conditions are fulfilled from the comparison, the seed pixel will grow towards its neighbors. The neighboring pixels are included into the region if they satisfy the member criteria mentioned below:

i) In the case of nucleus region, that is the red region; if the gray level of the pixel is less or equal to the preselected threshold and also the red pixel value is more than or equal to the preselected value.

ii)In the case of cytoplasm region, that is the green region; if the gray level of the pixel OR the value of the blue pixel is less than or equal to the preselected threshold and also if the value of green pixel OR the value of the red pixels is greater than or equal to the preselected threshold.

If the neighboring pixel fulfils the above mentioned criteria, it will be added to the region. The neighboring pixel will be transformed to the value of the current region pixels. After the addition and transformation of each pixel, the process will be repeated with a new pixel as seed pixel. The process continues recursively until the region cannot grow anymore or all the pixels have been considered.

Figure 2: The seed pixel growing towards (a) its 4 adjacent neighbors (b) its 4 diagonal neighbors and (c) its 8 surrounding neighbors.

There are three possible ways for a seed to grow which are either towards its 4 adjacent, its 4 diagonal or towards its surrounding neighbors as shown in Figure 2 (a), (b) and (c) respectively. After every inclusion of a neighboring pixel into the region, the total pixel and the gray level for this region must be updated. In other words, during the process, the size for the respected region and its gray level is calculated by using equation (3) and (4) respectively.

regiontheinpixelsofTotalSize = (3)

regiontheinpixelsofTotal

regiontheinpixelsallforlevelGrayofTotallevelGray = (4)

C. Methodology

A number of 508 images from samples of cervical cells have been captured from ThinPrep slides collected from Hospital Universiti Sains Malaysia (HUSM), Kota Bharu, Kelantan, Malaysia. These representative images are used to determine the capability and the suitability of the PCFE technique to extract the size and gray level of the nucleus and cytoplasm. The samples consist of 384 normal cells, 79 LSIL cells and 45 HSIL cells.

51 3 0 2 4 6 7 8 9 10

2

4

0

1

3

5

6

7

8

9

10

Seed point Neighboring pixel

316 2010 10th International Conference on Intelligent Systems Design and Applications

In order to extract the size and gray level of two cervical cells’ structures which are the nucleus and the cytoplasm, several steps need to be taken. The steps include:

i) Segmentation process of cervical cell via pseudo color technique. During this process the region of interest of the cervical cell is segmented into three regions i.e. red for nucleus, green for cytoplasm and blue for background.

ii)Features extraction process by using the PCFE algorithm. During this process the region for nucleus and cytoplasm will be extracted to get its sizes and gray level values. In order to get an accurate value, the PCFE algorithm also acts as filter to sift any unwanted element in the predetermined region in the previous step. The size of nucleus and cytoplasm that are calculated by PCFE algorithm are represented by total number of pixels.

The cells features are also extracted manually by experienced and certified cytotechnologist from HUSM by using an image analyzer. As previously mentioned, in the current study, the size of nucleus and cytoplasm that are calculated by PCFE algorithm are represented by total number of pixels, while the cytotechnologist calculated the size of nucleus and cytoplasm in micrometer measurement size. However, the color of nucleus and cytoplasm that are determined by PCFE algorithm, and by cytotechnologist through an image analyzer software generates values in the same measurement unit, the gray level. In order to determine the capability and suitability of PCFE algorithm, the comparison need to be done with cytotechnologist reading. However, this comparison cannot be performed due to the difference in parameter especially for size of nucleus and cytoplasm. As an alternative, a correlation test needs to be performed to know the degree of relationship between those two variables. The relationship is described as strong positive if the correlation value is higher than 0.8, moderate if equal or more than 0.5 but less than 0.8 and weak if less than 0.5[28]with positive value.

IV. RESULTS

The proposed PCFE algorithm is applied on 508 images of cervical cells, which were taken from ThinPrep samples. Figs. 3 to 7 show the Image 364_1, Image 23_1, Image 194_2 and Image 67_2 respectively. Image (a) shows the original image, while image (b) shows the image after applying Pseudo Color technique and (c) after applying PCFE algorithm. Table I shows the results of features extraction of those images using the PCFE algorithm. The correlation test results between data extracted by using PCFE algorithm and data extracted manually by cytotechnologist are summarized in Table II. Also presented in Table II is a correlation test results between data extracted by using Seeded Region Growing Features Extraction (SRGFE) with gray level images and data extracted manually by cytotechnologist for comparison purposes. Both results indicate a very strong positive linear relationship with correlation factor nearly to the value of 1.0. The strongest relationship is the size of cytoplasm with correlation factor

of 0.988595 and the next strongest relationship is its gray level with correlation factor of 0.981534. Fig. 8 (a), (b), (c) and (d) show plotted graph of data extracted by using the PCFE algorithm versus data extracted manually by cytotechnologist, whereby each graph represents the data for size of nucleus, size of cytoplasm, gray level of nucleus and gray level of cytoplasm respectively.

(a) (b) (c)

Figure 3: Results of features extraction using the PCFE algorithm for Image 63_1. (a) Original Image (b) Image after applying Pseudo Color (c) Image after applying PCFE.

(a) (b) (c)

Figure 4: Results of features extraction using the PCFE algorithm for Image 364_1. (a) Original Image (b) Image after applying Pseudo Color (c) Image after applying PCFE.

(a) (b) (c) Figure 5: Results of features extraction using the PCFE algorithm for Image 67_2. (a) Original Image (b) Image after applying Pseudo Color (c) Image after applying PCFE.

(a) (b) (c)Figure 6: Results of features extraction using the PCFE algorithm for Image23_1. (a) Original Image (b) Image after applying Pseudo Color (c) Image after applying PCFE.

(a) (b) (c) Figure 7: Results of features extraction using the PCFE algorithm for Image 194_2. (a) Original Image (b) Image after applying Pseudo Color (c) Image after applying PCFE.

TABLE I. RESULT OF FEATURES EXTRACTION USING THE PCFEALGORITHMN

Data Type Images

Image

63_1

Image

364_1

Image

67_2

Image

23_1

Image

194_2

Size of Nucleus (pixels) 1343 558 847 658 529

Size of cytoplasm (pixels) 12867 4833 6736 14465 1551

Gray Level of Nucleus 150.2 114.9 127.2 167.6 137.4

Gray Level of Cytoplasm 185.5 156.7 166.7 205.8 195.6

TABLE II. RESULT FOR THE CORRELATION TEST

Data Type Correlation value

PCFE SRGFE with gray level image

Size of Nucleus 0.93979 0.897858

Size of cytoplasm 0.988597 0.981593

Gray Level of Nucleus 0.967879 0.973557

Gray Level of Cytoplasm 0.981534 0.981128

2010 10th International Conference on Intelligent Systems Design and Applications 317

(a)

(c)

(b)

(d)

Figure 8. Graphs of data extracted by using the PCFE versus data extracted manually by cytotechnologist.

V. DISCUSSION

With reference to Figs. 3 to 7 it can be observed that the PCFE algorithm has a high ability to detect and extract the nucleus and cytoplasm size of the cervical cells. Moreover, each images are preserved in its original form and size based on its original images.

The original images for Image 63_1, Image 364_1 and Image 67_2 as shown in Fig. 3, Fig. 4 and Fig. 5 respectively are considered as challenging images. It is because the pixel intensity for cytoplasm is almost the same as pixel intensity for nucleus in the original images (refer to the region highlighted with arrows). After applying pseudo coloring algorithm, the resultant image clearly highlights the region for nucleus as well as its cytoplasm and gives more useful information for further interpretation. But such process has also indirectly highlighted the insignificant existence of noise in the illustrated region. The PCFE algorithm successfully detected the nucleus by filtering any unnecessary region to avoid the noise from being included as nucleus area. The nucleus is clearly distinguished from its cytoplasm region. Hence, the calculation for area of nucleus and cytoplasm becomes more accurate.

The most challenging application for PCFE algorithm is to determine the cells area in the images. As most cells of cervical are scattered randomly in a ThinPrep slide image, where some cells might be far apart from each other and some might be much closer to another cell. For the isolated cells, PCFE algorithm does not face much difficulty in determining the cells area but for cells situated very close to one another, the PCFE algorithm should be made intelligent enough to differentiate between different areas belonging to different cells. The results obtained in Fig. 5, Fig. 6 and Fig. 7 show that the proposed PCFE algorithm successfully segmented the cells area and separate them from the unwanted cell area as well as the background.

The results proved that the PCFE algorithm is a reliable technique to extract features from cervical cells images. Besides that, the PCFE is also able to detect the nucleus and cytoplasm region from the ThinPrep images by successfully distinguishing between insignificant area and background. Our study has shown that, calculation for area of nucleus and cytoplasm is more accurate because this area is completely separated from any unnecessary elements before calculation process commences. It proves that the PCFE is able to give accurate values of nucleus and cytoplasm sizes.

In order to determine the capability of the proposed method, this study compares the results with clinical results by performing a correlation test. A good performance results is obtained, where for the four extracted cervical cells features generated, the correlation factors are higher than 0.9. The results are supported with statistical analysis as illustrated in graphs shown in Fig. 8. Based on the graphs, the manually extracted measurement also increases as measurement extracted by using PCFE increases. The graphs distributions depict a very strong positive correlation especially in Fig. 8 (b), (c) and (d). It supports our justification of getting an accurate value of the cervical cell features as close as possible to its clinical reading. However, for the size of nucleus, although the correlation factor shows a very strong correlation that is 0.93979, as per the graph but the distribution is clustered at its lower value. It is because, almost 76% of the images are normal cases and as mentioned before the size of nucleus increases from normal to HSIL through LSIL, therefore the distribution is clustered at its lower value. The distributions are still closer to each other, indicating a strong relationship between clinical readings and PCFE values.

A comparison study with SRGFE algorithm is also employed. The results obtained are almost similar. The summarized result shown in Table II depicts a very strong positive linear relationship between both methods. The correlation test result for PCFE data is slightly higher as

318 2010 10th International Conference on Intelligent Systems Design and Applications

compared to the other technique that uses SRGFE with gray level images. Superior correlation test suggests that the proposed PCFE algorithm is a good feature extraction technique. Its application in Pap smear would be able to assist cytopathologists for a better cervical cancer screening by providing accurate values of size and gray level of nucleus and cytoplasm of cervical cells.

VI. CONCLUSION

The PCFE algorithm has been proposed as features extraction technique for digital images. The current study used the proposed algorithm to extract four cervical cells features; size of nucleus and cytoplasm, and gray level of nucleus and cytoplasm. The data extracted using the PCFE algorithm gives a high correlation value when compared to data extracted manually by cytotechnologist using the image analyzer. PCFE shows a better linear correlation when compared with SRGFE with gray level image. A strong positive linear relationship between both types of data clearly shows that the PCFE algorithm is suitable to be used in extracting cell features. Thus, it can be concluded that the PCFE algorithm, when applied to Pap smear could better assist the cytopathologists in cervical cancer screening by providing accurate value of size and gray level of nucleus and cytoplasm.

ACKNOWLEDGMENT

The work described in this paper is a collaboration work with MAKNA, Kuala Lumpur, Malaysia, which funded this study. The authors would like to express gratitude to Mdm. Shaira, Mdm. Suzana and Mdm. Wan Salha for their valuable advice in terms of language and also to Mdm. Tengku Muhaini for her advice in interpreting statistical data.

REFERENCES

[1] N. H. Othman, “Cancer of the Cervix- from Bleak Past to Bright Future; A Review, With an Emphasis on Cancer of the Cervix in Malaysia,” Malaysian Journal of Medical Sciences, 2003. 10(1): p. 13-26.

[2] P. Baldwin, R. Laskey, and N. Coleman, “Translational approaches to improving cervical screening,” Nat Rev Cancer, 2003. 3(3): p. 217-226.

[3] D. Maslinor Ismail, “Kanser Serviks,” in http://www.e-rham.com/atcls_07.asp. 2007, Pusat Kepakaran Reproduksi Manusia LPPKN,: Malaysia.

[4] WebMD, “Cervical Cancer - Topic Overview,” in http://www.webmd.com/cancer/tc. 2006.

[5] C. P. Crum, “Female Genital Tract,” 5th Edition ed. Robbins: Pathologic Basis of Disease, ed. R.S. Cotran, Kumar, V. & Robbins, S. L. 1994: Philadelphia: W.B Saunders Company. 1033-1088.

[6] S. W. K. Chan, K. S. Leung, and W. S. Felix Wong, “An expert system for the detection of cervical cancer cells using knowledge-based image analyzer,” Artificial Intelligence in Medicine, 1996. 8(1): p. 67-90.

[7] T. S. Kuise, “Cervical Cancer: Its Causes and Prevention,” 1996, Singapore: Times Book Int.

[8] N. H. Othman, et al., “Pap Smear- Is It An Effective Screening Methods for Cervical Cancer Neoplasia?- An Experience with 2289 Cases,” The Malaysian Journal of Medical Sciences, 1997. 4(1): p. 45-50.

[9] T. G. Hishop, et al., “Cervical Cancer Screening in Canadian Native Women: Adequacy of the Papanicolaou Smear,” The Journal Of Clinical Cytology and Cytopathology, 1994. 38(1): p. 29-32.

[10] J. H. Nam, et al., “A comparison of modified MonoPrep2(TM) of liquid-based cytology with ThinPrep(R) Pap test,” Gynecologic Oncology, 2004. 94(3): p. 693-698.

[11] N. A. M. Isa, M. Y. Mashor, and N. H. Othman, “Automatic Seed Based Region Growing for Pap Smear Image Segmentation,” in Kuala Lumpur International Conference on Biomedical Engineering. 2002. Kuala Lumpur, Malaysia.

[12] S. D. D. V. Rughooputh, and H. C. S. Rughooputh, “Image Segmentation of Living Cells,” in International Conference on Robotics, Vision and Parallel Processing for Automation. 1999. Ipoh, Malaysia.

[13] R. Ghafar, M.Y. Mashor, and N. H. Othman, “Segmentation of Pap Smear Slides Images Using K-Means Clustering,” in Kuala Lumpur International Conference on Biomedical Engineering. 2002. Kuala Lumpur, Malaysia.

[14] D. Anoraganingrum, “Cell Segmentation with Median Filter and Mathematical Morphology Operation,” in Conference on Image Analysis and Processing. 1999.

[15] [15] J. Romberg, W. Akram, and J. Gamiz, “Image Segmentation Using Region Growing,” in http://www.owlnet.rice.edu/elec539/pro ject97 /WDEKnow/index. 1997.

[16] N.A.M. Isa, M.Y. Mashor, and N.H. Othman, “Contrast Enhancement Image Processing Technique on Segmented Pap Smear Cytology Images,” in World Congress on Medical Physics and Biomedical Engineering (WC2003). 2003. Sydney, Australia.

[17] N. E. A. Khalid, P. A. Venkatachalam, and U. K. Ngah, “Diagnosis of Bone Lesion Based on Histogram Equalization,” in International Conference on Robotics, Vision and Parallel Processing for Automation. 1999.

[18] A. N. Bondarenko, A. V. Katsuk, and N. S. Katsuk, “Extracting feature vectors of biomedical images,” in Science and Technology, 2005. KORUS 2005. Proceedings. The 9th Russian-Korean International Symposium on. 2005.

[19] H. R. Myler, and A. R. Weeks, “Computer Imaging Recipes in C.,” 1993: Prentice Hall.

[20] T. Twellmann, L. Oliver, S. Axel, W. Axel, and W. N. Tim,., “An Adaptive Extended Colour Scale for Comparison of Pseudo Colouring Techniques for DCE-MRI Data,” in Bildverarbeitung für die Medizin 2005; Informatik aktuell. 2005, Springer Berlin Heidelberg. p. 312-316.

[21] H. Tang, E. X. Wu, D. Gallagher, S. B. A. Heymsfield, “Monochrome image representation and segmentation based on the pseudo-color and PCT transformations,” in Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE. 2001.

[22] T. Tjahjadi, and D. K. Bowen, “The Use of Colour in Image Enhancement of X-Ray Microtomographs,” Journal of X-Ray Science & Technology, 1989. 1: p. 171-189.

[23] N. A. M. Isa, M. Y. Mashor, and N. H. Othman, “Seeded Region Growing Features Extraction Algorithm; It’s Potential Use in Improving Screening for Cervical Cancer,” International Journal of The Computer, The Internet and Management, 2005. 13(1): p. 61-70.

[24] M. Carolyn Crandall, “FACP Pap Smear,” in http://www.medicinenet.com/pap_smear/article.htm, M. William C. Shiel Jr., FACP, FACR Editor. 2005.

[25] A. D. T. Govan, P. S. MacFarlane, and R. Callander, “Pathology Illustrated,” 2nd Edition ed. 1986, Edinburgh: Churchil Livingstone.

[26] B. Jahne, “Digital Image Processing,” 6th Revised and Extended Edition ed. 2005: Springer.

[27] I. Pitas, “Digital Image Processing Algorithmns and Applications,” 1999: John Wiley & Sons.

[28] J. L. Devore, “Probablility and Statistics for Engineering and The Sciences,” 5th Edition ed. 2000: New York: Duxbury Press.

2010 10th International Conference on Intelligent Systems Design and Applications 319