TWO PHASE HOLISTIC APPROACH FOR OVERLAPPING CELLS SEPARATION IN 2D PAP SMEAR IMAGES

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1 TWO PHASE HOLISTIC APPROACH FOR OVERLAPPING CELLS SEPARATION IN 2D PAP SMEAR IMAGES 1 B.SAVITHA, 2 DR.P.SUBASHINI, 3 DR.M.KRISHNAVENI 1 Research Scholar, Department of Computer Science, 2 Professor, Department of Computer Science, 3 Assistant Professor, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women University, Coimbatore. 1 savibalu36@gmail.com, 2 mail.p.subashini@gmail.com, 3 krishnaveni.rd@gmail.com ABSTRACT Separating the overlapped cells in microscopic Pap smear images is an important step for automating the early detection of cervical cancer. Existing methods have not attempted a complete overlapping cell separation due to the challenges involved in delineating cells with severe overlap and poor contrast. This paper, proposes a two phase approach to segment nuclei and cytoplasm region from the cervical cell by separating the overlapping cells. In phase one, the overlapped cell is separated by searching an object using HSV color conversion followed by each recognized object outlined and processed with sequence of operations like contrast adjustment, histogram equalization and morphological closing operation. The second phase is done to achieve nucleus and cytoplasm regions by converting the cell separated image into pseudo color conversion form. The final result shows the complete separation of overlapped cells without the loss of cell s information after a number of iteration. Experiments are carried out with various images with different stages. The proposed two stage concept executes the separation procedure more efficiently, which can be further processed for identification and classification of cervical cancer. Keywords: Cervical Cancer, Cell Overlapping, Object Oriented Approach, HSV Color Map, Pseudo Color Conversion. 1. INTRODUCTION Cell Overlapping is a common problem with no common solutions, especially in the case of Pap smear images. The reason might also initiates from the preparation of the sample until digital conversion of the sample cell taken. Thus, it is necessary to be very particular in preparation of digital microscopic images. The initial step in preparation of sample is to take a sample of mucus containing cells from the cervix using a spatula. This sample is smeared upon a glass slide and it has to be handled carefully since the thin layer of mucus will not create any overlapping phenomena unless the overlapping will occur. The microscopic digital camera s static nature also shows the cells as overlapped one while capturing images of glass slide. This overlapping phenomenon becomes a practical problem that needs to be overcome by algorithm development. In the medical imaging application as well as in the field of computer vision accurate automated Pap smear analysis system has the need of separating the overlapped cell accurately [9]. More specifically, the algorithm in existence does separation based on concavity points or separating line concept. Few literatures have also used contour method in combination with separating line to separate the touching cells which may deform and leads to loss of cell shape[2]-[3]. In this paper a two phase approach based on color conversion and morphological operation is proposed to segment nuclei and cytoplasm region from the cervical cell through separating the overlapping cells. The paper is structured as follows, the basic understanding of the cell images is detailed in section 2, section 3 discusses the experiments carried out with various images with different stages, and section 4 gives the conclusion followed by the references. 129

2 2. UNDERSTANDING OF THE CELL IMAGES As an early stage of overlapping cell separation, the understanding of the cell image nature becomes vital for the accurate separation of overlapping cells. Especially in the medical image processing, color of the image (objects) due to stains, size of the object, position of the object and number of objects will play a very vital role. 2.1 Types of Cells The overlapping cervical cell may be classified into two different categories. They are, Cell s overlapped in adjacent manner. Cell s overlapped in a layered manner Cell s overlapped in adjacent manner Overlapping in adjacent means cells lying near. The cell s which are overlapped in an adjacent manner can be separated easily with the help of concavity point and cell separating line algorithms. As a result, the number of cells which are present in the overlapped region can be easily calculated by counting the concavity points. By connecting the two concave points that have shortest linear distance are located and connected to separate the overlapping cells [3]. Figure.1 (a) shows the sample images for adjacent mannered overlapping cells Cell s overlapped in layered manner Overlapping in a layered manner means one over another. Separating these layered cells is very difficult due to the common region among the two different cells. Thus, applying the concavity point or cell separating line algorithm will leads to false segment result as well as loss of information. As per the literature, the existing algorithms do not support the layered manner overlapped cells. Therefore a new approach is in need to be applied on the Pap smear image to get accurate segmentation results. Figure.1 (b) shows the sample images for layered mannered overlapping cells. Figure.1(B): Cell s Overlapped In Layered Manner 3. DIFFERENT EXPERIMENTAL METHODS FOR LAYERED MANNER CELL SEPARATION This section details the attempts and experiments done for framing a methodology to separate the cells and the limitation inferred from those methods. 3.1 Separation Based On Intensity Planes The effort is to separate overlapping cell, cell nuclei and cytoplasm region with the understanding of the cell characteristic and stains natures. As an initial step, the original Pap smear image is divided into three different level of intensity planes; i.e. Hue plane, Saturation plane and Value plane [10]. Now the colors of original image with the highest saturation have the highest values and are represented as white which shows the Saturation plane image. Subsequently, the brightest areas of the Value plane image corresponds to the brightest colors in the original image which shows the Value channel image. By doing so, the cells overlapped with each other get separated with one another due to the differences in its intensity and color combination. As per the inference the contrasts among the cells are lesser and thus, it is necessary to increase the contrast of an image by mapping the values of the input intensity image. With the contrast enhanced Saturation and Value channel image as an input to the pseudo color technique for separating the nuclei and cytoplasm region, the label for each object is identified in the label matrix. The result in Figure.2 shows that while increasing the contrast of saturation and value channels the contrast/intensity among nuclei/cytoplasm becomes similar. Therefore, it is possible to separate two overlapped cell but impossible to separate nuclei region from cytoplasm and vice versa. Figure.1 (A): Cell s Overlapped In Adjacent Manner 130

3 region. As a result shown in Figure.5, it is possible to spot the nuclei as well as cytoplasm region but the extraction of the region was not obtained. This method results in loss of cytoplasm region during the experimentation. Figure 2: Subjective Results For Separation Based On Intensity Planes 3.2 Separation Based On Contour Lines From the above experiment, it is found that to separate the nuclei region from the cytoplasm region it is must to distinguish between the complete nuclei and cytoplasm region in the separated cells. Therefore, contour has been applied on the contrast enhanced and histogram equalized image to distinguish the nuclei region from the cytoplasm [8], [13]. These contrast adjustments allow to draw a complete contour due to the irregular and similar contrast among the cells. As a result, it is possible to watch complete cell but not complete nuclei or cytoplasm region. Figure.3: Subjective Results For Separation Based On Contour Lines Figure.4: Subjective Results Of Separation Based On Contrast Enhancement 3.4 Separation Based On Object Oriented Approach From the above experimentation increasing the contrast of the V channel image the 0 pixels(black) gets converted into 1 s(white) at certain regions and therefore the cytoplasm s regions gets the same intensity value as the background region. This results in loss of cytoplasm region. To overcome that, object oriented approach is introduced that is contrast enhancement technique is applied on the Saturation channel and histogram equalization technique is applied on the Value channel to minimize the loss of cytoplasm [6],[11]. After the separation of complete cell, the segmented nuclei region is subtracted from the separated cell to get a complete cytoplasm region. Therefore, pseudo color method is used in identifying each objects i.e. nuclei, cytoplasm and background in the label matrix maps to a different color (Red, Green and Blue color) [5]. Finally, by separating the Red, Green and Blue channel the nuclei and cytoplasm gets separated and can be extracted [9]. Though, the approach could separate the overlapped cells as per Figure.5. There exist grainy noises which form a limitation to this method that leads to false result. 3.3 Separation Based On Contrast Enhancement To minimize the difficulty faced in the previous method 3.2, firstly the nuclei region is segmented from the whole cell using the threshold based segmentation with the fact that the nucleus is darker than the surrounding cytoplasm [12], [15]. Secondly, the segmented nuclei region is subtracted from the separated cell region to get the cytoplasm 131

4 The result shown in figure.8 depicts that very few holes get closed and remaining are left as such. The analysis predicts that this process will not be sufficient to separate nuclei and cytoplasm regions. Figure.7: Grainy Noises Closed Using The Morphological Operation Figure.5: Subjective Results Of Separation Based On Object Oriented Approach 3.6 Separation Based On The Morphological Operation In Phase-1 From the above method experimentation, it is found that the morphological closing operation should be done in the contrast enhanced and histogram equalization results. As a result, the holes which occur due to the original image characteristics get closed. As per the inference from the above all subjective results, the problem is more due to threshold based nuclei segmentation. The reason is that very small threshold leads to over segmentation and very high threshold leads to under segmentation. Therefore, some alternative method for threshold based nuclei segmentation method is in need for better performance. Figure.6: Grainy Noises From Separation Based On Object Oriented Approach. 3.5 Separation Method Based On Morphological Operation To get rectified from grainy noises that present in the final result of method 3.4, morphological closing operation has been introduced to close those small grainy unwanted objects from the separated cells. So, as an initial stage the original Pap smear image is divided into Hue, Saturation and Value Channel. After that, contrast enhancement technique is applied on the Saturation channel and histogram equalization is done on Value channel to get the complete cells. And then, to separate the nuclei region, threshold based segmentation has been applied on the original image followed by; the segmented nuclei region is subtracted from the complete cells to get the cytoplasm region. Finally, the holes/grainy noises are closed by morphological close operation[7]. Figure.8: Subjective Results Of Separation Based On The Morphological Operation In Phase-1 Followed by the phase two shows the pseudo color conversion and RGB separation technique could able to extract the complete nuclei and cytoplasm region. From the results, it is found that the proposed method maintains the shape and size of the cell regions with no loss of information. And, the proposed two stage concept executes the separation procedure more efficiently, which can be further processed for identification and classification of cervical cancer. The future includes the objective evaluation of the 132

5 methodologies which are subjectively evaluated in this paper. 3.7 Complete methodological approach for layered overlapping cell separation From the above all subjective results it is found, while doing the morphological closing operation most of the holes gets closed but the problem is with threshold based nuclei segmentation because it results in over segmentation or under segmentation. To overcome that, the active contour method has been applied for the reason that the boundaries of the object region in mask define the initial contour position used for contour evolution to segment the nuclei region. As a result only the nuclei region gets segmented from the original image. As per the inference, no oversegmentation or under segmentation takes place but the complete nuclei gets segmented. Finally, by subtracting the above segmented nuclei from the contrast enhanced and histogram equalized holes closed images the nuclei and cytoplasm gets distinguished from each other completely in phase-1. In phase-2, with the subtracted image as an input to the pseudo color technique for extracting the nuclei and cytoplasm region, the label for each object is identified in the label matrix and provides red color for nuclei, green color for cytoplasm and blue color for background[14]. Finally, the RGB color separation technique is applied to extract the pseudo color image into red plane (Nuclei), green plane (Cytoplasm) and blue plane (Background) which is shown in Figure.9. And, Figure.10 shows the block diagram of the complete methodological approach for layered overlapping cell separation. 4. CONCLUSION This paper has proposed a two stage concept for separating and extracting the nuclei and cytoplasm regions from the overlap separated cervical cells. The phase one emphasize the importance of basic understanding of the input Pap smear image and it is proven that the HSV color conversion technique, contrast enhancement technique and histogram equalization technique is suitable for the separation of overlapped cervical cells. And, it is also proven that the morphological operation like subtraction and closing is suitable to distinguish nuclei region from cytoplasm region. One major limitation encountered during the preparation of this paper related to the proposed methodology is: it suits only to the pap smear image with saturation(red) and value(black) color combinations of cells. Future scope of this research is to explore an optimized methodology which should separate the overlapping cells of any colors. Figure.9: Block Diagram Of The Proposed Methodology 133

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