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Hybrid framework based on Evidence theory for blood cell image segmentation Ismahan Baghli*, Amir Nakib**, Elie Sellam**, Mourtada Benazzouz*, Amine Chikh* and Eric Petit** *Universit´e de Tlemcen, Laboratoire GBM, Tlemcen, Algeria **Universit´e Paris-Est Cr´eteil, Laboratoire LISSI, Cr´eteil, France

Abstract The segmentation of microscopic images is an important issue in biomedical image processing. Many works can be found in the literature; however, there is not a gold standard method that is able to provide good results for all kinds of microscopic images. Then, authors propose methods for a given kind of microscopic images. This paper deals with new segmentation framework based on evidence theory, called ESA (Evidential Segmentation Algorithm) to segment blood cell images. The proposed algorithm allows solving the segmentation problem of blood cell images. Herein, our goal is to extract the components of a given cell image by using evidence theory, that allows more flexibility to classify the pixels. The obtained results showed the efficiency of the proposed algorithm compared to other competing methods. keywords: Microscopic image, Evidence theory, Image segmentation, Watershed.

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Introduction

Cell image segmentation is until now a challenging problem due to the large variability of the images; different microscopes, stains, cell types, cell densities, etc. and their complexity: large number of cells, acquired using multiple microscopes, acquired at different wavelength, etc. So, in practice manual methods are the most used, even if they remain imprecise, subjective and very onerous. It is obvious that the use of an efficient image processing method will improve the effectivness of the analysis, the diagnostic, and save time and money. In the case of blood cell images, an example of automation consists in the detection and the count of the main different components of blood: White Blood Cells (WBCs), Red Blood Cells (RBCs) and platelets. Many segmentation methods were proposed in the litterature to solve the cell segmentation problem since 1960 [1]. However, most of them are based on few basic approaches: intensity thresholding, feature detection, deformable model fitting, morphological filtering, and region accumulation. In this paper, we considered the last approach, where the principle consists in strating from selected seed points in the image and to iteratively add connected points from labeled regions. In this class of approaches, the well-known example is the watershed transform that is from mathematical morphology and works per intensity layer. It requires an edge-enhanced image, as it is commonly suited to have the atershed lines at the edges. This approach is famous for providing oversgmentation and requires further processig [2] [3]. Indeed, this work deals with the post processing step to extract from a given microscopic image blood cells the Nucleus, the cytoplasm (both components of the WBC), and RBCs (Red Blood Cell) from the background (see fig. 1; Nucleus, Cytoplasm, RBC and background are colored with green, yellow, red and black respectively).

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Figure 1: Cell’s Extraction. (a) Original image (b) Segmented image

In the literature, one can find different techniques. Going from simple method as global thresholding [4], up to hybrid methods. The combination between evidence theory and FCM (Fuzzy C-Means) to segment cells in [5], the use of Mean Shift and GVF (Gradient Vector Flow snake) to segment nucleus and cytoplasm in [6], a snake algorithm to segment nucleus and the Zack’s threshold to segment cytoplasm in [7], a thresholding to remove RBC and background and another thresholding to separate the detected WBC into nucleus and cytoplasm [8], the Gram-Schmidt orthogonalization with a snake algorithm to segment nucleus and cytoplasm [9] and nucleus enhancer followed by Otsu threshold and RBC size estimation to segment WBC in [10]. Moreover, many authors -including us-, have been interested by the combination between watershed and other methods: a marker controlled watershed is applied to segment RBC in [11] and to segment WBC in [12], the obtained watershed region were merged by granularity criterion to segment WBC in [13], color watershed growing after a clustering of color’s plane for cells segmentation in [14], the use of hybrid gradient (Texture and intensity) as a marker for the watershed transform to segment WBC in [15], saturation gradient extraction, morphological reconstruction and watershed transform for WBC segmentation in [16], two schemes for nucleus segmentation(watershed transform and level set method) and two schemes of cytoplasm segmentation (granulometric analysis and morphological operators) in [17] [18], Otsu’s threshold to segment RBC and watershed transform to separate overlapping cells in [19], marker-controlled watershed by internal and external markers to segment RBC from images acquired by a DHM camera (Digital Holographic Microscopy) in [20], the internal marker was obtained by morphological operators (opening, erosion and dilation)on binary image and the external marker was extracted by distance and watershed transform, morphological watersheds and region merging based on the dissimilarities of watershed regions to segment cells in [21], three different masks to apply three different watershed in order to segment WBC, RBC and platelets [22], Application of the watershed algorithm based on map distances (closing and erosion operations on binary image) for the final division of the image into catchment’s basins, each corresponding to one cell in [23] and [24]. The rest of the paper is organised as follows. The Proposed method is exposed in section 2, while the experimental results, discussion, and comparison study are presented in section 3. We conclude this paper in section 4.

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Proposed framework

In this section, we present The proposed framework, called ESA (Evidential Segmentation Algorithm). Herein, we use the power of the evidence theory to classify the pixels of a given watershed transformed image. The idea is to use the evidence theory to classify the pixels of the watershed transformed image rather than the original image. In this way, we reduce the complexity of the segmentation method (the number of pixels to classify). In other terms, we take profit from the oversegmentation provided by the watershed transformation. Then, a merging step, based on evidence theory, is performed. The added value of using the evidence theory instead of a traditional classification method, lies in its ability to integrate the colors and lightning uncertainties, and hence, increasing the belief on a

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given hypothesis by computing the degree of belief between the color distance. The two main step of ESA are summarized below: 1. Watershed transformationis applied to the original image. 2. Evidential classification: (a) We associate to each labeled region WBC or RBC a degree of belief using the approach as in [25]. At this step, we considered the CMYK color space that allows to extract RBCs in most cases. However, in some cases the cytoplasm is confused with RBC. (b) To enhance the performance, we switch to the HSV (Hue, Saturation, Value) color space and apply the evidential classification. To do so, a mass function that consists in representing each region by its average value in the HSV space is considered. (c) The following heuristic is applied: if the mass is greater than 0.5, then the region is reclassified as WBC, otherwise, it is left as RBC region. (d) Classification of WBC into Nucleus and Cytoplasm using the green channel of the RGB space.

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Results and discussion

Our experiments were performed on an image database (87 images) acquired in the hemobiology service of the Tlemcen Hospital (Algeria). The blood smears were colored using a MGG (May-Granwald Giemsa ) coloration. The use of a LEICA environment (camera and microscope with a 100x magnification) allowed obtaining 24-bits RGB pictures of 1024 × 768 pixels. To illustrate the performance of the proposed method on a given cell image from our database, we present in Fig 2 the obtained segmentation with the ESA, in Fig. 2-(b) Nucleus, Cytoplasm, RBC and background are colored with green, yellow, red and black respectively.

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Figure 2: ESA results: (a) original image (b) ESA segmentation (c) Ground truth

The cell components were well recognized in fig. 2, fig. 3, fig. 4 and fig. 5, especially in fig. 3 and 5 where the segmentation is almost perfect. However, in fig. 2 a small part of cytoplasm has been identified as Nucleus, which not greatly affect the identification of the WBC, and in fig. 4 a cytoplasm’s part was skipped on the cell in center left. A comparison of the performance of the proposed method to those of others methods: stochastic watershed [26] and controlled watershed [27] was performed. The Fig. 6 illustrates this comparison to the other competing methods on an image that is not easy to segment. Fig. 6 (b) and fig. 7 (b) present the result when using the stochastic watershed. One can remark that the algorithm oversegments or misses a few cells (at the bottom of fig. 6 (b), one RBC is not detected and the WBC on fig. 7 (b) is oversegmented). In fig. 6 (c) and fig. 7 (c) the result via controlled watershed is showed. As it can be seen, a part of 3

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Figure 3: ESA results: (a) original image (b) ESA segmentation (c) Ground truth

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Figure 4: ESA results: (a) original image (b) ESA segmentation (c) Ground truth

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Figure 5: ESA results: (a) original image (b) ESA segmentation (c) Ground truth

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Figure 6: Segmentation comparison: (a) original image (b) stochastic watershed (c) Controlled watershed (green: markers) (d) ESA segmentation

the original image is subsegmented: cytoplasm of the WBC in fig. 6 (c), at bottom part is well segmented, but that

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(a)

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Figure 7: Segmentation comparison: (a) original image (b) stochastic watershed (c) Controlled watershed (green: markers) (d) ESA segmentation

in the top part is merged with the nucleus and in fig. 7 (c) two RBCs were merged with the Nucleus of the WBC; the major problem is that we have to mark each cell to be well segmented. In fig. 6 (d) and fig. 7 (d), we present the ESA result, the edges are well detected, nevertheless, a slight overtaking between nucleus and cytoplasm on the WBC in the bottom of fig. 6 (d). When applied to all our data, ESA leads to recognize more than 88% of the processed cells. The Dice score and the Jacard similarity have achieved a good rates (see Table 1). Table 1: The ESA results 87 images

Dice score 0.9325

Jacard similarity 0.8736

We have observed that the γi from the Deneoux formula [25], which is equal to and/or decreasing false positives like is noted in table 2.

1 di ,

has an impact on increasing

Table 2: The impact of γi parameter Color projection Magenta Magenta Magenta Magenta Magenta Magenta Magenta HSV HSV HSV HSV

γi for WBC data 0.9 to 0.05 0.05 to 0.004 used 0.0048 less than 0.003 used 0.0048 used 0.0048 used 0.0048 -

γi for RBC data used = 0.01 used = 0.01 used = 0.01 used = 0.01 0.9 to 0.016 0.015 to 0.011 less than 0.009 0.9 to 0.1 used 0.09 0.08 to 0.05 less than 0.04

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Impact many RBC False positives few RBC False positives the fewest False positives RBC and WBC False positives RBC and WBC False positives few WBC False positives RBC False positives RBC False positives the fewest False positives few False positives WBC False positives

We have observed that the changes in γi parameter lead to: 1. In Magenta channel: • As the distance between WBC samples grows (γi decreases) as RBC false positives decrease, until γi reaches values equal or less than 0.003, here, the results get worse due to many RBC and WBC false positives. • Both RBC and WBC false positives are observed when there is a small distance between RBC samples up to 0.016, past 0.015 until 0.011 there are few WBC positives, and then RBC false positives decrease when γi is equal or less than 0.009. 2. In the HSV projection: • When a small distance between RBC samples is observed, there is RBC false positives, where the RBC samples are neither far neither close then a few false positives were observed, more distance grows (γi less than 0.04) more WBC false positives increase. In the HSV color space, only RBC samples are considered because the transformation is made only to check if the RBC classification is right. Thus, values of γi in Magenta channel has an important impact compare to its values in the HSV space. Segmentation performances are good enough. However, some drawbacks were observed: errors mostly consist of classification of some cytoplasms as RBCs. Let’s keep in mind that, unlike the RBCs which always have the same appearance, WBCs have diverse classes such as basophil, eosinophil etc. Those differences mainly concern the shape of Nuclei and the color of Cytoplasm.

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Conclusion

In this paper, a segmentation framework based on evidence theory, called ESA is proposed. It allows solving the segmentation problem of blood microscopic images, where the goal is to extract the component of a cell. The use of evidence theory and watershed transformation is under work and, we also would like to extend our researches to the possibility theory, in order to enhance the performances of our method.

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