IMAGE SEGMENTATION AND RECOGNITION TECHNIQUES FOR CYTOLOGY

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Sophie Lane
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1 IMAGE SEGMENTATION AND RECOGNITION TECHNIQUES FOR CYTOLOGY Wojciech Bieniecki Tech. Univ. of Łódź, Computer Engineering Dept. Al. Politechniki 11, Łódź ABSTRACT This paper presents the process of segmentation of color biomedical images. We take into account microscopic cytological and histological images. Due to poor quality of the images and variety of their content, the segmentation is performed as the process that uses the combination of color discrimination, locally applied region based methods and object measurements. 1. IMAGE SEGMENTATION Segmentation is one of the most important steps in the process of image analysis. Its main goal is to divide an image into parts that have a strong correlation with objects or areas of the real world contained in the image. A complete segmentation defines a set of disjoint regions uniquely corresponding with objects in the input image, while a partial segmentation divides an image area to regions not directly corresponding with objects but for example with the classes of objects. Applying segmentation needs to construct a classifier function for separating the regions with respect to one of their properties: absolute brightness, brightness gradient, color, shape, area or texture. Next, we are presenting main approaches to the process of segmentation. The easiest one is a global image approach. It assigns each pixel to one of defined classes: for example (objects A, objects B, background) without respect to object consistence. The technique is often based on thresholding the intensity level of each pixel. Finding an optimal range of intensity allows separating the background from the objects. In case of a color space, where the number of classes is bigger or even unknown, this color space can be clustered using for example mean shift [4] or nearest neighbor techniques. The more sophisticated method is an edge based segmentation that relies on edges found in the image by edge detection filters: Prewitt s, Sobel s, Canny [5] etc. It transforms these edges into chains describing objects boundaries. In case of blurred objects, or where the edges are insufficiently exposed this method often fails, and does not guarantee to find a complete, closed contour. Another group of techniques is called the region-based segmentation. Their goal is to partition the whole image area into regions; each containing one object. The techniques

are often classified into region splitting, region merging and watersheds [1].

The main idea is to start with some initial boundary shape and iteratively modify it by applying various shrink/expansion operations according to some energy function.

THE CHARACTERISTIC OF THE INPUT IMAGE There are a few types of objects to be distinguished in the picture (Fig.

2 are often classified into region splitting, region merging and watersheds [1]. Recently another segmentation method has been proposed, a connectivity-preserving relaxation-based segmentation, often referred to as the active contour model. The main idea is to start with some initial boundary shape and iteratively modify it by applying various shrink/expansion operations according to some energy function. There are some different approaches to this technique: snakes and ray propagation [10]. 2. THE CHARACTERISTIC OF THE INPUT IMAGE There are a few types of objects to be distinguished in the picture (Fig. 1): a positively reacted, a negatively reacted, a which reacted only partly, a which is not properly saturated with a dye, cytoplasm, fat tissue, nonnuclear tissue elements, other elements: air bubbles, pollutions, video artifacts, etc. Cytoplasm Seven jammed nuclei Other tissue elements Positively reacted Badly saturated Partly reacted Ne gatively reacted Air bubble Fig 1. A sample image from biopsy to be segmented and recognized. Several types of objects can be distinguished. Images, that we take into account are acquired from the microscope through a video camera then grabbed by a standard TV card and stored in a PC machine as 24 bit color BMP files. The resolution of the images is 700 per 525 pixels. Raw microscopic images taken in such a way need preprocessing in order to make the segmentation procedure possible. Some of their properties come from the nature of the images. A microscopic slide is a two-dimensional projection of three-dimensional reality and as such may bring about false information about sizes of objects (if, for example, two objects of equal size are sliced at different planes), which results in different shapes and areas. The phenomenon is called Holmes effect [11] (Fig.2). Moreover, the nuclei can overlap; some of them may be distorted or even damaged during the preparation process.

Fig. 2. Holmes effect: a result of 2D projection. Another problem characteristic to optical microscopic images is a significant amount of blurred objects, because of the optical limitations of lenses.

3 Fig. 2. Holmes effect: a result of 2D projection. Another problem characteristic to optical microscopic images is a significant amount of blurred objects, because of the optical limitations of lenses. The boundaries of objects, that are out of focus are more difficult to estimate, and that makes the edge based segmentation methods less usable. There are some methods [8] that may be used to reduce the effect based on composing the output image from a set of pictures of the same area taken in different focal adjustments. Unfortunately they do not work properly with microscopic images because they are partly transparent. In opposite to the usual photography, where the exposed objects reflect the light, the microscopic picture exists because the light rays come through the space that we observe and some of them are absorbed by the objects. The opacity of the nuclei is irregular, for instance, the centers of them may be more transparent than the background and of course than their edges. This fact is very important when we introduce threshold based segmentation techniques. Even if we capture a single frame of a static scene, the image is disturbed by an interlace effect. It originates from the method of scanning the frame by a camera the odd lines are not acquired in exactly the same moment that the even ones. This time shift may bring about image distortion. A de-interlace filter removes the odd lines and interpolates them with neighboring even lines. 3. THE ALGORITHM We tested various methods of color and morphological segmentation, including own modifications concerning the specifics of tested images. For color image, simple but efficient pattern recognition methods seem appropriate. We applied Nearest Neighbor methods [6], also with reference set reduction to achieve satisfactory performance for the cost of negligible accuracy loss. Another useful technique of segmentation is the watershed algorithm with its numerous mutations [1]. We have implemented own morphological segmentation algorithm based on the watershed technique. In the final step of image processing we deal with identification and quantitative analysis.

4 Image preprocessing and enhancement RGB HSI, grad(i) color space conversion Load the training set. Run Skalak s reduction k-nn classification of the pixels Region splitting based iterative segmentation The flow chart is presented in the Fig 3. Initially the image is being filtered to make the processing conditions uniform. Apart from histogram correction filters there are morphological operations as erosion and dilatation performed in a color space known as image closing [7]. Image closing filter reduces the granularity of the shapes which results in better performance of region based segmentation. Next we create for the image an additional table containing H, S, I and grad(i) values. This table will be used as a testing set for the k- NN method. Choosing a proper training is crucial for accurate recognition. We obtain it manually performing a human segmentation on another picture. Than the set is reduced by Skalak s method [9] to required cardinality and can be used for all other pictures. Fig. 3. The segmentation algorithm overview After the k-nn analysis the picture is quantized to three levels and there are three classes of points: belong to the background, belong to red (positively reacted) nuclei and belong to blue (negatively reacted) nuclei. At this point the second phase of analysis is run (Fig. 4). 1. Index the classified points, by running flooding procedure. Define the objects A 1...A m of connected A-class pixels, and B 1...B n as connected, disjoint sets of B-class points. 2. For each object A 1...A m, B 1...B n, compute the area S(A i ), S(B j ). Manually or by histogram analysis find an optimal area of each class object S 0 A and S 0 B. 3. For each object A i find a list L(A i ) of B-class objects connected to it. For each object B j find a list L(B j ) of A-class objects connected to it. 4. Assign objects of area less than ½ S 0 A for A-class and ½ S 0 B for B-class as small. Assign objects bigger than 3/2 S 0 A and 3/2 S 0 B as badly segmented. Assign other objects as segmented. Remove all small objects, which have empty lists L. 5. For each small object neighbor try to merge the objects. If there are more than one neighbor, find the one, which after merging will have the best area. The pixels of a glued object will change the class. 6. Estimate the number of real objects in badly segmented objects. 7. Perform a watershed transform on badly segmented objects. Remove iteratively the weakest walls in case of oversegmentation. Fig. 4. Region splitting iterative algorithm At first the neighboring points that belong to the same class are aggregated to the objects. The objects are supposed to be individual cell nuclei. In a real case some of them are well segmented nuclei, some due to classification errors should belong to the background (these are usually small shapes) and finally there is a big amount of overlapping (badly segmented) objects that should be split. There is also a small amount of small objects that belong to the partially saturated nuclei. They have to be merged to one object and classified to appropriate class.

object count 40 Object count 25 35 30 Probably well segmenteted nuclei 20 Probably w ell segmented nuclei 25 15 20 15 10 10 5 5 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 Area in pixels Red

5 object count 40 Object count Probably well segmenteted nuclei 20 Probably w ell segmented nuclei Area in pixels Red objects A rea in pixels Blue objects Fig. 5. Area distribution histograms for a sample segmented image After initial area measurement we have to evaluate the S 0 area, that supposedly contains one nuclei. It may be set experimentally or computed by analyzing the area distribution for a specific image (Fig. 6) Next step is to evaluate how many jammed cell nuclei may be present in shapes that do not satisfy the conditions. Fig. 6. Picture with watershed grid (on the left) and with weakest walls removal (on the right). For a rectangle surrounding such a shape we apply the watershed transform of the intensity value that creates a grid separating the objects. Such a transform very precisely indicates the object borders but also, as we can see in Fig. 5, draws too many splitting lines. Another drawback of this transform is a big computing cost for high dimensions. This is why we do not apply it for a whole image. Our implementation of the transform indicates the weakest watershed lines and can remove some of them to obtain a required

6 number of objects in a query subimage. 4. THE CONCLUSIONS We have tested our algorithm for a set of 70 pictures. Initially the photographs were analyzed manually by a specialist. Independently, we do the analysis using our software [2]. A statistical analysis of the results is presented in [3]. In the future, we are going to apply some edge detecting filters combined with watershed transform for image segmentation improvement. We also intend to check, if some shape measurements could help in proper object classifications. REFERENCES [1] S. Beucher and C. Lantuejoul, Use of watersheds in contour detection, in Proceedings of International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, Rennes, Sept [2] W. Bieniecki, Sz. Grabowski, J. Sekulska, M. Turant, A. Kaluzynski Automatic segmentation and recognition of patomorphological microscopic images, CADSM 2003 Feb. 2003, Lviv Slavsko Conference, pp [3] W. Bieniecki, Sz. Grabowski, J. Sekulska, A system for pathomorphological microscopic image analysis, this conference. [4] D. Comaniciu, P. Meer, Mean Shift Analysis: A Robust Approach Toward Feature Space Analysis, IEEE Transactions On Pattern Analysis and Computer Intelligence, vol. 24, no. 5, May [5] J. Canny, A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI 8, No. 6, pp [6] E. Fix and J. L. Hodges, Discriminatory Analysis: Nonparametric Discrimination Small Sample Performance, from project , Report Number 11, USAF School of Aviation Medicine, Randolph Field, Texas, pp , [7] G. Louverdis, I. Andreadis and Ph. Tsalides, Morphological Granulometries for Color Images 2nd Hellenic Conference on Artificial Intelligence, SETN Thes Saloniki, Greece, [8] J.M. Ogden, E.H. Adelson, J.R. Bergen, P.J. Burt, Pyramid-based Computer Graphics, RCA Engineer, Sept/Oct, [9] D. Skalak, Prototype and Feature Selection by Sampling and Random Mutation Hill-Climbing Algorithms, in Proc. Eleventh International Conference on Machine Learning, pp , New Brunswick, New Jersey, [10] H. Tek, D. Comaniciu, J.P. Williams, Vessel Detection by Mean Shift Based Ray Propagation, IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, Hawaii, [11] K. Zielinski, M. Strzelecki, Komputerowa analiza obrazu biomedycznego. PWN, W-wa, Lodz, 2002.

Multi-pass approach to adaptive thresholding based image segmentation

1 Multi-pass approach to adaptive thresholding based image segmentation Abstract - Thresholding is still one of the most common approaches to monochrome image segmentation. It often provides sufficient