Semiautomatic Snake-Based Segmentation ofSolid Breast Nodules on Ultrasonography

Miguel Aleman-Flores1, Patricia Aleman-Flores2, Luis Alvarez-Leon1,M. Belen Esteban-Sanchez1, Rafael Fuentes-Pavon2,

and Jose M. Santana-Montesdeoca2

1 Departamento de Informatica y Sistemas,Universidad de Las Palmas de Gran Canaria, 35017, Las Palmas, Spain

2 Seccion de Ecografıa, Servicio de Radiodiagnostico Hospital,Universitario Insular de Gran Canaria, 35016, Las Palmas, Spain

maleman@dis.ulpgc.es

Abstract. Ultrasonography plays a crucial role in the diagnosis ofbreast cancer. However, it is one of the most difficult types of images tosegment and analyze. The presence of speckle noise and low contrast ar-eas limits the success of most noise reduction filters and segmentation al-gorithms. In this paper, we propose a combination of different techniqueswhich provide quite satisfactory results in the segmentation of breast tu-mors on ultrasonography. It is performed in a semiautomatic way, whicheliminates the need for a manual delineation of the contour of the nodules.These techniques include the truncated median filter, a region-growing al-gorithm and active contours. Furthermore, this can be the initial phase foran exhaustive analysis of the diagnostic criteria in breast ultrasound.

1 Introduction

Medical imaging can be very useful for the early detection of breast cancer. Themost commonly used types of images are mammography and ultrasonography.For the latter case, radiologists have described a series of criteria which helpdeciding whether a solid breast nodule is malignant or benignant by analyzingultrasound images [1]. These factors involve a precise visual examination of theshape of the nodule and its contour. Thus, a measurement of how ellipsoid thenodule is, the extraction of the ramifications, or the location of microlobulations,angular margins and spiculations require the analysis of the whole contour orcertain parts of it. For this reason, it is very important to obtain an accuratedelimitation of the nodule boundary. Computer Vision techniques provide somemethods for the extraction of the contours, but, due to the special characteristicsof ultrasound images, they must be adapted to obtain satisfactory results. Thispaper presents a new approach for the segmentation of breast tumors usingactive contours, combined with other techniques, such as the truncated medianfilter or the structure tensor.

Several semiautomatic segmentation methods in ultrasound images have pre-viously been proposed. These methods include pixel and region-based segmenta-tion, edge-based segmentation and hybrid techniques [2][3]. The success of these

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Fig. 1. General scheme of the segmentation method: truncated median, structure ten-sor and a region-growing algorithm are used to extract an initial segmentation of thenodule, whereas geodesic active contours are applied to obtain a more precise contour

techniques is moderate because they cannot obtain a precise shape of the re-gion of interest due to the special characteristics of ultrasound images or therequirement of a priori knowledge of the shape.

Region-growing algorithms do not require an initial approximation, but pro-duce quite inaccurate segmentations. On the other hand, active contours tech-niques generate very satisfactory results provided the initialization is closeenough. This is the reason why we propose a combination of both kinds ofmethods. Due to the presence of speckle noise, a previous filtering of the imageis required. Figure 1 shows a general scheme of our method. The rest of thepaper is structured as follows: Section 2 explains how the initial segmentation isobtained. Section 3 presents the use of the active contours technique to improvethe segmentation. Finally, in Sect. 4, we give an account of our main conclusions.

2 Initial Segmentation

In order to reduce the speckle noise which characterizes ultrasound images, wehave used the truncated median filter [4]. This consists in an iterative filterwhich, for every pixel, approximates the mode of the region by means of themedian of the most representative values. In a few iterations, the image is muchmore suitable for the application of a region-growing algorithm. On the filteredimage, we apply two 3x3 masks to estimate the gradient in every pixel and, bymeans of the structure tensor, we obtain a better estimation of the magnitude ofthe gradient. From a seed point introduced by the specialist, the selection grows

Semiautomatic Snake-Based Segmentation of Solid Breast Nodules 469

Fig. 2. From top to bottom and from left to right: original image of a nodule inultrasonography, filtered image, magnitude of the gradient using the structure tensor,and initial segmentation by means of the region-growing algorithm

until a certain threshold for the magnitude of the gradient is reached. Figure 2shows an example of an ultrasound image of a nodule, the filtered image, themagnitude of the gradient, and the initial segmentation of the nodule obtainedthrough the region-growing algorithm.

3 Active Contours

The active contours technique [5][6], also called snakes, is recognized as one of themost efficient tools for image segmentation. This method consists in deforming aninitial contour of the object under a set of internal and external forces. Severaldifficulties appear when applying this model to ultrasound images because ofultrasound characteristic speckle noise, the requirement of an initial outline ofthe nodule boundary to start the algorithm, and the need for a good adequacyof the external forces which guide the snake.

In our case, once the region-growing algorithm has been applied, the contourof the selected area is considered as the initial snake for the evolution of theactive contours, based on the following equation:

∂u(x, y)∂t

= gσ(I(x, y)) |�u(x, y)| div

(�u(x, y)|�u(x, y)|

)+λ∇gσ(I(x, y))�u(x, y) (1)

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where u(x, y; 0) = u0(x, y) is the initial snake contour, λ ≥ 0, and gσ(I(x, y))represents the stopping function that attracts the snake u(x, y) to the real con-tour of the object. It is an edge detector that must be selected according to thecharacteristics of the image. A typical choice of gσ(I(x, y)) is:

gσ(I(x, y)) =1√

1 + α |∇Iσ(x, y)|2(2)

where α ≥ 0 is a constant and Iσ(x, y) denotes the smoothed version of the orig-inal image, i.e. I(x, y) has been convolved with a Gaussian kernel with standarddeviation σ. We have used a multiscale implementation based on the geodesicactive contours proposed in [6] and the level-set method proposed in [7]. Giventhe final standard deviation σ0 and the number of scales to apply Ns, the cor-responding standard deviations for the different scales are calculated accordingto the following expression:

σn = (n + 1)σ0 n = Ns − 1, .., 0

For a given scale k, the initial contour is given by the final contour for scale(k + 1), which has been calculated previously, except for the first case, in which

Fig. 3. Evolution of the snake: initial approximation obtained with the region-growingalgorithm and final snakes using three different scales (3σ0, 2σ0 and σ0)

Semiautomatic Snake-Based Segmentation of Solid Breast Nodules 471

Fig. 4. Comparison of initial contours (black) and final contours (white) in four differ-ent nodules

the segmentation provided by the region-growing algorithm is used as initialapproximation.

We have adapted snake adjustment from the classical model to the specialcharacteristics of ultrasonography, and we have compared the most represen-tative methods for speckle reduction in ultrasound images. Initially, the bestresults have been obtained using the truncated median filter and the geometricfilters [8], but the first one was faster.

Figure 3 shows the evolution of the snakes as the different scales and itera-tions are applied. The multiscale implementation allows adapting the values ofthe parameters α and λ at each scale. This results in a faster evolution withoutdecreasing the accuracy. Figure 4 shows a comparison of the initial and final seg-mentations. As observed, the active contours technique extracts the real contoursof the nodules in a more accurate way.

4 Conclusion

We have proposed a new method for the segmentation of breast tumors in ul-trasonography. The combination of a region-growing algorithm, guided by thestructure tensor, and the active contours have provided quite promising results.The use of a region-growing algorithm does not provide very accurate segmen-tations, but it generates an initial contour for the active contours technique, in

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such a way that only a single inner point is needed, instead of a large set ofcontour points or a whole delineation. We have tested the proposed scheme on aset of breast ultrasound images. The comparison of the results provided by thesystem and those supplied by the specialists through manual delineation showsthe accuracy and reliability of the proposed technique, and emphasizes its use-fulness for the further analysis of the diagnostic criteria used in the classificationof the nodules.

References

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