Gradient vector flow with mean shift for skin lesion segmentation

https://doi.org/10.1016/j.compmedimag.2010.08.002Get rights and content

Abstract

Image segmentation is an important task in the analysis of dermoscopy images since the extraction of skin lesion borders provides important cues for accurate diagnosis. In recent years, gradient vector flow based algorithms have demonstrated their merits in image segmentation. However, due to the compromise of internal and external energy forces within the partial differential equation these methods commonly lead to under- or over-segmentation problems. In this paper, we introduce a new mean shift based gradient vector flow (GVF) algorithm that drives the internal/external energies towards the correct direction. The proposed segmentation method incorporates a mean shift operation within the standard GVF cost function. Theoretical analysis proves that the proposed algorithm converges rapidly, while experimental results on a large set of diverse dermoscopy images demonstrate that the presented method accurately determines skin lesion borders in dermoscopy images.

Introduction

Malignant melanoma, the most deadly form of skin cancer, is one of the most rapidly increasing cancers in the world, with an estimated incidence of 68,720 and an estimated total of 8650 deaths in the United States in 2009 alone [1]. Early diagnosis is particularly important since melanoma can be cured with a simple excision if detected early.

Dermoscopy, one of the major tools for the diagnosis of melanoma, is a non-invasive skin imaging technique that involves optical magnification which makes sub-surface structures more readily visible compared to conventional clinical images [2]. This in turn reduces screening errors and provides greater differentiation between difficult lesions such as pigmented Spitz nevi and small, clinically equivocal lesions [3]. However, it has also been demonstrated that dermoscopy might lower the diagnostic accuracy in the hands of inexperienced dermatologists [4]. Therefore, in order to minimise diagnostic errors resulting from the difficulty and subjectivity of visual interpretation, the development of computerised image analysis techniques is of paramount importance [5].

Automatic border detection of lesions is often the first step in the automated or semi-automated analysis of dermoscopy images and is crucial for accurate diagnosis [6]. Image segmentation can be defined as the grouping of similar pixels (i.e. lesion and non-lesion pixels) in a parametric space. Segmentation algorithms include balloons [7], distance potential force [8], diffusion snakes [9], gradient vector flow (GVF) [10] and its generalisation [11] and further developments [12], [13]. GVF and its variants have been shown to work well by attracting the active contour towards object boundaries from a relatively large distance, while being capable of converging to object cavities. In recent years, numerous efforts have been made to provide potential solutions towards capture range or/and topological change problems. For example, a graph theory based approach was introduced by Li et al. [14] within the external force term in the snake model to perform automatic snake initialisation or splitting. Chuang and Lie [15] presented a downstream algorithm based on an extended GVF field model, where the downstream process starts with a set of seeds scored and selected by considering local gradient direction information around each pixel. Yang et al. [16] proposed a robust colour GVF snake model which combined robust estimation and colour gradients using a L2E robust estimation. Vasilevskiy and Siddiqi [17] demonstrated a gradient flow model which can be used to maximise the rate of increase of flux of a vector field in a two- or three-dimensional domain. The main contribution of this work is the direction of the vector field along with its magnitudes. Paragios et al. [13] proposed an edge driven bi-direction geometric flow for boundary detection by combining the geodesic active contour flow [18] and the gradient vector flow model [10].

In this paper, we propose a new type of dynamic energy force for snakes by combining local GVFs with a mean shift strategy. The energy force starts with the calculation of force vectors in the image domain. The deformation of the region surrounded by the evolving boundary is constrained by the mean shift of the pixels in the region. In other words, the evolution of the contour is not only driven by the gradient vector flows but also by the cumulative energy of the image region. This extended mean shift based GVF algorithm is versatile and flexible in that both local and global energy minimisation are achieved, leading to correct convergence against a severely noisy background.

The rest of the paper is organised as follows: In Section 2, the original GVF algorithm and its variants are introduced and discussed. Our proposed mean shift based GVF approach is described in Section 3. Section 4 presents extensive comparative results of the proposed scheme and conventional approaches. Finally, conclusions and future directions are given in Section 5.

Section snippets

GVF image segmentation

Snake (active contour) algorithms are used to detect object boundaries or edges given an initial guess of the evolving contours. The classical snake model considers a combination of internal and external energy, in which the boundary will stop evolving on the compromise of the two energy interactions. The internal energy term maintains smoothness and compactness of the curve shape, while the external energy term tunes the curve in order to be consistent with the inherent image gradients.

Problem formulation

When the GVF snake is finally settled, where the internal and external forces are balanced, one can have the Euler equation, expressed asαC(s)βC(s)+γV=0,where α and β are the weighting parameters that are used to control the strength of the snake's tension and rigidity respectively, γ is a proportional coefficient and V is the external force. Practically, these three parameters are set to be constants within the equation. C(s) is the contour that delineates the desired boundaries, and s  

Experimental work

The proposed segmentation algorithm was evaluated on a set of 100 dermoscopy images (30 invasive malignant melanoma and 70 benign) obtained from the EDRA Interactive Atlas of Dermoscopy [2] and the dermatology practices of Dr. A. Marghoob (New York, NY), Dr. H. Rabinovitz (Plantation, FL) and Dr. S. Meznies (Sydney, Australia). The benign lesions included nevocellular nevi and dysplastic nevi. A subset of the images is shown in Fig. 3. Manual borders were obtained by selecting a number of

Conclusions

GVF based algorithms have been frequently used to segment medical images, but also need further development to improve segmentation accuracy. In this paper we have introduced a new mean shift based GVF segmentation algorithm for segmenting skin lesions in dermoscopy images. The proposed method incorporates a mean field term within the standard GVF objective function. Experimental results on a large dataset of 100 dermoscopy images have shown that the proposed segmentation technique is capable

Acknowledgments

The authors would like to thank the anonymous reviewers for constructive comments that helped in improving the quality of this manuscript.

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