Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation
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Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation Shoulin Yin 1
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& Hang Li & Desheng Liu & Shahid Karim
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Received: 13 November 2019 / Revised: 3 August 2020 / Accepted: 13 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Currently, medical image segmentation has attracted more attention from researchers, which can assist in medical diagnosis. However, in the process of traditional medical image segmentation, it is sensitive to the initial contour and noise, which is difficult to deal with the weak edge image, complex iterative process. In this paper, we propose a new medical image segmentation method, which adopts density-oriented BIRCH (balanced iterative reducing and clustering using hierarchies) clustering method to modify active contour model and improve the robustness of noise. The BIRCH is a multi-stage clustering method using clustering feature tree. The improved model can effectively deal with the gray non-uniformity of real medical images. And we also introduce a new energy function in active contour model to make the contour curve approach to the edge, and finally stay at the edge of the image to complete the object segmentation. Experimental results show that this new model can overcome the influence of complex background on medical image segmentation and improve the speed and accuracy of medical segmentation results. Keywords Medical image segmentation . Density-oriented BIRCH . Active contour model . Energy function
* Hang Li [email protected] Shoulin Yin [email protected] Desheng Liu [email protected] Shahid Karim [email protected] Extended author information available on the last page of the article
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1 Introduction With the development of medical imaging technology, biomedical image segmentation is becoming more and more important in clinical diagnosis and treatment [15]. Active contour models are widely used in medical image segmentation due to the complex grayscale of medical images and the variety of topological structures. In order to better deal with the problems in the field of image segmentation, especially for medical image segmentation, the level set method is introduced, which greatly improves the difficulty of complex image processing [11, 33]. Nowadays, the level set method is widely used in image processing and other fields, and the edge set, region level set image segmentation algorithms are proposed. The boundary-based active contour model is mainly derived from image gradient information, so it is sensitive to noise, initial contour position and gray level inhomogeneity. The regionbased active contour model uses regional information to reduce the sensitivity to noise, effectively overcome the above deficiencies. As for the non-uniformity of image grayscale, the LBF (Local Binary Fitting) model based on Local area Fitting is proposed [19]. Although the image presents uneven gray level in the global scope, the gray level changes slowly in the small
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