A Region-based Level Set Formulation Using Machine Learning Approach in Medical Image Segmentation

A new region-based active contour model in level set formulation is proposed to segment medical images with poorly defined boundaries. From literature, it is observed that the traditional methods often fail to detect weak boundaries for images with intensity inhomogeneity. However, the machine learning (ML) algorithms are highly effective for such images but due to the noise most pixels are misclassified. Therefore in this paper, we propose a region-based active contour model using ML. In this paper, we consider an active contour driven by local Gaussian distribution (LGD) fitting energy which is known as LGD model. Further, this active contour LGD model is integrated with fuzzy k-nearest neighbor (k-NN) for added accurate segmentation. Also the energy stop function (ESF) of LGD model is modified to combine with k-NN. The results obtained are compared with the existing state-of-the-art models and the proposed method is clearly a triumph. The experimental results proves that the proposed model provides higher accuracy results for medical image segmentation and is robust compared to the other existing methods.