Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, andill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

中文翻译：

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使用新的 Harris Hawk 优化方法和主动轮廓模型自动分割医学图像

医学图像分割是辅助诊断多种疾病的关键步骤，分割方法的准确性对于不同疾病的进一步治疗至关重要。不同的医学成像方式面临着不同的挑战，例如强度不均匀、噪声、对比度低和边界不明确，这使得自动分割成为一项艰巨的任务。为了解决这些问题，我们提出了一种新的全自动医学图像分割方法，它利用了阈值和活动轮廓模型的优势。在这项研究中，Harris Hawks 优化器用于确定最佳阈值，用于获得用于分割的初始轮廓。通过在活动轮廓模型中使用空间变化的高斯核来进一步细化获得的轮廓。然后使用标准皮肤数据集 (ISBI 2016) 验证所提出的方法，该数据集由可变大小的病变和不同的具有挑战性的伪影组成，以及标准心脏磁共振数据集 (ACDC, MICCAI 2017) 具有广泛的正常心脏、先天性心脏病和心功能不全。实验结果表明，与其他最先进的算法相比，所提出的方法可以有效地分割感兴趣区域，并为皮肤（整体 Dice 得分 0.90）和心脏数据集（整体 Dice 得分 0.93）产生出色的分割结果。和心功能不全。实验结果表明，与其他最先进的算法相比，所提出的方法可以有效地分割感兴趣区域，并为皮肤（整体 Dice 得分 0.90）和心脏数据集（整体 Dice 得分 0.93）产生出色的分割结果。和心功能不全。实验结果表明，与其他最先进的算法相比，所提出的方法可以有效地分割感兴趣区域，并为皮肤（整体 Dice 得分 0.90）和心脏数据集（整体 Dice 得分 0.93）产生出色的分割结果。