Lung nodule segmentation is an interesting research topic, and it serves as an effective solution for the diagnosis of Lung cancer. The existing methods of lung nodule segmentation suffer from accuracy issues due to the heterogeneity of the nodules in the lungs and the presence of visual deviations in the nodules. Thus, there is a requirement for an effective lung nodule segmentation, which assists the physicians in making accurate decisions. Accordingly, this study proposes a lung nodule segmentation process based on the kernel-based Bayesian fuzzy clustering (BFC), which is the integration of kernel functions in the BFC. Initially, the input computed tomography image is pre-processed for ensuring the effective segmentation, and the lobes are identified using the adaptive thresholding strategy. Then, the dominant areas in the lobes are identified using a scale-invariant feature transform descriptor, and the significant nodules are extracted using the grid-based segmentation. Finally, the lung nodules are segmented using the proposed kernel-based BFC. The proposed algorithm is evaluated using the Lung Image Database Consortium and Image Database Resource Initiative database, and it acquires the accuracy, sensitivity, and false positive rate of 0.955, 0.999, and 0.025, respectively.

中文翻译：

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基于核的贝叶斯聚类计算机断层扫描图像用于肺结节分割

肺结节分割是一个有趣的研究课题，它是诊断肺癌的有效方法。由于肺中结节的异质性和结节中视觉偏差的存在，肺结节分割的现有方法存在准确性问题。因此，需要有效的肺结节分割，这有助于医师做出准确的决定。因此，本研究提出了一种基于核的贝叶斯模糊聚类（BFC）的肺结节分割过程，该过程是BFC中核功能的集成。最初，对输入的计算机断层扫描图像进行预处理以确保有效分割，然后使用自适应阈值化策略识别叶瓣。然后，使用尺度不变特征变换描述符识别叶中的优势区域，并使用基于网格的分割来提取显着的结节。最后，使用提出的基于核的BFC分割肺结节。使用肺图像数据库协会和图像数据库资源倡议数据库对提出的算法进行了评估，该算法分别获得了0.955、0.999和0.025的准确性，灵敏度和假阳性率。