Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm
Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm
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DOI：
10.3390/bdcc3020027
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发表时间：
2019-06-01
影响因子：
3.7
通讯作者：
Miah, Md Sipon
中科院分区：
文献类型：
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作者：
Alam, Md Shahariar;Rahman, Md Mahbubur;Miah, Md Sipon
近几十年来，脑肿瘤的检测已成为医学领域最具挑战性的问题之一。在本文中，我们提出了一个包含基于模板的K均值和改进的模糊C均值（TKFCM）算法的模型，用于在磁共振成像（MRI）图像中检测人类脑肿瘤。该算法首先利用基于模板的K-means算法，根据图像的灰度强度，通过对模板的完美选择，实现对分割的有效初始化；其次，利用模糊c均值（fuzzy C-means， FCM）算法在接近最佳结果时，根据聚类质心到聚类数据点的距离确定更新后的隶属度，最后，利用改进的FCM聚类算法，根据肿瘤图像的对比度、能量、不相似度、同质性、熵和相关性等不同特征，更新得到的隶属度函数，进行肿瘤位置检测。仿真结果表明，在灰度强度较小的情况下，该算法能较好地检测出人脑中的异常组织和正常组织。此外，与其他算法的几分钟相比，该算法可以在很短的时间内检测到人类脑肿瘤——几秒钟。
In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human brain tumors in a magnetic resonance imaging (MRI) image. In this proposed algorithm, firstly, the template-based K-means algorithm is used to initialize segmentation significantly through the perfect selection of a template, based on gray-level intensity of image; secondly, the updated membership is determined by the distances from cluster centroid to cluster data points using the fuzzy C-means (FCM) algorithm while it contacts its best result, and finally, the improved FCM clustering algorithm is used for detecting tumor position by updating membership function that is obtained based on the different features of tumor image including Contrast, Energy, Dissimilarity, Homogeneity, Entropy, and Correlation. Simulation results show that the proposed algorithm achieves better detection of abnormal and normal tissues in the human brain under small detachment of gray-level intensity. In addition, this algorithm detects human brain tumors within a very short time-in seconds compared to minutes with other algorithms.