Department of Computer & Information Sciences, PIEAS, P.O. Nilore, Islamabad, Pakistan.
Comput Methods Programs Biomed. 2012 Dec;108(3):1261-76. doi: 10.1016/j.cmpb.2012.08.011. Epub 2012 Sep 14.
Disease diagnosis based on ultrasound imaging is popular because of its non-invasive nature. However, ultrasound imaging system produces low quality images due to the presence of spackle noise and wave interferences. This shortcoming requires a considerable effort from experts to diagnose a disease from the carotid artery ultrasound images. Image segmentation is one of the techniques, which can help efficiently in diagnosing a disease from the carotid artery ultrasound images. Most of the pixels in an image are highly correlated. Considering the spatial information of surrounding pixels in the process of image segmentation may further improve the results. When data is highly correlated, one pixel may belong to more than one clusters with different degree of membership. In this paper, we present an image segmentation technique namely improved spatial fuzzy c-means and an ensemble clustering approach for carotid artery ultrasound images to identify the presence of plaque. Spatial, wavelets and gray level co-occurrence matrix (GLCM) features are extracted from carotid artery ultrasound images. Redundant and less important features are removed from the features set using genetic search process. Finally, segmentation process is performed on optimal or reduced features. Ensemble clustering with reduced feature set outperforms with respect to segmentation time as well as clustering accuracy. Intima-media thickness (IMT) is measured from the images segmented by the proposed approach. Based on IMT measured values, Multi-Layer Back-Propagation Neural Networks (MLBPNN) is used to classify the images into normal or abnormal. Experimental results show the learning capability of MLBPNN classifier and validate the effectiveness of our proposed technique. The proposed approach of segmentation and classification of carotid artery ultrasound images seems to be very useful for detection of plaque in carotid artery.
基于超声成象的疾病诊断因其无创性而广受欢迎。然而，由于存在斑点噪声和波干扰，超声成像系统产生的图像质量较低。由于需要专家付出相当大的努力才能从颈动脉超声图像中诊断出疾病，因此存在这种缺点。图像分割是一种可以帮助从颈动脉超声图像中有效诊断疾病的技术之一。图像中的大多数像素都是高度相关的。在图像分割过程中考虑周围像素的空间信息可能会进一步提高结果。当数据高度相关时，一个像素可能属于不同程度成员的多个聚类。在本文中，我们提出了一种图像分割技术，即改进的空间模糊 c 均值和颈动脉超声图像的集成聚类方法，以识别斑块的存在。从颈动脉超声图像中提取空间、小波和灰度共生矩阵（GLCM）特征。使用遗传搜索过程从特征集中去除冗余和不重要的特征。最后，在最优或减少的特征上执行分割过程。具有减少特征集的集成聚类在分割时间和聚类精度方面表现更好。从所提出的方法分割的图像中测量内中膜厚度（IMT）。基于测量的 IMT 值，使用多层反向传播神经网络（MLBPNN）将图像分类为正常或异常。实验结果显示了 MLBPNN 分类器的学习能力，并验证了我们提出的技术的有效性。颈动脉超声图像的分割和分类方法似乎对检测颈动脉斑块非常有用。
