Sheejakumari V, Sankara Gomathi B
Department of Information Technology, Rajaas Engineering College, Tirunelveli, Vadakkangulam, Tamil Nadu 627116, India.
Department of Electronics & Instrumentation Engineering, National Engineering College, Kovilpatti, Tamil Nadu 628503, India.
Comput Math Methods Med. 2015;2015:807826. doi: 10.1155/2015/807826. Epub 2015 Apr 22.
The advantages of magnetic resonance imaging (MRI) over other diagnostic imaging modalities are its higher spatial resolution and its better discrimination of soft tissue. In the previous tissues classification method, the healthy and pathological tissues are classified from the MRI brain images using HGANN. But the method lacks sensitivity and accuracy measures. The classification method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new classification method is proposed in this paper. Here, new tissues classification method is proposed with improved particle swarm optimization (IPSO) technique to classify the healthy and pathological tissues from the given MRI images. Our proposed classification method includes the same four stages, namely, tissue segmentation, feature extraction, heuristic feature selection, and tissue classification. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of the proposed classification method in classifying the tissues and the achieved improvement in sensitivity and accuracy measures. Furthermore, the performance of the proposed technique is evaluated by comparing it with the other segmentation methods.
磁共振成像（MRI）相较于其他诊断成像方式的优势在于其更高的空间分辨率以及对软组织更好的辨别能力。在先前的组织分类方法中，使用HGANN从MRI脑部图像中对健康组织和病理组织进行分类。但该方法缺乏敏感性和准确性度量。就这两个参数而言，该分类方法的性能并不充分。因此，为避免这些缺点，本文提出了一种新的分类方法。在此，提出了一种新的组织分类方法，采用改进的粒子群优化（IPSO）技术从给定的MRI图像中对健康组织和病理组织进行分类。我们提出的分类方法包括相同的四个阶段，即组织分割、特征提取、启发式特征选择和组织分类。该方法得以实施，并根据各种统计性能度量对结果进行分析。结果表明所提出的分类方法在组织分类方面的有效性以及在敏感性和准确性度量方面所取得的改进。此外，通过将所提出的技术与其他分割方法进行比较来评估其性能。
