Senthil Kumar K, Venkatalakshmi K, Karthikeyan K
Assistant Professor, Department of Electrical and Electronics Engineering, University College of Engineering, Arni, India.
Assistant Professor, Department of Electronics and Communication Engineering, University College of Engineering Tindivanam, Tindivanam, India.
Comput Math Methods Med. 2019 Jan 8;2019:4909846. doi: 10.1155/2019/4909846. eCollection 2019.
The objective of this paper is to explore an expedient image segmentation algorithm for medical images to curtail the physicians' interpretation of computer tomography (CT) scan images. Modern medical imaging modalities generate large images that are extremely grim to analyze manually. The consequences of segmentation algorithms rely on the exactitude and convergence time. At this moment, there is a compelling necessity to explore and implement new evolutionary algorithms to solve the problems associated with medical image segmentation. Lung cancer is the frequently diagnosed cancer across the world among men. Early detection of lung cancer navigates towards apposite treatment to save human lives. CT is one of the modest medical imaging methods to diagnose the lung cancer. In the present study, the performance of five optimization algorithms, namely, -means clustering, -median clustering, particle swarm optimization, inertia-weighted particle swarm optimization, and guaranteed convergence particle swarm optimization (GCPSO), to extract the tumor from the lung image has been implemented and analyzed. The performance of median, adaptive median, and average filters in the preprocessing stage was compared, and it was proved that the adaptive median filter is most suitable for medical CT images. Furthermore, the image contrast is enhanced by using adaptive histogram equalization. The preprocessed image with improved quality is subject to four algorithms. The practical results are verified for 20 sample images of the lung using MATLAB, and it was observed that the GCPSO has the highest accuracy of 95.89%.
本文的目的是探索一种用于医学图像的便捷图像分割算法，以减少医生对计算机断层扫描（CT）图像的解读。现代医学成像方式会生成大量图像，手动分析极为困难。分割算法的结果取决于准确性和收敛时间。目前，迫切需要探索和实施新的进化算法来解决与医学图像分割相关的问题。肺癌是全球男性中最常被诊断出的癌症。早期发现肺癌有助于采取恰当的治疗措施以挽救生命。CT是诊断肺癌的较为常用的医学成像方法之一。在本研究中，已实施并分析了五种优化算法，即均值聚类、中值聚类、粒子群优化、惯性加权粒子群优化和保证收敛粒子群优化（GCPSO）从肺部图像中提取肿瘤的性能。比较了预处理阶段中值、自适应中值和均值滤波器的性能，结果证明自适应中值滤波器最适合医学CT图像。此外，通过使用自适应直方图均衡化来增强图像对比度。对经过质量改进的预处理图像应用四种算法。使用MATLAB对20幅肺部样本图像的实际结果进行了验证，结果发现GCPSO的准确率最高，为95.89%。
