Tahir Bilal, Iqbal Sajid, Usman Ghani Khan M, Saba Tanzila, Mehmood Zahid, Anjum Adeel, Mahmood Toqeer
Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan.
Department of Computer Science, Bahauddin Zakariya University, Multan, Pakistan.
Microsc Res Tech. 2019 Jun;82(6):803-811. doi: 10.1002/jemt.23224. Epub 2019 Feb 15.
Automatic medical image analysis is one of the key tasks being used by the medical community for disease diagnosis and treatment planning. Statistical methods are the major algorithms used and consist of few steps including preprocessing, feature extraction, segmentation, and classification. Performance of such statistical methods is an important factor for their successful adaptation. The results of these algorithms depend on the quality of images fed to the processing pipeline: better the images, higher the results. Preprocessing is the pipeline phase that attempts to improve the quality of images before applying the chosen statistical method. In this work, popular preprocessing techniques are investigated from different perspectives where these preprocessing techniques are grouped into three main categories: noise removal, contrast enhancement, and edge detection. All possible combinations of these techniques are formed and applied on different image sets which are then passed to a predefined pipeline of feature extraction, segmentation, and classification. Classification results are calculated using three different measures: accuracy, sensitivity, and specificity while segmentation results are calculated using dice similarity score. Statistics of five high scoring combinations are reported for each data set. Experimental results show that application of proper preprocessing techniques could improve the classification and segmentation results to a greater extent. However, the combinations of these techniques depend on the characteristics and type of data set used.
自动医学图像分析是医学界用于疾病诊断和治疗规划的关键任务之一。统计方法是主要使用的算法，由预处理、特征提取、分割和分类等几个步骤组成。此类统计方法的性能是其成功应用的重要因素。这些算法的结果取决于输入到处理管道的图像质量：图像质量越好，结果越高。预处理是在应用所选统计方法之前尝试提高图像质量的管道阶段。在这项工作中，从不同角度研究了流行的预处理技术，这些预处理技术分为三大类：去噪、对比度增强和边缘检测。形成这些技术的所有可能组合并应用于不同的图像集，然后将这些图像集传递到特征提取、分割和分类的预定义管道。使用三种不同的度量来计算分类结果：准确率、灵敏度和特异性，而使用骰子相似性分数来计算分割结果。报告了每个数据集的五个高分组合的统计数据。实验结果表明，应用适当的预处理技术可以在更大程度上提高分类和分割结果。然而，这些技术的组合取决于所使用数据集的特征和类型。
