Sudarshan Vidya K, Acharya U Rajendra, Ng E Y K, Tan Ru San, Chou Siaw Meng, Ghista Dhanjoo N
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore; Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia.
Comput Biol Med. 2016 Apr 1;71:231-40. doi: 10.1016/j.compbiomed.2016.01.028. Epub 2016 Feb 9.
Cross-sectional view echocardiography is an efficient non-invasive diagnostic tool for characterizing Myocardial Infarction (MI) and stages of expansion leading to heart failure. An automated computer-aided technique of cross-sectional echocardiography feature assessment can aid clinicians in early and more reliable detection of MI patients before subsequent catastrophic post-MI medical conditions. Therefore, this paper proposes a novel Myocardial Infarction Index (MII) to discriminate infarcted and normal myocardium using features extracted from apical cross-sectional views of echocardiograms. The cross-sectional view of normal and MI echocardiography images are represented as textons using Maximum Responses (MR8) filter banks. Fractal Dimension (FD), Higher-Order Statistics (HOS), Hu's moments, Gabor Transform features, Fuzzy Entropy (FEnt), Energy, Local binary Pattern (LBP), Renyi's Entropy (REnt), Shannon's Entropy (ShEnt), and Kapur's Entropy (KEnt) features are extracted from textons. These features are ranked using t-test and fuzzy Max-Relevancy and Min-Redundancy (mRMR) ranking methods. Then, combinations of highly ranked features are used in the formulation and development of an integrated MII. This calculated novel MII is used to accurately and quickly detect infarcted myocardium by using one numerical value. Also, the highly ranked features are subjected to classification using different classifiers for the characterization of normal and MI LV ultrasound images using a minimum number of features. Our current technique is able to characterize MI with an average accuracy of 94.37%, sensitivity of 91.25% and specificity of 97.50% with 8 apical four chambers view features extracted from only single frame per patient making this a more reliable and accurate classification.
横断面超声心动图是一种用于表征心肌梗死（MI）以及导致心力衰竭的扩展阶段的高效非侵入性诊断工具。一种用于横断面超声心动图特征评估的自动化计算机辅助技术可以帮助临床医生在MI患者随后出现灾难性的MI后医疗状况之前进行早期且更可靠的检测。因此，本文提出了一种新颖的心肌梗死指数（MII），使用从超声心动图心尖横断面视图中提取的特征来区分梗死心肌和正常心肌。正常和MI超声心动图图像的横断面视图使用最大响应（MR8）滤波器组表示为纹理基元。从纹理基元中提取分形维数（FD）、高阶统计量（HOS）、Hu氏矩、Gabor变换特征、模糊熵（FEnt）、能量、局部二值模式（LBP）、雷尼熵（REnt）、香农熵（ShEnt）和卡普尔熵（KEnt）特征。使用t检验以及模糊最大相关性和最小冗余性（mRMR）排序方法对这些特征进行排序。然后，将排名靠前的特征组合用于综合MII的制定和开发。通过使用一个数值，这个计算出的新颖MII用于准确快速地检测梗死心肌。此外，使用不同的分类器对排名靠前的特征进行分类，以使用最少数量的特征来表征正常和MI左心室超声图像。我们当前的技术能够以94.37%的平均准确率、91.25%的灵敏度和97.50%的特异性来表征MI，从每位患者仅一帧中提取8个心尖四腔视图特征，这使得分类更加可靠和准确。
