Rebouças Filho Pedro P, Sarmento Róger Moura, Holanda Gabriel Bandeira, de Alencar Lima Daniel
Laboratório de Processamento Digital de Imagens e Simulação Computacional (LAPISCO), Instituto de Federal de Educação, Ciência e Tecnologia do Ceará (IFCE), Maracanaú, CE, Brazil.
Comput Methods Programs Biomed. 2017 Sep;148:27-43. doi: 10.1016/j.cmpb.2017.06.011. Epub 2017 Jun 24.
Cerebral vascular accident (CVA), also known as stroke, is an important health problem worldwide and it affects 16 million people worldwide every year. About 30% of those that have a stroke die and 40% remain with serious physical limitations. However, recovery in the damaged region is possible if treatment is performed immediately. In the case of a stroke, Computed Tomography (CT) is the most appropriate technique to confirm the occurrence and to investigate its extent and severity. Stroke is an emergency problem for which early identification and measures are difficult; however, computer-aided diagnoses (CAD) can play an important role in obtaining information imperceptible to the human eye. Thus, this work proposes a new method for extracting features based on radiological density patterns of the brain, called Analysis of Brain Tissue Density (ABTD).
The proposed method is a specific approach applied to CT images to identify and classify the occurrence of stroke diseases. The evaluation of the results of the ABTD extractor proposed in this paper were compared with extractors already established in the literature, such as features from Gray-Level Co-Occurrence Matrix (GLCM), Local binary patterns (LBP), Central Moments (CM), Statistical Moments (SM), Hu's Moment (HM) and Zernike's Moments (ZM). Using a database of 420 CT images of the skull, each extractor was applied with the classifiers such as MLP, SVM, kNN, OPF and Bayesian to classify if a CT image represented a healthy brain or one with an ischemic or hemorrhagic stroke.
ABTD had the shortest extraction time and the highest average accuracy (99.30%) when combined with OPF using the Euclidean distance. Also, the average accuracy values for all classifiers were higher than 95%.
The relevance of the results demonstrated that the ABTD method is a useful algorithm to extract features that can potentially be integrated with CAD systems to assist in stroke diagnosis.
脑血管意外（CVA），又称中风，是全球范围内一个重要的健康问题，每年影响全球1600万人。中风患者中约30%死亡，40%留有严重的身体功能受限。然而，如果立即进行治疗，受损区域有可能恢复。对于中风，计算机断层扫描（CT）是确认其发生并研究其范围和严重程度的最合适技术。中风是一个早期识别和采取措施都很困难的紧急问题；然而，计算机辅助诊断（CAD）在获取人眼难以察觉的信息方面可以发挥重要作用。因此，这项工作提出了一种基于脑组织密度模式提取特征的新方法，称为脑组织密度分析（ABTD）。
所提出的方法是一种应用于CT图像以识别和分类中风疾病发生情况的特定方法。将本文提出的ABTD提取器的结果评估与文献中已有的提取器进行比较，如灰度共生矩阵（GLCM）特征、局部二值模式（LBP）、中心矩（CM）、统计矩（SM）、胡氏矩（HM）和泽尼克矩（ZM）。使用一个包含420张颅骨CT图像的数据库，将每个提取器与多层感知器（MLP）、支持向量机（SVM）、k近邻（kNN）算法、优化流形（OPF）和贝叶斯等分类器一起应用，以分类一张CT图像代表的是健康大脑还是患有缺血性或出血性中风的大脑。
当使用欧几里得距离与OPF结合时，ABTD的提取时间最短，平均准确率最高（99.30%）。此外，所有分类器的平均准确率值均高于95%。
结果的相关性表明，ABTD方法是一种有用的算法，可用于提取特征，这些特征有可能与CAD系统集成以辅助中风诊断。
