According to the statistic from World Health Organization (WHO), stroke is one of the major causes of death globally. Computed tomography (CT) scan is one of the main medical diagnosis system used for diagnosis of ischemic stroke. CT scan provides brain images in Digital Imaging and Communication in Medicine (DICOM) format. The presentation of CT brain images is mainly relied on the window setting (window center and window width), which converts an image from DICOM format into normal grayscale format. Nevertheless, the ordinary window parameter could not deliver a proper contrast on CT brain images for ischemic stroke detection. In this paper, a new proposed method namely gamma correction extreme-level eliminating with weighting distribution (GCELEWD) is implemented to improve the contrast on CT brain images. GCELEWD is capable of highlighting the hypodense region for diagnosis of ischemic stroke. The performance of this new proposed technique, GCELEWD, is compared with four of the existing contrast enhancement technique such as brightness preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), extreme-level eliminating histogram equalization (ELEHE), and adaptive gamma correction with weighting distribution (AGCWD). GCELEWD shows better visualization for ischemic stroke detection and higher values with image quality assessment (IQA) module. SCANNING 38:842-856, 2016. © 2016 Wiley Periodicals, Inc.

中文翻译：

---

使用加权分布的伽马校正极值消除脑早期梗塞检测

根据世界卫生组织（WHO）的统计，中风是全球主要的死亡原因之一。计算机断层扫描（CT）扫描是用于诊断缺血性脑卒中的主要医学诊断系统之一。CT 扫描提供医学数字成像和通信 (DICOM) 格式的大脑图像。CT脑部图像的呈现主要依靠窗口设置（窗口中心和窗口宽度），将图像从DICOM格式转换为正常灰度格式。然而，普通的窗口参数不能在用于缺血性中风检测的 CT 脑图像上提供适当的对比度。在本文中，实施了一种新提出的方法，即带权重分布的伽马校正极值消除（GCELEWD）方法，以提高 CT 脑图像的对比度。GCELEWD 能够突出低密度区域以诊断缺血性中风。将这种新提出的技术 GCELEWD 的性能与现有的四种对比度增强技术进行了比较，例如亮度保持双直方图均衡化 (BBHE)、二元子图像直方图均衡化 (DSIHE)、极值消除直方图均衡化 (ELEHE) )，以及带权重分布的自适应伽马校正 (AGCWD)。GCELEWD 显示了更好的缺血性中风检测可视化和更高的图像质量评估 (IQA) 模块值。扫描 38:842-856, 2016. © 2016 Wiley Periodicals, Inc. 与现有的四种对比度增强技术，如亮度保持双直方图均衡化（BBHE）、二元子图像直方图均衡化（DSIHE）、极值消除直方图均衡化（ELEHE）和带权重分布的自适应伽马校正（ AGCWD）。GCELEWD 显示了更好的缺血性中风检测可视化和更高的图像质量评估 (IQA) 模块值。扫描 38:842-856, 2016. © 2016 Wiley Periodicals, Inc. 与现有的四种对比度增强技术，如亮度保持双直方图均衡化（BBHE）、二元子图像直方图均衡化（DSIHE）、极值消除直方图均衡化（ELEHE）和带权重分布的自适应伽马校正（ AGCWD）。GCELEWD 显示了更好的缺血性中风检测可视化和更高的图像质量评估 (IQA) 模块值。扫描 38:842-856, 2016. © 2016 Wiley Periodicals, Inc.