School of Medical Science and Technology, IIT Kharagpur, Kharagpur, WB, 721302, India.
Advanced Technology Development Centre, IIT Kharagpur, Kharagpur, WB, 721302, India.
Int J Comput Assist Radiol Surg. 2017 Apr;12(4):539-552. doi: 10.1007/s11548-017-1520-x. Epub 2017 Jan 9.
Diffusion-weighted imaging (DWI) is a widely used medical imaging modality for diagnosis and monitoring of cerebral stroke. The identification of exact location of stroke lesion helps in perceiving its characteristics, an essential part of diagnosis and treatment planning. This task is challenging due to the typical shape of the stroke lesion. This paper proposes an efficient method for computer-aided delineation of stroke lesions from DWI images.
Proposed methodology comprises of three steps. At the initial step, image contrast has been improved by applying fuzzy intensifier leading to the better visual quality of the stroke lesion. In the following step, a two-class (stroke lesion area vs. non-stroke lesion area) segmentation technique based on Gaussian mixture model has been designed for the localization of stroke lesion. To eliminate the artifacts which would appear during segmentation process, a binary morphological post-processing through area operator has been defined for exact delineation of the lesion area.
The performance of the proposed methodology has been compared with the manually delineated images (ground truth) obtained from different experts, individually. Quantitative evaluation with respect to various performance measures (such as dice coefficient, Jaccard score, and correlation coefficient) shows the efficient performance of the proposed technique.
弥散加权成像（DWI）是一种广泛用于诊断和监测脑卒中的医学成像方式。准确识别脑卒中病灶的位置有助于了解其特征，这是诊断和治疗计划的重要组成部分。由于脑卒中病灶的典型形状，这项任务具有挑战性。本文提出了一种从 DWI 图像中自动勾画脑卒中病灶的有效方法。
所提出的方法包括三个步骤。在初始步骤中，通过应用模糊增强器来改善图像对比度，从而提高脑卒中病灶的视觉质量。在接下来的步骤中，设计了一种基于高斯混合模型的两分类（脑卒中病灶区域与非脑卒中病灶区域）分割技术，用于定位脑卒中病灶。为了消除分割过程中出现的伪影，通过面积算子定义了二进制形态后处理，以精确勾画病灶区域。
将所提出的方法的性能与不同专家手动勾画的图像（金标准）进行了个体比较。通过各种性能指标（如骰子系数、Jaccard 得分和相关系数）进行定量评估，表明了该技术的高效性能。
