A New Technique of Flow Voids Segmentation on MRI Image for Cerebrovascular Disease

Amin Sabirin Tajudin, I. Isa, Z. H. C. Soh, S. N. Sulaiman, N. Karim, I. Shuaib
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引用次数: 1

Abstract

The flow voids is the condition occurs when the MRI image has lost its signal due to flow of bloods and other fluids such as cerebrospinal fluid (CSF) and urine. Generally, the MRI images particularly the vessels that contain vigorously flowing blood is seen low signal and this may reflect to vascular patency. Moreover, the manual delineation method to visually detect the flow voids is tedious and time consuming. Recently, an image processing technique such as watershed segmentation is most recommended technique to segment the MRI images of flow voids. A common watershed transformation used for segmentation is the marker-controlled segmentation, but the application of such method is limited particularly due to over-segmentation and sensitivity to the noise. Therefore, in order to overcome such limitations, this study is proposed a new scheme of improved technique to segment flow voids image based on watershed and k-means segmentation algorithms. The proposed technique that involves pre-processing process and the improved watershed segmentation algorithm is used to capture the flow voids in the MRI images. The performance of the proposed technique is measured by evaluating its accuracy to detect flow-voids and hence the results are compared to the golden standard results provided by manual delineation method. The proposed segmentation technique reveals that it is has highly suffice to reduce over-segmentation detection of flow voids in the MRI images with accuracy up to 90%. From the comparison results, it is also shows that the new proposed has potential to be used as pre-processing tools for radiologists in the future.
脑血管疾病MRI图像血流空隙分割新技术
流动空洞是由于血液和其他液体(如脑脊液和尿液)的流动而导致MRI图像失去信号的情况。一般来说,MRI图像,特别是含有大量血流的血管,可以看到低信号,这可能反映血管通畅。此外,人工圈定方法在视觉上检测流空洞是繁琐且耗时的。近年来,分水岭分割等图像处理技术被广泛应用于MRI流腔图像的分割。一种常用的分水岭变换用于分割是标记控制的分割,但这种方法的应用受到限制,特别是由于过度分割和对噪声的敏感性。因此,为了克服这些局限性,本研究提出了一种基于分水岭和k-means分割算法的流腔图像分割改进技术的新方案。该方法采用预处理和改进的分水岭分割算法来捕获MRI图像中的流动空洞。通过评估其检测流隙的准确性来衡量所提出的技术的性能,从而将结果与人工描绘方法提供的金标准结果进行比较。结果表明,该分割技术能够有效地减少MRI图像中流动空洞的过分割检测,分割精度可达90%。对比结果也表明,新提出的方法在未来有可能被用作放射科医生的预处理工具。
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