تقرير
A Time Series Graph Cut Image Segmentation Scheme for Liver Tumors
| العنوان: | A Time Series Graph Cut Image Segmentation Scheme for Liver Tumors |
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| المؤلفون: | Paxton, Laramie, Cao, Yufeng, Vixie, Kevin R., Wang, Yuan, Hobbs, Brian, Ng, Chaan |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Tumor detection in biomedical imaging is a time-consuming process for medical professionals and is not without errors. Thus in recent decades, researchers have developed algorithmic techniques for image processing using a wide variety of mathematical methods, such as statistical modeling, variational techniques, and machine learning. In this paper, we propose a semi-automatic method for liver segmentation of 2D CT scans into three labels denoting healthy, vessel, or tumor tissue based on graph cuts. First, we create a feature vector for each pixel in a novel way that consists of the 59 intensity values in the time series data and propose a simplified perimeter cost term in the energy functional. We normalize the data and perimeter terms in the functional to expedite the graph cut without having to optimize the scaling parameter $\lambda$. In place of a training process, predetermined tissue means are computed based on sample regions identified by expert radiologists. The proposed method also has the advantage of being relatively simple to implement computationally. It was evaluated against the ground truth on a clinical CT dataset of 10 tumors and yielded segmentations with a mean Dice similarity coefficient (DSC) of .77 and mean volume overlap error (VOE) of 36.7%. The average processing time was 1.25 minutes per slice. Comment: Image processing; image analysis; medical imaging |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1809.05210 |
| رقم الأكسشن: | edsarx.1809.05210 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |