Three-Dimensional Tumor Segmentation for Positron Emission Tomography Images

• Main Authors: Feng, Chen-Lin, 馮珍琳
• Other Authors: Dong, Lan-Rong
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/42677846297085721537

碩士 === 國立交通大學 === 生醫工程研究所 === 104 === With the non-invasive and high screening rates, Positron Emission Tomography image plays an important role in today's medical diagnosis and becomes a common diagnostic tool. However, in the production of radiation treatment plan, the doctor must spend a lot of effort determining the region of tumor. In order to reduce unnecessary labor costs, automated segmentation is significant to clinical diagnosis. It is difficult to segment PET images due to the low resolution, high variation of intensity and low signal to noise ratio. It is hard to detect the boundary between background and tumor with similar intensity as well. Especially for the image with small tumor, there is only a little hot spot in image. The fact that the edge of small tumor is often recognized as background causes the situation of insufficient segmentation. On the other hand, for the low contrast images, the background is often classified as a tumor and it causes the situation of over segmentation. It is because the intensity of tumor and surrounding tissue are too similar. We propose an algorithm for lung cancer which can adaptively adjust the size of VOI and low-interference with small tumor image and low contrast image. We use Kurtosis value to find the best two ranges of histogram which have the biggest similarity to Gaussian distribution. This algorithm can reduce the interference of background noise by adjusting the size of the VOI. It can also take advantage of Kurtosis to get the best distribution and then find the best threshold. The similarity between given result and ground truth defined by doctor reaches 80.38%. It also cost less time than most algorithms.