Comparison of Lung Tumor Segmentation Methods on PET Images

被引：0

作者：

Eset, Kubra ^{[1
]}

Icer, Semra ^{[1
]}

Karacavus, Seyhan ^{[2
]}

Yilmaz, Bulent ^{[3
]}

Kayaalti, Omer ^{[4
]}

Ayyildiz, Oguzhan ^{[1
,3
]}

Kaya, Eser ^{[5
]}

机构：

[1] Erciyes Univ, Biyomed Muhendisligi Bolumu, Melikgazi Kayseri, Turkey

[2] Bozok Univ, Tip Fak, Nukleer Tip AD, Yozgat, Turkey

[3] Abdullah Gul Univ, Elekt Elekt Muhendisligi, Kocasinan Kayseri, Turkey

[4] Erciyes Univ, Develi Huseyin Sahin MYO, Melikgazi Kayseri, Turkey

[5] Acibadem Kayseri Hastanesi, Nukleer Tip Bolumu, Melikgazi, Kayseri Provinc, Turkey

来源：

2015 MEDICAL TECHNOLOGIES NATIONAL CONFERENCE (TIPTEKNO) | 2015年

关键词：

segmentation; k-means; Otsu's tresholding; active contour; CANCER;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Lung cancer is the most common cause of cancer-related deaths that occur all over the world. Recently, various image processing approaches have been used on PET images in order to characterize the uniformity, density, coarseness, roughness, and regularity (i.e., texture properties) of the intratumoral F-18-fluorodeoxyglucose (FDG) uptake. The first and important step of this kind of analysis is to differentiate tumor region from other structures and background, which is called segmentation. In this study, k-means, active contour (snake), and Otsu's tresholding methods were applied on PET images obtained from 36 patients and the performances were compared by the nuclear medicine expert in our team. The results show that Otsu tresholding approach is more selective.

引用

页数：4

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