應用模糊分類演算法、區域成長及專業知識技術於自動化腦膜瘤之磁振影像分割

Automatic Segmentation of non-contrast-enhanced Meningioma in MR Images Using Combined Fuzzy c-Means, Region Growing and Knowledge-Based Techniques

劉怡旻(Yi-Min Liu)

指導教授 ： 翁昭旼

共同指導教授 ： 蔣以仁(I-Jen Chiang)

國立臺灣大學/工學院/醫學工程學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.00984

摘要

近年來，磁振造影的影像由於其解析度高、沒有放射性傷害，而且對軟組織擁有高對比的特性，使其成為臨床上診斷腦部結構不可或缺的依據。同一種腦組織以磁振造影掃描後，會因為不同的遲豫時間得到不同強度的訊號，而在T1及T2影像上有不同的灰階表現，因此目前臨床上，常以這樣的特性來進行診斷。腦部組織的標定與體積測量，對腦部疾病的診治來說，是一件極為重要的工作，然而臨床上，人工標定腦瘤影像的方法，不僅困難度高，又耗費時間與人力，而且標定的結果往往會因為個人主觀的想法不同而產生誤差，所以一套自動化的腦瘤MR影像之分割是必須的。
為了讓病患免於承受顯影劑併發症的風險，並減輕醫師繁忙工作的負擔，此研究希望藉由不需要經過顯影劑增強的影像，來進行自動化的腦瘤標定與體積測量，研究一開始利用模糊c-平均值演算法結合多維度直方圖分析將磁振影像進行自動分群，再以區域成長，將種子區域鄰近的像素一併加入演算，並結合臨床上的專業知識基礎來篩選、剔除非腦瘤組織的群組，分成半自動與全自動的方式將腦瘤影像選取出來並合併，最後再加入形態學的影像處理技術，得到更完整的分割結果。將研究結果與醫師人工標定的腫瘤影像量化後，可評估系統之優劣。
綜合以上之論點，本研究僅利用兩張且未施打顯影劑的MR影像，來進行自動化腦瘤標定與體積測量，與醫師人工標定的腦瘤影像比較的結果，在自動腦瘤影像分割方面，PM = 72.80±36.20%，CR = 0.43±0.86；而半自動腦瘤影像分割方面，PM = 87.82±15.91%、CR =0.79±0.15，兩者皆顯示出此系統在標定腦瘤影像上，擁有良好的可行性。相信在未來臨床磁振造影的應用上，此系統將會成為一套快速、方便的醫療輔助診斷及治療腦瘤之系統。

關鍵字

自動化 ； 磁振影像 ； 腦瘤 ； 腦膜瘤 ； 影像分割 ； 模糊分類演算法 ； 區域成長 ； 專業知識基礎

並列摘要

In recent years, magnetic resonance imaging (MRI) has became an important modality for brain tissue diagnosis, due to its high resolution, less radiation injury and an excellent resolution for soft tissue imaging. The tissue characteristics can be described by different pixel intensity in the feature space of T1, T2 image via MRI system. This is especially useful for any attempt to segment brain tissues, normal or abnormal in clinical practice. Tumor segmentation is a key work for brain disease diagnosis. However, manual brain tumor segmentation from magnetic resonance images is a cumbersome and time-consuming task for physicians. For this reason, an automated brain tumor segmentation method is desirable.
This system used two non-contrast-enhanced MR images, T1 and T2 image to segment brain tumor automatically. In the beginning, we performed a multi-spectral histogram for pixels clustering by FCM, and merged the neighborhood pixels of seed by region growing. By knowledge-based information, our system selected tumorous clusters and merged them into one tumorous image. Finally, we optimize the tumorous image with morphology technique.
Based on above algorithm, this system used only two non-contrast-enhanced T1 and T2 MR images to label tumor and measure the area of tumor automatically. To evaluate the segmentation results, they were then compared to “ground-truth” (GT) on a pixel level. The performance in automatic brain tumor segmentation of our system, the total PM varies from 15.5% to 99.91% with a mean and standard deviation of 86.98% and 17.42% respectively, and the total CR varies from 0.16 to 0.95 with a mean and standard deviation of 0.782 and 0.169 respectively. While in the semi-supervised brain tumor segmentation of our system, the total PM varies from 15.5% to 99.91% with a mean and standard deviation of 87.85% and 15.93% respectively, and the total CR varies from0.16 to 0.95 with a mean and standard deviation of 0.793 and 0.155 respectively. This statement represents that tumorous pixels were not only highly match between GT and our system, but has a fair level of correspondence between GT and our system. Therefore, this system has a great potential of becoming a clinical MR images analysis tool for helping experts to obtain tumor location and volumetric estimation or eventually to therapeutic planning in the future.

並列關鍵字

Automatic ； Magnetic Resonance Images ； MRI ； Brain Tumor ； Meningioma ； Image Segmentation ； Fuzzy Clustering ； Region Growing ； Knowledge-Based Techniques

參考文獻

[1] L. M. Fletcher-Heath, et al., "Automatic segmentation of non-enhancing brain tumors in magnetic resonance images," Artificial Intelligence in Medicine, vol. 21, pp. 43-63, Jan-Mar 2001.

[2] M. C. Clark, et al., "Automatic tumor segmentation using knowledge-based techniques," Medical Imaging, IEEE Transactions on, vol. 17, pp. 187-201, 1998.

[3] S. R. Kannan, et al., "New Robust Fuzzy C-Means Based Gaussian Function in Classifying Brain Tissue Regions," ed, 2009, pp. 158-169.

[4] C. Li, et al., "Knowledge-based classification and tissue labeling of MR images of human brain," IEEE Transactions on Medical Imaging, vol. 12, pp. 740-50, 1993.

[5] H. Khotanlou, et al., "3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models," Fuzzy Sets and Systems, vol. 160, pp. 1457-1473, 2009.