應用於多層斷層掃描的多種肺部疾病全自動篩檢系統

此研究主要是發展一個應用在多層斷層掃描影像的電腦輔助診斷系統。在系統中，主要應用了三維數學形態學、紋理和模糊邏輯分析的原理，自動地偵測並分辨多種原發性間質性肺部疾病及肺氣腫。整個系統的運作主要可以分成四大部份：(1) 一開始，我們的診斷系統使用一種多重解析的分解方式，此方式是依據三維形態學原理，以不同的分析刻度來分割影像中的肺部區域；(2) 為了加強在第一階段中的分割效果，我們另外使用了一種基於肺部紋理的空間區隔方法，再進一步地細分每個分析刻度下的分割結果；(3) 接下來，我們採用了階層式的樹狀結構來描述並記錄多重解析分解方法所得到的結果。在此樹狀結構中，每個節點都代表每個分析刻度裡所分解出的一個肺部區塊，每個分支都表示了不同分析刻度裡每個對應節點間的對應關係。並且，針對每個節點本身以及在不同階層間的對應關係，我們定義了六個特徵(六個模糊分析裡的歸屬函數)，用以量化每個節點(區塊)為正常或是病變組織的機率；(4) 最後，使用模糊邏輯分析來決定每個節點是屬於正常組織、肺氣腫、纖維化/蜂巢化或是毛玻璃化病變。在實驗驗證的部份，我們先是針對不同的系統參數設定以及不同的斷層掃瞄影像協定來評估診斷系統的效能。根據實驗的結果，我們所研發的診斷系統，在設定分析刻度為12層時，對於採用"LUNG"或"BONEPLUS"重建核心以及較小的準直參數(不高於1.25 mm)的斷層掃描影像，有著最佳的效能。更進一步，診斷系統所得到的結果，會拿來和資深放射師所判定的結果做比較，以及對於同一病人做長期的追蹤分析，這些實驗結果都正面地顯示出所研究的電腦輔助診斷系統之效能。同時，我們亦列出在研究上的一些困難處，像是ground truth的取得，以及纖維化和高密度區域(像是血管)的辨別，都是未來進一步研究時需要改進的地方。

關鍵字

模糊邏輯；肺氣腫；特發間質性肺部疾病；多層斷層掃描；分類；階層式分解

並列摘要

This research aims at developing a computer-aided diagnosis (CAD) system for fully automatic detection and classification of pathological lung parenchyma patterns in idiopathic interstitial pneumonias (IIPs) and emphysema using multi-detector computed tomography (MDCT). The proposed CAD system is based on 3-D mathematical morphology, texture and fuzzy logic analysis, and can be divided into four stages: (1) a multi-resolution decomposition scheme based on a 3-D morphological filter was exploited to discriminate the lung region patterns at different analysis scales. (2) An additional spatial lung partitioning based on the lung tissue texture was introduced to reinforce the spatial separation between patterns extracted at the same resolution level in the decomposition pyramid. Then, (3) a hierarchic tree structure was exploited to describe the relationship between patterns at different resolution levels, and for each pattern, six fuzzy membership functions were established for assigning a probability of association with a normal tissue or a pathological target. Finally, (4) a decision step exploiting the fuzzy-logic assignments selects the target class of each lung pattern among the following categories: normal (N), emphysema (EM), fibrosis/honeycombing (FHC), and ground glass (GDG). The experimental validation of the developed CAD system allowed defining some specifications related with the recommendation values for the number of the resolution levels NRL = 12, and the CT acquisition protocol including the “LUNG” / ”BONPLUS” reconstruction kernel and thin collimations (1.25 mm or less). It also stresses out the difficulty to quantitatively assess the performance of the proposed approach in the absence of a ground truth, such as a volumetric assessment, large margin selection, and distinguishability between fibrosis and high-density (vascular) regions.

並列關鍵字

Fuzzy logic ； Emphysema ； Idiopathic interstitial pneumonias ； Multi-slice computed tomography ； Classification ； Hierarchic decomposition

參考文獻

[1] "American Thoracic Society/European Respiratory Society international multidisciplinary consensus classification of the idiopathic interstitial pneumonias," Am. J. Respir. and Crit. Care Medicine, vol. 165, no. 2, pp. 277-304, Jan. 2002.

[2] O. S. Al-Kadi and D. Watson, "Texture analysis of aggressive and nonaggressive lung tumor CE CT images," IEEE Trans. on Biomed. Eng., vol. 55, no. 7, pp. 1822-1830, Jul. 2008.

[3] R. A. Blechschmidt, R. Werthschutzky, and U. Lorcher, "Automated CT image evaluation of the lung: a morphology-based concept," IEEE Trans. on Med. Imag., vol. 20, no. 5, pp. 434-442, May 2001.

[5] S. Hu, E. A. Hoffman, and J. M. Reinhardt, "Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images," IEEE Trans. on Med. Imag., vol. 20, no. 6, pp. 490-498, Jun. 2001.

[7] R. M. Summers, C. D. Johnson, L. M. Pusanik, J. D. Malley, A. M. Youssef, and J. E. Reed, "Automated polyp detection at CT colonography: feasibility assessment in a human population," Radiology, vol. 219, no. 1, pp. 51-59, Apr. 2001.

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應用於多層斷層掃描的多種肺部疾病全自動篩檢系統

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