結合紋理特徵分析與模糊可能性分群分割腦部磁振影像

Abstract

腦部磁振影像分割，又名大腦擷取或頭骨去除(Skull Stripping)，是醫學影像分析的重要前處理之一。因為人類腦部的高複雜度和磁振造影(Magnetic Resonance Imaging，MRI)的多變參數影響，頭骨去除是具有相當挑戰性的。在本篇論文當中，我們提出一個利用區域紋理特徵與模糊可能性分析結合型態學的演算法。在所提出的演算法中，我們對影像每一點計算區域特徵值，分別為田村(Tamura)紋理特徵、灰階共生矩陣(Gray Level Co-Occurrence Matrix)及灰階長度矩陣(Gray Level Run-Length Matrix)特徵。田村紋理特徵使用粗糙度、對比度、方向性、線相似性、知覺性、規律性以及粗略性來代表影像的特徵值，本篇論文取前三項使用。灰階共生矩陣研究灰階值的空間相關特性，反映灰階值的分布特性，也是種常用的描述紋理方法。灰階長度矩陣紋理特徵則根據角度計算灰階值的連續長度得出特徵矩陣，再計算各種特徵值。接著使用模糊可能性分群的方法來將特徵影像(Feature image)分類。最後使用型態學對分類過後的特徵影像做處理。首先使用侵蝕處理，接著找出最大區域，由於腦接近眼睛的影像常和許多組織混在一起，若是直接找最大區域，所得出的結果將不是我們所需要的，但是腦中間切片卻可以分的十分清楚，因此我們從腦中央開始往兩端做運算，利用腦部是連續的且中間切片一定是最大的概念，取前一張影像結果作遮罩計算影像和遮罩交集的區域。再利用擴張將受侵蝕的影像復原，結尾將影像中的小洞填滿。最後我們將實驗結果與現有的兩種廣為人知的方法做比較，比較結果指出本研究所提出之演算法，在腦部分割影像網路資料庫所提供的臨床真實磁振影像的頭骨去除結果，有更好的精準度。

Segmentation of brain tissue from non-brain tissue, also known as skull stripping, has been challenging due to the complexity of human brain structures and variable parameters of MR scanners. It is one of the most important preprocessing steps in medical image analysis. Skull stripping is often performed using a sequence of mathematical morphological operations following an initial separation of the brain from other tissues of the head. We propose a new brain segmentation algorithm that is based on a texture feature analysis, fuzzy possibilistic c-means and morphological operations. Tamura texture feature consist of six features. Gray Level Run-Length Matrices method is a comparably simple and straightforward texture analysis approach, and So does gray level co-occurrence matrix. Three methods are well-known and representative. After computation of textures, we apply fuzzy possibilistic c-means(FPCM) for voxel clustering, which provides a labeled image for the following morphological operations. The last step, we then apply sequence morphological operations followed by FPCM to find out the brain region. Our method starts from middle image to side because of the high accuracy in middle. We compare our methods with two famous methods, with internet brain segmentation repository data sets. Experimental results indicated that the proposed algorithm is effectively and potential application in a wide variety of brain image segmentation.