Noise-robust method for image segmentation

Segmentation of noisy images is one of the most challenging problems in image analysis and any improvement of segmentation methods can highly influence the performance of many image processing applications. In automated image segmentation, the fuzzy c-means (FCM) clustering has been widely used because of its ability to model uncertainty within the data, applicability to multi-modal data and fairly robust behaviour. However, the standard FCM algorithm does not consider any information about the spatial linage context and is highly sensitive to noise and other imaging artefacts. Considering above mentioned problems, we developed a new FCM-based approach for the noise-robust fuzzy clustering and we present it in this paper. In this new iterative algorithm we incorporated both spatial and feature space information into the similarity measure and the membership function. We considered that spatial information depends on the relative location and features of the neighbouring pixels. The performance of the proposed algorithm is tested on synthetic image with different noise levels and real images. Experimental quantitative and qualitative segmentation results show that our method efficiently preserves the homogeneity of the regions and is more robust to noise than other FCM-based methods

FCM Clustering Algorithms for Segmentation of Brain MR Images

The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed

キンデン シンゴウ カイセキ ニオケル WAVELET ヘンカン ノ テキヨウ ニ カンスル ケンキュウ

本論文は表面筋電信号(SEMG)の解析に対するwavelet変換の適応について論述するため，漸増負荷テストおよび筋疲労によるSEMGを離散wavelet変換（DWT)による独自パラメータと連続wavelet変換（CWT）を用いて検討した．最初に，漸増負荷による等尺性収縮時のSEMG変化を検討した．筋活動の状態を見るためにDWTを用いたパラメータを提案した．パラメータは，1)分解レベルj毎の信号のパワー：PD(j)，2)全ての分解レベルでのパワーの総和：TPw，3)分解レベルj毎のPD(j)とTPwの比率：RPD(j)とした．実験1では．健康な成人女性14名の上腕二頭筋からSEMGを導出した．対象に肘関節屈曲90゜の位置で最大限度の肘屈曲を行わせ，これを最大随意筋力(MVC)とした．続いて各対象に肘関節を同じ位置に保つように支持し，負荷を漸増させた．その結果，TPwは25%から35%MVCの間で有意な増加を示し，RPD(3)とRPD(4)のグラフはそれぞれ凸状，凹状に変化した．実験2では，健康な成人女性ボランティア13名から負荷の増加度を1.03Nm/s(低レベル: LL)と1.37Nm/s(高レベル: HL)に変化させた際のSEMGを導出した．その結果，HLでは50%MVCのTPwがMVCのTPwより高かった．次に，筋疲労評価に対してwavelet変換を適用した．実験1では，CWTによるSEMGの解析を行い，瞬時平均周波数（IMNF）と高周波成分の特徴をデータから抽出した．対象は健康な男性ボランティア11名で，5kgのダンベルで疲労困憊までアームカール課題を実行し，課題遂行中のSEMGを上腕二頭筋から記録した．結果，IMNFが低周波数方向へ有意にシフトし，高周波成分は有意に減少した．実験2では，等尺性収縮時の筋疲労をDWTによるパラメータで解析した．対象は健康な男性ボランティア5名で，背臥位で70%MVCの等尺性肘関節屈曲を行わせ，上腕二頭筋からSEMG信号を記録した．SEMG信号からIMNFとDWTによるパラメータを算出した．疲労の過程で，IMNFは低周波数方向にシフトし，PD(1)とPD(2)は減少，PD(4)とPD(5)は増加していた．以上の結果より，筋収縮活動におけるMU動員パターンの相違が示唆され，SEMG解析へのwavelet変換の適応は，有力で臨床的に役立つことが示された

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

Methods for assisting the automation of Dynamic Susceptibility Contrast Magnetic Resonance Imaging Analysis

Purpose Dynamic susceptibility-contrast magnetic resonance imaging (DSC-MRI) is widely used for cerebral perfusion measurement, but dependence on operator input leads to a time-consuming, subjective, and poorly-reproducible analysis. Although automation can overcome these limitations, investigations are required to further simplify and accelerate the analysis. This research focuses on automating arterial voxel (AV) and brain tissue segmentation, and model-dependent deconvolution steps of DSC-MRI analysis. Methods Several features were extracted from DSC-MRI data; their AV- and tissue voxel- discriminatory powers were evaluated by the area-under-the-receiver-operating-characteristic-curve (AUCROC). Thresholds for discarding non-arterial voxels were identified using ROC cut-offs. The applicability of DSC-MRI time-series data for brain segmentation was explored. Two segmentation approaches that clustered the dimensionality-reduced raw data were compared with two raw−data-based approaches, and an approach using principal component analysis (PCA) for dimension-reduction. Computation time and Dice coefficients (DCs) were compared. For model-dependent deconvolution, four parametric transit time distribution (TTD) models were compared in terms of goodness- and stability-of-fit, consistency of perfusion estimates, and computation time. Results Four criteria were effective in distinguishing AVs, forming the basis of a framework that can determine optimal thresholds for effective criteria to discard tissue voxels with high sensitivity and specificity. Compared to raw−data-based approaches, one of the proposed segmentation approaches identified GM with higher (>0.7, p<0.005), and WM with similar DC. The approach outperformed the PCA-based approach for all tissue regions (p<0.005), and clustered similar regions faster than other approaches (p<0.005). For model-dependent deconvolution, all TTD models gave similar perfusion estimates and goodness-of-fit. The gamma distribution was most suitable for perfusion analysis, showing significantly higher fit stability and lower computation time. Conclusion The proposed methods were able to simplify and accelerate automatic DSC-MRI analysis while maintaining performance. They will particularly help clinicians in rapid diagnosis and characterisation of tumour or stroke lesions, and subsequent treatment planning and monitoring