Weighted Shape-Based Averaging With Neighborhood Prior Model for Multiple Atlas Fusion-Based Medical Image Segmentation

Gorthi, Subrahmanyam; Cuadra, M. Bach; Tercier, Pierre-Alain; Allal, Abdelkarim Said; Thiran, Jean-Philippe

doi:10.1109/lsp.2013.2279269

Cited by 13 publications

(12 citation statements)

References 11 publications

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“…We have shown that MAF strategy significantly improves TV-based fetal brain reconstruction (improvement of 7.03dB with 99% confidence interval for the PSNR). Future work will investigate more advanced fusion strategies such as local weighted voting combined with MRF-based edge-preserving smoothing 22,23 . Also, the atlas selection could be improved by selecting e.g.…”

Section: Discussionmentioning

confidence: 99%

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Automatic brain extraction in fetal MRI using multi-atlas-based segmentation

et al. 2015

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In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality. DESCRIPTION OF THE PURPOSEMost of the high-resolution reconstruction algorithms used in fetal MRI 1-10 rely only on brain tissue-relevant voxels of low-resolution (LR) images. In general, those algorithms need to perform brain segmentation as a preprocessing step. This brain extraction is essential to ensure good results of the subsequent image processing steps (denoising, bias correction, motion estimation, and super-resolution reconstruction). Despite of manual brain segmentation can be performed, it is highly time-consuming (around 15 minutes per stack of 15 slices) and not a realistic solution for large-scale studies. In the literature, accurate brain extraction tools have been developed for adult and infant brain MRI. 11, 12 But, fetal brain MRI differs a lot from neonatal or adult imaging in terms of image content (with maternal tissues surrounding the fetal brain), image contrast and brain size. Consequently, those tools are not well adapted to fetal MRI.Few works have addressed the automatic extraction of fetal brain in MRI. Two major types of approaches can be distinguished, either template-based segmentation 13-15 or machine learning 16-18 techniques. The first attempt 13 of fetal brain extraction proposed first to estimate the location of the eyes (based on rigid template registration) in order to segment the fetal brain using contrast, morphological and biometrical prior information. This method gave precise results in 22 out of 24 stacks of fetuses aged between 30 and 53 gestational weeks (GW). However, they relied on the assumption of low motion between slices that limits the robustness of the method to clinical databases where large motion can occur. More recently, Taleb et al. 14 presented an efficient brain extraction method based on single age-specific template segmentation (affine template registration) and fusion of orthogonal segmentations (transversal, coronal and sagittal) where final brain masks were successfully estimated in 82% of the cases. The validation was however rather qualitative (i.e. only success or failure label was given to the results). A supervised approach, 16 based on a two-phase random forest classifier, was adopted in order to obtain a method applicable to all fetal ages and more robust with respect to motion between slices. This method has shown comparable results to the method 13 but the whole brain was c...

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Section: Discussionmentioning

confidence: 99%

“…19,22,23 In our framework, we adopt a global weighted voting (GWV) MAF strategy. It is formulated as the following voxel-wise maximization problem:…”

Section: Multi-atlas Fusion (Maf) Strategymentioning

confidence: 99%

Automatic brain extraction in fetal MRI using multi-atlas-based segmentation

et al. 2015

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“…An alternative approach involves using the signed distance maps of the original atlas label images (Gholipour et al, 2012; Gorthi et al, 2013; Sabuncu et al, 2010; Sjöberg and Ahnesjö, 2013; Weisenfeld and Warfield, 2011a; Xu et al, 2014b). Each label has an associated signed distance map, which takes positive values within the corresponding structure, negative values outside, and the magnitude is proportional to the closest distance to the label boundary.…”

Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

Multi-atlas segmentation of biomedical images: A survey

Iglesias

Sabuncu

2015

Medical Image Analysis

603

457

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Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 – 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

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“…In medical image fusion, merging automated segmentations obtained from multiple sources has become a common practice for improving accuracy [13]. Subrahmanyam Gorthi et al proposed two fusion methods -Global weighted shape based averaging and Local weighted shape based averaging.…”

Section: Different Image Fusion Techniquesmentioning

confidence: 99%

Analysis on Image Fusion Techniques for Medical Applications

Indira¹,

Hemamalini²

2014

IJAREEIE

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ABSTRACT:Image fusion is the process of combining relevant information from a set of images into a single image in which the fused image contains more information than any of the input images. This technique improves the quality of data. Image fusion is one of the important re-processing steps in digital image reconstruction. In this paper a review on some of the image fusion techniques like curvelet transform, wavelet transform, contourlet transform, stationary wavelet transform, framelet transform etc. used in image fusion is submitted.

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Weighted Shape-Based Averaging With Neighborhood Prior Model for Multiple Atlas Fusion-Based Medical Image Segmentation

Cited by 13 publications

References 11 publications

Automatic brain extraction in fetal MRI using multi-atlas-based segmentation

Automatic brain extraction in fetal MRI using multi-atlas-based segmentation

Multi-atlas segmentation of biomedical images: A survey

Analysis on Image Fusion Techniques for Medical Applications

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