3D Angiographic Image Segmentation

Naegel, Benoît; Passat, Nicolas; Ronse, Christian

doi:10.1002/9781118600788.ch15

Cited by 1 publication

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References 17 publications

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“…Max-trees allow the efficient implementation of many connected filters like area-open (Vincent, 1994), hmax (Salembier et al, 1998), ultimate opening (Fabrizio and Marcotegui, 2009), statistical attribute filters (Teeninga et al, 2015), vector attribute filters (Kiwanuka and Wilkinson, 2015), among others. They have been used in a wide range of automatic applications like object recognition (Souza et al, 2014), scale and rotation invariant image classification (Urbach et al, 2007), detection, tracking and recognition of license plates (Donoser et al, 2007), recognition of text in natural scenes (Merino-Gracia et al, 2012), 3D magnetic resonance (MR) brain segmentation (Dokládal et al, 2003), angiographic image segmentation (Caldairou et al, 2009), dermatological image segmentation (Naegel et al, 2007), medical image registration (Richard et al, 2000), remote sensing (Ghamisi et al, 2016b;, detection of local features like Maximally Stable Extremal Regions (MSER) (Matas et al, 2002) and Morse Regions (Xu et al, 2014), among many others.…”

Section: Introductionmentioning

confidence: 99%

Max-tree

Souza¹

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The max-tree is a mathematical morphology tool that represents all connected components resulting of every possible upper threshold of an image. This work focus on advancing further the max-tree theory, algorithms and applications. In the theory domain, extinction filters (EFs) for increasing attributes are defined and a procedure for computing EFs for nonincreasing attributes, based on the space of shapes approach, is presented. In the algorithms domain, an array-based node-oriented data structure to store the max-tree and algorithms to process it are proposed. This structure gives direct access to the max-tree nodes, is more memory efficient and allows faster processing than other structures described in the literature. Using this proposed structure, EFs can be implemented efficiently as our experiments and complexity analysis indicate. EFs are experimentally compared to attribute filters and the results show that EFs are better in terms of simplification for recognition. We provide an open-source toolbox entitled iamxt that implements the data structure and the algorithms proposed using an array-based programming style. In the applications domain, we investigate two problems: classification of satellite images and magnetic resonance (MR) images brain segmentation. These applications are built upon EFs and the efficient max-tree algorithms. The satellite image classification approach we propose is called Extinction Profiles. It is currently the state-of-the-art for the classification of this kind of image. We validate this comparing to another state-of-the-art approach called Attribute Profile using two different datasets. For the brain segmentation application, we propose a public multi-vendor multi-field strength T 1-weighted brain MR dataset with 359 volumes and brain segmentation "silver standards" generated using supervised classification. We validate our method using this dataset and two other public datasets. The results show that our method has the highest sensitivity among the methods compared, i.e. preserves the brain, and it is robust to parameter initialization.

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Section: Introductionmentioning

confidence: 99%

Max-tree

Souza¹

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3D Angiographic Image Segmentation

Cited by 1 publication

References 17 publications

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