Radon-Like features and their application to connectomics

Kumar, Ritwik; Vázquez-Reina, Amelio; Pfister, Hanspeter

doi:10.1109/cvprw.2010.5543594

Cited by 59 publications

(66 citation statements)

References 12 publications

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“…Ray features, first introduced in [51], were applied to 2D mitochondria segmentation in [36]. Inspired by Ray features, Radon-like features were proposed in [33], but have shown to perform significantly worse than Ray features in [55].…”

Section: A Mitochondria Segmentationmentioning

confidence: 99%

Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features

Lucchi

Smith

Achanta

et al. 2012

IEEE Trans. Med. Imaging

236

211

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Abstract-It is becoming increasingly clear that mitochondria play an important role in neural function. Recent studies show mitochondrial morphology to be crucial to cellular physiology and synaptic function and a link between mitochondrial defects and neuro-degenerative diseases is strongly suspected. EM microscopy, with its very high resolution in all three directions, is one of the key tools to look more closely into these issues but the huge amounts of data it produces make automated analysis necessary.State-of-the-art computer vision algorithms designed to operate on natural 2D images tend to perform poorly when applied to EM data for a number of reasons. First, the sheer size of a typical EM volume renders most modern segmentation schemes intractable. Furthermore, most approaches ignore important shape cues, relying only on local statistics that easily become confused when confronted with noise and textures inherent in the data. Finally, the conventional assumption that strong image gradients always correspond to object boundaries is violated by the clutter of distracting membranes.In this work, we propose an automated graph partitioning scheme that addresses these issues. It reduces the computational complexity by operating on supervoxels instead of voxels, incorporates shape features capable of describing the 3D shape of the target objects, and learns to recognize the distinctive appearance of true boundaries.Our experiments demonstrate that our approach is able to segment mitochondria at a performance level close to that of a human annotator, and outperforms a state-of-the-art 3D segmentation technique.

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Section: A Mitochondria Segmentationmentioning

confidence: 99%

Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features

Lucchi

Smith

Achanta

et al. 2012

IEEE Trans. Med. Imaging

236

211

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“…While these techniques achieve reasonable results, they incorporate only textural cues while ignoring shape information. More recently, more sophisticated features [8][9][10] have been successfully used in conjunction with either a Random Forest classifier [11] or a Structured SVM (SSVM) [12,13]. The latter approach [12,13] is state-of-the-art in terms of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentation

Lucchi

Becker

Neila

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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“…Additionally, we incorporate newly developed features that proved to be informative for neuronal reconstruction: radon-like features [9], ray features [12] and line filter transform [13]. Also we use all the components of SIFT histogram [11] in the pixel.…”

Section: Featuresmentioning

confidence: 99%

Anisotropic ssTEM Image Segmentation Using Dense Correspondence across Sections

Laptev

Vezhnevets

Dwivedi

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Connectomics based on high resolution ssTEM imagery requires reconstruction of the neuron geometry from histological slides. We present an approach for the automatic membrane segmentation in anisotropic stacks of electron microscopy brain tissue sections. The ambiguities in neuronal segmentation of a section are resolved by using the context from the neighboring sections. We find the global dense correspondence between the sections by SIFT Flow algorithm, evaluate the features of the corresponding pixels and use them to perform the segmentation. Our method is 3.6 and 6.4% more accurate in two different accuracy metrics than the algorithm with no context from other sections.

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Radon-Like features and their application to connectomics

Cited by 59 publications

References 12 publications

Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features

Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features

Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentation

Anisotropic ssTEM Image Segmentation Using Dense Correspondence across Sections

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