Structure enhancement diffusion and contour extraction for electron tomography of mitochondria

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Abstract

The interpretation and measurement of the architectural organization of mitochondria depend heavily upon the availability of good software tools for filtering, segmenting, extracting, measuring, and classifying the features of interest. Images of mitochondria contain many flow-like patterns and they are usually corrupted by large amounts of noise. Thus, it is necessary to enhance them by denoising and closing interrupted structures. We introduce a new approach based on anisotropic nonlinear diffusion and bilateral filtering for electron tomography of mitochondria. It allows noise removal and structure closure at certain scales, while preserving both the orientation and magnitude of discontinuities without the need for threshold switches. This technique facilitates image enhancement for subsequent segmentation, contour extraction, and improved visualization of the complex and intricate mitochondrial morphology. We perform the extraction of the structure-defining contours by employing a variational level set formulation. The propagating front for this approach is an approximate signed distance function which does not require expensive re-initialization. The behavior of the combined approach is tested for visualizing the structure of a HeLa cell mitochondrion and the results we obtain are very promising.

Introduction

To date, it is firmly established that mitochondrial function plays an important role in the regulation of apoptosis (Green and Reed, 1998, Obeid et al., 2007). For instance, following a variety of cell death signals, mitochondria exhibit early alterations in function and morphologic changes, such as the opening of the permeability transition pore or mitochondrial megachannel (Frank et al., 2001, Zamzami et al., 2007). There is also strong evidence that defects in function may be related to many of the most common diseases of aging, such as Alzheimer dementia, Parkinson’s disease, type II diabetes mellitus, stroke, atherosclerotic heart disease, and cancer. This is founded on the observation that mitochondrial function undergoes measurable disturbance accompanied by drastic morphologic alterations in the presence of these multisystem diseases (Frey et al., 2006, Munnich and Rustin, 2001, Tandler et al., 2002).

Concurrent with the aforementioned conceptual advances there has been a significant increase in the types of tools available to study the correlation between mitochondrial structure and function. Along with the now classic methods for isolating mitochondria and assaying their biochemical properties, there are new and powerful methods for visualizing, monitoring, and perturbing mitochondrial function while assessing their genetic consequences (Marco et al., 2004, Pon and Schon, 2007). Electron tomography (ET) has allowed important progress in the understanding of mitochondrial structure. This imaging technique currently provides the highest three-dimensional (3D) resolution of the internal arrangement of mitochondria in thick Section (Perkins and Frey, 1999, Mannella et al., 1994). Nevertheless, the interpretation and measurement of the structural architecture of mitochondria depend heavily on the availability of good software tools for filtering, segmenting, extracting, measuring, and classifying the features of interest (Frey et al., 2002, Perkins et al., 1997).

This paper is organized as follows: Section 2 presents an overview of anisotropic nonlinear diffusion models in image processing in general, and in electron microscopy in particular. The level set method is also presented briefly as it is applied to the extraction of contours in images. In Section 3 we propose a new image smoothing and edge detection technique for electron tomography as an extension to the model proposed by Bazán and Blomgren (2007). This approach employs a combination of anisotropic nonlinear diffusion and bilateral filtering. In Section 4 we exhibit the performance of the combined approach for visualizing the structure of a HeLa cell mitochondrion with very promising results. We end this paper with a summary and discussion in Section 5.

Section snippets

Related work

In this section we present an overview of anisotropic nonlinear diffusion models in image processing and electron microscopy. The level set method is also presented briefly as it is applied to the extraction of contours in images. We only review here the works that serve as background to the model we propose in Section 3. For an excellent and comprehensive survey of diffusion methods in image processing we refer the interested reader to the book by Weickert (1998) and the references therein.

Anisotropic nonlinear diffusion and bilateral filter in electron tomography

In Section 2.2 we discussed the application of anisotropic nonlinear diffusion in electron tomography. The approach used by Frangakis et al., 1999, Frangakis and Hegerl, 2001, Fernández and Li, 2003, Fernández and Li, 2005 is based on a hybrid EED/CED denoising mechanism that performs very well on data containing low- to mid-frequency signal components. The technique greatly facilitates image enhancement for subsequent segmentation and improved visualization of complex biological specimens. In

Image acquisition

The electron tomogram employed in our experiments corresponds to a HeLa cell and was obtained from a 250 nm semi-thick section across a mitochondrion expressing cytochrome c-GFP. In the interest of research not discussed here, apoptosis was induced in the mitochondria with 100 μM etoposide for 15 h. The imaging occurred before the release of cytochrome c or loss of membrane potential allowing the maintenance of normal mitochondrion profiles; however, the treatment caused elongation of the crista

Summary and discussion

We have presented a multi-stage approach for extracting the mitochondrial structures from electron tomograms. In particular, we apply the strategy to a 3D tomogram of a HeLa cell mitochondrion.

In the initial reconstruction, or noise reduction phase, we propose a structure enhancing anisotropic nonlinear diffusion strategy: the local structure tensor Jbf is formed from the gradient information of a bilaterally smoothed version of the current image. In order to close gaps in structures caused by

Acknowledgements

The authors thank Dr. Terry Frey and the San Diego State University Mitochondria Research Group for their input and for providing the images used in our computations. From this group, our special thanks go to Mei Sun and Mariam Ghochani. This work has been supported in part by NIH Roadmap Initiative award R90 DK07015.

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