A refined circular template matching method for classification of human cytomegalovirus capsids in TEM images

Sintorn, Ida‐Maria; Homman-Loudiyi, Mohammed; Söderberg‐Nauclér, Cecilia; Borgefors, Gunilla

doi:10.1016/j.cmpb.2004.03.006

Cited by 30 publications

(16 citation statements)

References 14 publications

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“…Template matching [9] is done on a binary image (IB) converted from the likelihood scene Lv. Correlation between the selected template and IB is computed at each of the pixels c ∈ C. The choice of template is motivated by the size and shape of the structure of interest.…”

Section: Template Matchingmentioning

confidence: 99%

Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

Naik

Doyle

Agner

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

259

176

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Automated detection and segmentation of nuclear and glandular structures is critical for classification and grading of prostate and breast cancer histopathology. In this paper, we present a methodology for automated detection and segmentation of structures of interest in digitized histopathology images. The scheme integrates image information from across three different scales: (1) lowlevel information based on pixel values, (2) high-level information based on relationships between pixels for object detection, and (3) domain-specific information based on relationships between histological structures. Low-level information is utilized by a Bayesian classifier to generate a likelihood that each pixel belongs to an object of interest. High-level information is extracted in two ways: (i) by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Bayesian classifier to identify object boundaries, and (ii) by a template matching algorithm, where shape models are used to identify glands and nuclei from the low-level likelihood scenes. Structural constraints are imposed via domainspecific knowledge in order to verify whether the detected objects do indeed belong to structures of interest. In this paper we demonstrate the utility of our glandular and nuclear segmentation algorithm in accurate extraction of various morphological and nuclear features for automated grading of (a) prostate cancer, (b) breast cancer, and (c) distinguishing between cancerous and benign breast histology specimens. The efficacy of our segmentation algorithm is evaluated by comparing breast and prostate cancer grading and benign vs. cancer discrimination accuracies with corresponding accuracies obtained via manual detection and segmentation of glands and nuclei.

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Section: Template Matchingmentioning

confidence: 99%

Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

Naik

Doyle

Agner

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

259

176

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“…Segmenting and classifying the different capsids is essential to finally determine what their specific role is for HBV or other viruses [6]. Since the size difference between the T=4 and T=3 capsids is relatively small, an automatic tool is needed to avoid introducing bias by human quantification.…”

Section: Introductionmentioning

confidence: 99%

Automatic non-parametric capsid segmentation using wavelets transform and graph

Cassany

Kann

Sibarita

2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

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Every year, one million people dies from Hepatitis B virus. As for other viruses, its genetic material is enclosed by a capsid whose segmentation and classification is essential. In this paper, we present a novel capsid segmentation technique which is a combination of a "à trous" wavelet process (for background filtering) and a graph-based structure (for segmentation and classification). Capsids were acquired in transmission electron microscopy (TEM) as a set of 9 series of 40 images each. Our technique achieved as much as 80% of capsid detection and classification for 8 of the series, reaching more than 90% for 5 of them.

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“…Another way of describing the intensity variations in an object is to compute a radial or density profile (RDP). In [10] radial density profiles are used to discriminate between three maturation stages of human cytomegalovirus capsids in TEM images of cell sections. [11,12] are examples of their use in cyro-electron microscopy where DNA packing is compared between two virus types and attachment sites on Simian Cytomegalovirus capsids are analysed, respectively.…”

Section: Introductionmentioning

confidence: 99%

Virus Texture Analysis Using Local Binary Patterns and Radial Density Profiles

Kylberg

Uppström

Sintorn

2011

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. Local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures.

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A refined circular template matching method for classification of human cytomegalovirus capsids in TEM images

Cited by 30 publications

References 14 publications

Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

Automatic non-parametric capsid segmentation using wavelets transform and graph

Virus Texture Analysis Using Local Binary Patterns and Radial Density Profiles

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