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Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image.

Kato T, Relator R, Ngouv H, Hirohashi Y, Takaki O, Kakimoto T, Okada K - BMC Bioinformatics (2015)

Bottom Line: Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution.Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG.The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Science and Engineering, Gunma University, Kiryu-shi, Gunma, 376-8515, Japan. katotsu@cs.gunma-u.ac.jp.

ABSTRACT

Background: The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining. Although the rectangular histogram of oriented gradients (Rectangular HOG) is a widely recognized powerful descriptor for general object detection, it shows many false positives owing to the aforementioned difficulties in the context of glomeruli detection.

Results: A new descriptor referred to as Segmental HOG was developed to perform a comprehensive detection of hundreds of glomeruli in images of whole kidney sections. The new descriptor possesses flexible blocks that can be adaptively fitted to input images in order to acquire robustness for the detection of the glomeruli. Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution. Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG.

Conclusion: The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

No MeSH data available.


Candidate glomeruli and line segments. Segmental HOG (S-HOG) is based on the boundary of the objects of interest. If the boundary of a candidate glomerulus (Panel (a)) is to be located, boundary likeliness is computed at every point on m(=36) line segments placed uniformly in all m directions (Panel (b)). The boundary likeliness is computed at n points on each line segment. The n locations are depicted with dots in Panel (b)
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Fig1: Candidate glomeruli and line segments. Segmental HOG (S-HOG) is based on the boundary of the objects of interest. If the boundary of a candidate glomerulus (Panel (a)) is to be located, boundary likeliness is computed at every point on m(=36) line segments placed uniformly in all m directions (Panel (b)). The boundary likeliness is computed at n points on each line segment. The n locations are depicted with dots in Panel (b)

Mentions: The algorithm developed by Kvarnström et al. [21] is relevant to the proposed segmentation technique. Their algorithm for cell contour recognition is based on a dynamic program, where they first estimated the cell centers and constructed a ray from the center to each m direction (Fig. 1b), where m=32. Then, they computed the boundary likeliness at n points on each ray, where they set n=30. Their algorithm finds a smooth contour by taking a point on each ray to connect them. To this end, they posed the following discrete optimization problem: (1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $${} {\fontsize{8.9pt}{12.6pt}\selectfont{\begin{aligned} \max\quad\!&\sum\limits_{i=1}^{m}L_{i}(p_{i}),\qquad \text{wrt}\quad p_{1},\dots,p_{m}\in\{1,\dots,n\}, \\ \text{subject to} \quad\!& /p_{1}-p_{2}/ \le\varsigma, \dots, /p_{m-1}-p_{m}/\le\varsigma, /p_{m}-p_{1}/\le\varsigma, \end{aligned}}} $$ \end{document}max∑i=1mLi(pi),wrtp1,…,pm∈{1,…,n},subject to/p1−p2/≤ς,…,/pm−1−pm/≤ς,/pm−p1/≤ς,Fig. 1


Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image.

Kato T, Relator R, Ngouv H, Hirohashi Y, Takaki O, Kakimoto T, Okada K - BMC Bioinformatics (2015)

Candidate glomeruli and line segments. Segmental HOG (S-HOG) is based on the boundary of the objects of interest. If the boundary of a candidate glomerulus (Panel (a)) is to be located, boundary likeliness is computed at every point on m(=36) line segments placed uniformly in all m directions (Panel (b)). The boundary likeliness is computed at n points on each line segment. The n locations are depicted with dots in Panel (b)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4590714&req=5

Fig1: Candidate glomeruli and line segments. Segmental HOG (S-HOG) is based on the boundary of the objects of interest. If the boundary of a candidate glomerulus (Panel (a)) is to be located, boundary likeliness is computed at every point on m(=36) line segments placed uniformly in all m directions (Panel (b)). The boundary likeliness is computed at n points on each line segment. The n locations are depicted with dots in Panel (b)
Mentions: The algorithm developed by Kvarnström et al. [21] is relevant to the proposed segmentation technique. Their algorithm for cell contour recognition is based on a dynamic program, where they first estimated the cell centers and constructed a ray from the center to each m direction (Fig. 1b), where m=32. Then, they computed the boundary likeliness at n points on each ray, where they set n=30. Their algorithm finds a smooth contour by taking a point on each ray to connect them. To this end, they posed the following discrete optimization problem: (1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $${} {\fontsize{8.9pt}{12.6pt}\selectfont{\begin{aligned} \max\quad\!&\sum\limits_{i=1}^{m}L_{i}(p_{i}),\qquad \text{wrt}\quad p_{1},\dots,p_{m}\in\{1,\dots,n\}, \\ \text{subject to} \quad\!& /p_{1}-p_{2}/ \le\varsigma, \dots, /p_{m-1}-p_{m}/\le\varsigma, /p_{m}-p_{1}/\le\varsigma, \end{aligned}}} $$ \end{document}max∑i=1mLi(pi),wrtp1,…,pm∈{1,…,n},subject to/p1−p2/≤ς,…,/pm−1−pm/≤ς,/pm−p1/≤ς,Fig. 1

Bottom Line: Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution.Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG.The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Science and Engineering, Gunma University, Kiryu-shi, Gunma, 376-8515, Japan. katotsu@cs.gunma-u.ac.jp.

ABSTRACT

Background: The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining. Although the rectangular histogram of oriented gradients (Rectangular HOG) is a widely recognized powerful descriptor for general object detection, it shows many false positives owing to the aforementioned difficulties in the context of glomeruli detection.

Results: A new descriptor referred to as Segmental HOG was developed to perform a comprehensive detection of hundreds of glomeruli in images of whole kidney sections. The new descriptor possesses flexible blocks that can be adaptively fitted to input images in order to acquire robustness for the detection of the glomeruli. Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution. Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG.

Conclusion: The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

No MeSH data available.