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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.


Detection performances. The proposed descriptor, S-HOG, achieves evident improvement in F-measures compared to the existing descriptor, R-HOG. With small loss of true positives, S-HOG halves false positives of R-HOG. (See subsection on ‘Detection performance’ for details)
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Fig7: Detection performances. The proposed descriptor, S-HOG, achieves evident improvement in F-measures compared to the existing descriptor, R-HOG. With small loss of true positives, S-HOG halves false positives of R-HOG. (See subsection on ‘Detection performance’ for details)

Mentions: Figure 7 shows the plots of the F-measure, Precision, and Recall for each testing image. S-HOG achieved an average of 0.866, 0.874, and 0.897 for F-measure, Precision, and Recall, respectively, whereas R-HOG obtained 0.838, 0.777, and 0.911, respectively. While applying detection methods to pathological evaluation, Precision is more important than Recall [11], and in this study, S-HOG achieved considerably higher Precision with a small sacrifice in Recall. A two-sample t-test was performed to assess the statistical differences. While no statistical difference of Recall can be detected (P-value = 3.47·10−1), the differences among F-measure and Precision are significant (P-values = 1.34·10−3 and 3.75·10−5, respectively).Fig. 7


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)

Detection performances. The proposed descriptor, S-HOG, achieves evident improvement in F-measures compared to the existing descriptor, R-HOG. With small loss of true positives, S-HOG halves false positives of R-HOG. (See subsection on ‘Detection performance’ for details)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig7: Detection performances. The proposed descriptor, S-HOG, achieves evident improvement in F-measures compared to the existing descriptor, R-HOG. With small loss of true positives, S-HOG halves false positives of R-HOG. (See subsection on ‘Detection performance’ for details)
Mentions: Figure 7 shows the plots of the F-measure, Precision, and Recall for each testing image. S-HOG achieved an average of 0.866, 0.874, and 0.897 for F-measure, Precision, and Recall, respectively, whereas R-HOG obtained 0.838, 0.777, and 0.911, respectively. While applying detection methods to pathological evaluation, Precision is more important than Recall [11], and in this study, S-HOG achieved considerably higher Precision with a small sacrifice in Recall. A two-sample t-test was performed to assess the statistical differences. While no statistical difference of Recall can be detected (P-value = 3.47·10−1), the differences among F-measure and Precision are significant (P-values = 1.34·10−3 and 3.75·10−5, respectively).Fig. 7

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.