Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Kato, Tsuyoshi; Relator, Raissa; Ngouv, Hayliang; Hirohashi, Yoshihiro; Takaki, Osamu; Kakimoto, Tetsuhiro; Okada, Kinya
• Is Part Of: BMC bioinformatics. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 16
• Publication Date: 2015-12
• Subjects: Microscopy image analysis -- Glomerulus detection -- Computer vision -- Support vector machine -- Dynamic programming -- Glomerular injury marker -- Desmin immunostaining; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0739-1
• Languages: English
• ISSNs: 1471-2105

Abstract

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.