Name
Title | ||
|---|---|---|
Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors |
Abstract | ||
|---|---|---|
Graphical abstractDisplay Omitted HighlightsA new early fusion approach for acrosome integrity classification is proposed.Specific segmentation based on shape priors was carried out.The biggest acrosome intact-damaged dataset ever created was created and published.Acrosome contour is important for improving description and classification.The best result up to date combining shape and texture features has been obtained. The assessment of the state of the acrosome is a priority in artificial insemination centres since it is one of the main causes of function loss. In this work, boar spermatozoa present in gray scale images acquired with a phase-contrast microscope have been classified as acrosome-intact or acrosome-damaged, after using fluorescent images for creating the ground truth. Based on shape prior criteria combined with Otsu's thresholding, regional minima and watershed transform, the spermatozoa heads were segmented and registered. One of the main novelties of this proposal is that, unlike what previous works stated, the obtained results show that the contour information of the spermatozoon head is important for improving description and classification. Other of this work novelties is that it confirms that combining different texture descriptors and contour descriptors yield the best classification rates for this problem up to date. The classification was performed with a Support Vector Machine backed by a Least Squares training algorithm and a linear kernel. Using the biggest acrosome intact-damaged dataset ever created, the early fusion approach followed provides a 0.9913 F-Score, outperforming all previous related works. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.cmpb.2015.03.005 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Acrosome integrity,Texture description,Contour description,Early fusion,SVM | Kernel (linear algebra),Computer vision,Acrosome,Segmentation,Computer science,Support vector machine,Ground truth,Artificial intelligence,Thresholding,Prior probability,Grayscale | Journal |
Volume | Issue | ISSN |
120 | 1 | 0169-2607 |
Citations | PageRank | References |
4 | 0.46 | 19 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oscar García-Olalla | 1 | 42 | 4.24 |
| Enrique Alegre | 2 | 139 | 21.84 |
| Laura Fernández-Robles | 3 | 54 | 12.95 |
| Patrik Malm | 4 | 4 | 0.46 |
| ewert bengtsson | 5 | 135 | 25.36 |