A Bag of Features Based Approach for Classification of Motile Sperm Cells
| Autor: | Lyudmila Mihaylova, Daniel Alonso Paredes Soto, Maria Luisa Davila Garcia |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Contextual image classification
Computer science business.industry Feature vector Feature extraction Pattern recognition Image processing 02 engineering and technology 030218 nuclear medicine & medical imaging Support vector machine 03 medical and health sciences 0302 clinical medicine Histogram of oriented gradients Robustness (computer science) Histogram 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | iThings/GreenCom/CPSCom/SmartData |
| DOI: | 10.1109/ithings-greencom-cpscom-smartdata.2017.21 |
| Popis: | The analysis of sperm morphology remains an essential process for diagnosis and treatment of male infertility. In this paper, a novel framework based on image processing is proposed to classify sperm cell images affected by noise due to their movement. This represents a challenge, articularly because the cells are not fixed or stained. The proposed framework is based on Speeded-Up Robust Features (SURF) combined with Bag of Features (BoF) models to quantise features computed by SURF. Support Vector Machines (SVMs) are used to classify the simplified feature vectors, extracted from sperm cell images, into normal, abnormal and noncell categories. The performance of this framework is compared to a similar model where the Histogram of Oriented Gradients (HOG) is used to extract features and SVMs is applied for their classification. The proposed framework allows to achieve classification results with an average accuracy of 90% with the SURF approach compared to 78% with the HOG approach. |
| Databáze: | OpenAIRE |
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