Skip to Main content Skip to Navigation
Conference papers

A Probabilistic Approach to Organic Component Detection in Leishmania Infected Microscopy Images

Abstract : This paper proposes a fully automated method for annotating confocal microscopy images, through organic component detection and segmentation. The organic component detection is performed through adaptive segmentation using a two-level Otsu’s Method. Two probabilistic classifiers then analyze the detected regions, as to how many components may constitute each one. The first of these employs rule-based reasoning centered on the decreasing harmonic patterns observed in the region area density functions. The second one consists of a Support Vector Machine trained with features derived from the log likelihood ratios of incrementally Gaussian mixture modeling detected regions. The final step pairs the identified cellular and parasitic components, computing the standard infection ratios on biomedical research. Results indicate the proposed method is able to perform the identification and annotation processes on par with expert human subjects, constituting a viable alternative to the traditional manual approach.
Document type :
Conference papers
Complete list of metadata

Cited literature [12 references]  Display  Hide  Download

https://hal.inria.fr/hal-01521410
Contributor : Hal Ifip <>
Submitted on : Thursday, May 11, 2017 - 5:10:31 PM
Last modification on : Sunday, November 15, 2020 - 7:40:06 PM
Long-term archiving on: : Saturday, August 12, 2017 - 1:55:34 PM

File

978-3-642-33409-2_1_Chapter.pd...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Pedro Nogueira, Luís Teófilo. A Probabilistic Approach to Organic Component Detection in Leishmania Infected Microscopy Images. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.1-10, ⟨10.1007/978-3-642-33409-2_1⟩. ⟨hal-01521410⟩

Share

Metrics

Record views

139

Files downloads

233