24,400 research outputs found
Adaptive feature selection for classification of microscope images
For high-throughput screening of genetically modified plant cells, a system for the automatic analysis of huge collections of microscope images is needed to decide whether the cells are infected with fungi or not. To study the potential of feature based classification for this application, we compare different classifiers (kNN, SVM, MLP, LVQ) combined with several feature reduction techniques (PCA, LDA, Mutual Information, Fisher Discriminant Ratio, Recursive Feature Elimination). We achieve a significantly higher classification accuracy using a reduced feature vector instead of the full length feature vector
Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification
We propose a robust approach for performing automatic species-level
recognition of fossil pollen grains in microscopy images that exploits both
global shape and local texture characteristics in a patch-based matching
methodology. We introduce a novel criteria for selecting meaningful and
discriminative exemplar patches. We optimize this function during training
using a greedy submodular function optimization framework that gives a
near-optimal solution with bounded approximation error. We use these selected
exemplars as a dictionary basis and propose a spatially-aware sparse coding
method to match testing images for identification while maintaining global
shape correspondence. To accelerate the coding process for fast matching, we
introduce a relaxed form that uses spatially-aware soft-thresholding during
coding. Finally, we carry out an experimental study that demonstrates the
effectiveness and efficiency of our exemplar selection and classification
mechanisms, achieving accuracy on a difficult fine-grained species
classification task distinguishing three types of fossil spruce pollen.Comment: CVMI 201
The malaria system microApp: A new, mobile device-based tool for malaria diagnosis
Background: Malaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority.
Objective: The objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development.
Methods: The system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells.
Results: As a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly.
Conclusions: Accessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment.Peer ReviewedPostprint (published version
Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques
Among all animals, mosquitoes are responsible for the most deaths worldwide.
Interestingly, not all types of mosquitoes spread diseases, but rather, a
select few alone are competent enough to do so. In the case of any disease
outbreak, an important first step is surveillance of vectors (i.e., those
mosquitoes capable of spreading diseases). To do this today, public health
workers lay several mosquito traps in the area of interest. Hundreds of
mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have
to identify only the vectors to gauge their density. This process today is
manual, requires complex expertise/ training, and is based on visual inspection
of each trapped specimen under a microscope. It is long, stressful and
self-limiting. This paper presents an innovative solution to this problem. Our
technique assumes the presence of an embedded camera (similar to those in
smart-phones) that can take pictures of trapped mosquitoes. Our techniques
proposed here will then process these images to automatically classify the
genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer
Learning yielded an overall accuracy of 80% in classifying mosquitoes when
trained on 25,867 images of 250 trapped mosquito vector specimens captured via
many smart-phone cameras. In particular, the accuracy of our model in
classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are
deadly vectors) is amongst the highest. We present important lessons learned
and practical impact of our techniques towards the end of the paper
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