ArticleJournal of medical systems2021
Passive Auto Focusing of Pathological Microscope with Intelligent Field Image Collection Mechanism.
Article in Journal of medical systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 10 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The microscope is one of the widely used pathological equipment to analyze body fluids like blood, sputum, etc. in granular level. In order to reduce workload on pathologists and strengthen the telehealth services, an automatic self-focusing microscope with different field image collection mechanism is required. In this work, the conversion of a compound microscope into a complete digital self-focusing automatic microscope, with intelligent field image collection mechanism, is discussed. This method uses passive autofocusing technique. In this method, most informative regions are identified on the basis of texture information. Features from these identified regions are used for autofocusing the microscope. This system is capable of collecting multiple snaps from different regions of the smear sample slides. The problem with the smear slide is that it has un-uniform thickness upon the glass slide. So some region has a very thick layer and some region has a very thin layer. In general, both of these regions are not considered for pathological analysis. The proposed system is capable to detect the region of smear slide which is suitable for collection of snap images. A soft computing approach is used to detect the desired regions of the sample in the slide. The Raspberry pi is used to design the control section. Multi-threaded parallel programming is used to optimize I/O execution and waiting time. The performance of the proposed system is satisfactory. The average peak signal-to-noise ratio (PSNR) is about 33 in comparison with manual focusing by the domain expert. The performance of the system in terms of computation time, which is calculated on the benchmark microscopic image dataset, is better than other learning-based methods. Autofocusing of pathological microscope with an intelligent field image collection mechanism is highly useful in the remote healthcare domain. This work basically describes a mechanism to migrate the conventional compound microscope into a tale-health service compatible (IoT enabled) microscope. This system is highly suitable for developing countries where an overall change of existing infrastructure is difficult due to economic reasons.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.