Evidence map›Paper›PMID 28594383›Full record

ArticleSensors (Basel, Switzerland)2017

Image Processing of Porous Silicon Microarray in Refractive Index Change Detection.

Zhiqing Guo, Zhenhong Jia, Jie Yang, Nikola Kasabov, Chuanxi Li

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhiqing GuoCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, China. gzqxju@163.com.
Zhenhong JiaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, China. jzhh@xju.edu.cn.
Jie YangInstitute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China. Jieyang@sjtu.edu.cn.
Nikola KasabovKnowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New Zealand. nkasabov@aut.ac.nz.
Chuanxi LiSchool of Physical Science and Technology, Xinjiang University, Urumqi 830046, China. lcxxju@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A new method for extracting the dots is proposed by the reflected light image of porous silicon (PSi) microarray utilization in this paper. The method consists of three parts: pretreatment, tilt correction and spot segmentation. First, based on the characteristics of different components in HSV (Hue, Saturation, Value) space, a special pretreatment is proposed for the reflected light image to obtain the contour edges of the array cells in the image. Second, through the geometric relationship of the target object between the initial external rectangle and the minimum bounding rectangle (MBR), a new tilt correction algorithm based on the MBR is proposed to adjust the image. Third, based on the specific requirements of the reflected light image segmentation, the array cells are segmented into dots as large as possible and the distance between the dots is equal in the corrected image. Experimental results show that the pretreatment part of this method can effectively avoid the influence of complex background and complete the binarization processing of the image. The tilt correction algorithm has a shorter computation time, which makes it highly suitable for tilt correction of reflected light images. The segmentation algorithm makes the dots in a regular arrangement, excludes the edges and the bright spots. This method could be utilized in the fast, accurate and automatic dots extraction of the PSi microarray reflected light image.

Indexed as

feflected light imageporous silicon microarraypretreatmentspot segmentationtilt correction

Identifiers

PMID28594383
PMCPMC5492526

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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.