CHARACTERISATION OF TABLETS FOR DEFECT IDENTIFICATION USING IMAGE PROCESSING TECHNIQUES FOR QUALITY CONTROL IN PHARMACEUTICAL INDUSTRY

Authors

  • Durga Karthik Department of Computer Science Engineering, Srinivasa Ramanujan Centre, Sastra University, Kumbakonam, Tamil Nadu, India. http://orcid.org/0000-0003-3199-8814
  • Vijayarekha K Department of Electrical and Electronics Engineering, Sastra University, Thanjavur,India.
  • Surya K Department of Computer Science Engineering, Srinivasa Ramanujan Centre, Sastra University, Kumbakonam, Tamil Nadu, India.

DOI:

https://doi.org/10.22159/ajpcr.2017.v10i11.20033

Keywords:

Image processing, Tablets, Edge detection, Feature extraction, Characterization

Abstract

 

 Objective: Our aim is to characterize the parameters for identifying defective tablets from the manufacturing line using image processing techniques.

Methods: Manufactured tablets might have defects such as broken chips, missing tablet, and color variation. Images of tablets are captured using machine vision camera. The features are detected using feature extraction for a tablet without defects and are stored in a database. The stored details are used for identifying defective tablets during manufacturing.

Results: The characteristics such as color, shape, number of pills, area, and perimeter of the normal tablets without defects were extracted.

Conclusion: The defective tablets can be identified by comparing the characteristics stored in the database and can be removed effectively.

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Author Biography

Durga Karthik, Department of Computer Science Engineering, Srinivasa Ramanujan Centre, Sastra University, Kumbakonam, Tamil Nadu, India.

Department of Computer science and engineering

Assistant Professor

References

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Published

01-11-2017

How to Cite

Karthik, D., V. K, and S. K. “CHARACTERISATION OF TABLETS FOR DEFECT IDENTIFICATION USING IMAGE PROCESSING TECHNIQUES FOR QUALITY CONTROL IN PHARMACEUTICAL INDUSTRY”. Asian Journal of Pharmaceutical and Clinical Research, vol. 10, no. 11, Nov. 2017, pp. 270-2, doi:10.22159/ajpcr.2017.v10i11.20033.

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Original Article(s)