Dynamic detection of anomalies in pharmaceutical blisters using image processing

Sudharshan, Duth P.; Ameen, Shehran Ahmed Haji; Baig, Kareem

doi:10.1109/icisc.2018.8398890

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Despite this, their proposed methodology is very interesting regarding a further improvement of our system, especially regarding the extension for the classification of non-circular pills. Sudharshan et al [15] reported an accuracy of 95% with their proposed automatic detection tool of anomalies in blisters. In their methodology, the blisters are detected for colored regions using the different color channels of the image.…”

Section: Discussionmentioning

confidence: 99%

“…A typical modern smartphone is equipped with imaging sensors that can produce images of exceptional resolution and quality. Digital image processing has already played an important role in several projects related to the detection or identification of pills [12][13][14][15][16]. Lee and co-workers proposed an automatic pill identification and recognition program to identify illegal drugs [12].…”

Section: Introductionmentioning

confidence: 99%

“…Very encouraging experimental results of 96.33% accuracy were achieved over 50 blisters. In another research work, Sudharshan et al present a system based on image processing techniques to detect damaged pills in tablet blisters during the pharmaceutical production process [15]. The system uses median filtering and bounding box techniques for the inspection of the blisters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System

Holtkötter

Amaral

Almeida

et al. 2022

Sensors

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Long-term adherence to medication is of critical importance for the successful management of chronic diseases. Objective tools to track oral medication adherence are either lacking, expensive, difficult to access, or require additional equipment. To improve medication adherence, cheap and easily accessible objective tools able to track compliance levels are necessary. A tool to monitor pill intake that can be implemented in mobile health solutions without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the Circular Hough Transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In the counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills needs to be further improved, in particular for the detection of pills with non-oval shapes.

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Section: Discussionmentioning

confidence: 99%