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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 791246, 12 pages
Research Article

A Statistical-Textural-Features Based Approach for Classification of Solid Drugs Using Surface Microscopic Images

Department of Computer Science, Lahore College for Women University, Jail Road, Lahore 54000, Pakistan

Received 14 July 2014; Revised 15 September 2014; Accepted 15 September 2014; Published 13 October 2014

Academic Editor: Tingjun Hou

Copyright © 2014 Fahima Tahir and Muhammad Abuzar Fahiem. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The quality of pharmaceutical products plays an important role in pharmaceutical industry as well as in our lives. Usage of defective tablets can be harmful for patients. In this research we proposed a nondestructive method to identify defective and nondefective tablets using their surface morphology. Three different environmental factors temperature, humidity and moisture are analyzed to evaluate the performance of the proposed method. Multiple textural features are extracted from the surface of the defective and nondefective tablets. These textural features are gray level cooccurrence matrix, run length matrix, histogram, autoregressive model and HAAR wavelet. Total textural features extracted from images are 281. We performed an analysis on all those 281, top 15, and top 2 features. Top 15 features are extracted using three different feature reduction techniques: chi-square, gain ratio and relief-F. In this research we have used three different classifiers: support vector machine, -nearest neighbors and naïve Bayes to calculate the accuracies against proposed method using two experiments, that is, leave-one-out cross-validation technique and train test models. We tested each classifier against all selected features and then performed the comparison of their results. The experimental work resulted in that in most of the cases SVM performed better than the other two classifiers.