Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Degraded Image Analysis: An Invariant Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new wavelet-based measure of image focus
Pattern Recognition Letters
Hierarchical PCA Using Tree-SOM for the Identification of Bacteria
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Segmentation and classification of tuberculosis bacillifrom ZN-stained sputum smear images
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
A new focus measure using block maxima of image gradients
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Classification of mycobacterium tuberculosis in images of ZN-stained sputum smears
IEEE Transactions on Information Technology in Biomedicine
Multiclass mineral recognition using similarity features and ensembles of pair-wise classifiers
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Applications of 'TissueQuant'- A color intensity quantification tool for medical research
Computer Methods and Programs in Biomedicine
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Tuberculosis and other mycobacteriosis are serious illnesses which control is based on early diagnosis. A technique commonly used consists of analyzing sputum images for detecting bacilli. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid high errors. Image-processing techniques provide a good tool for improving the manual screening of samples. In this paper, a new autofocus algorithm and a new bacilli detection technique is presented with the aim to attain a high specificity rate and reduce the time consumed to analyze such sputum samples. This technique is based on the combined use of some invariant shape features together with a simple thresholding operation on the chromatic channels. Some feature descriptors have been extracted from bacilli shape using an edited dataset of samples. A k-means clustering technique was applied for classification purposes and the sensitivity vs specificity results were evaluated using a standard ROC analysis procedure.