QUANTIFICATION OF PLEURAL EFFUSION ON CT IMAGES BY AUTOMATIC AND MANUAL SEGMENTATION

  • Murk Rehman Department of Biomedical Engineering, Mehran University of Engineering & technology Jamshoro, Pakistan
  • Dr. Pertab Rai Department of Biomedical Engineering, Mehran University of Engineering & technology Jamshoro, Pakistan
Keywords: Quantification, Pleural, Segmentation, Automatic

Abstract

The objective of this research is to make reliable estimation of pleural effusion volume in CT imaging using digital image processing algorithms. In order to make reliable estimation we need to do the manual and automatic segmentation of CT images and to perform the comparison of automatic and manual segmentation for the quantification of pleural effusion on CT images which provides help in the diagnosis of the pleural disease. Pleural effusion is the collection of excess fluid in the pleural cavity. Excessive amount of fluid can impair breathing by limiting the expansion of lungs. Heart failure, cancer, cirrhosis, pneumonia, tuberculosis and many other are the causes of pleural effusion. A number of noninvasive imaging techniques such as radiography, ultrasound and computed tomography (CT) can detect the pleural effusion. The problem faced is the quantification of pleural effusion volume for the purpose of diagnosis of the pleural disease. The objective of this research is to make reliable estimation of pleural effusion volume in CT imaging using digital image processing algorithm. In order to make reliable estimation we need to do the manual and automatic segmentation of CT images and to perform the comparison of automatic and manual segmentation for the quantification of pleural effusion on CT images which provides help in diagnosis of the pleural disease. The results obtained by both the aforementioned techniques indicate that the manual segmentation is better because automated technique has less number of pixels.

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Published
2019-05-31
How to Cite
Rehman, M., & Rai, P. (2019). QUANTIFICATION OF PLEURAL EFFUSION ON CT IMAGES BY AUTOMATIC AND MANUAL SEGMENTATION . International Journal of Engineering Technologies and Management Research, 6(5), 95-100. https://doi.org/10.29121/ijetmr.v6.i5.2019.375