Open Access Open Access  Restricted Access Subscription or Fee Access

Sputum Based Pneumonia Detection through Image Processing

R. Ashwin, S. S. Rakesh, Dr. Preethi N. Patil


Sputum is a liquid composing saliva and mucus produced in the human lungs and in trachea that leads to lungs. The respiratory infection caused in human lungs could be investigates by processing and analyzing the sputum samples. Pneumonia is one kind of lung infection which is diagnosed by using sputum. Microscopic visualization and the inspection process for this detection process can be implemented by image processing. Image processing technique that accept the image as input, processes each pixel of the image and analyze each cell for detection and identification as the cell belong to infection causing bacteria or not. This process is very cumbersome when huge dataset is present. Hence, implementation of an automated system that takes microscopic image of sputum sample and process them by applying image processing techniques to detect and classify the type of bacteria present could be a solution for this problem.  The proposed method reduces the complexity involved in manual process and provides accessibility through automated system and helps to build a microbiological application. Sputum image has been tested for identification of Cocci and bacilli bacterial cell.


Sputum, Respiratory Infection, Microscopic Image, Cocci, Bacilli

Full Text:



F. I. Azman1, K. H. Ghazali1, Z. Mohamed2 and R. Hamid, ,Detection of Sputum Smear Cell Based on Image Processing Analysis–vol-10, 2015.

D. J. Flournoy, PhD, MT(ASCP)SM, Interpreting the Sputum Gram Stain Report

Hiremath P. S. and Parashuram Bannigidad, Automatic Identification and Classification of Cocci Bacterial Cells using Digital Microscopic Images, Int’l. J. on computational Biology and Drug Design (IJCBDD), Inderscience Publishers Ltd. USA, Vol. 4, No. 3, pp. 262-273, 2011.

Nicholas, B., Åke, H., Johan, W., Rocio, C-H. and Peter, K.B., Rapid Determination of Bacterial Abundance, Biovolume, Morphology, and Growth by Neural Network-Based Image analysis, Applied and Environmental Microbiology, Vol.64(9), 1998, pp.3246- 3255.

Sigal Trattner and Greenspan H, Automatic Identification of Bacterial Types Using Statistical Imaging Methods, IEEE Transactions on Medical Imaging, Vol.23(7), 2004, pp.807-820.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.