i-manager Publications

Detection of Lung Nodule Using Watershed Management Technique

Simija Asmilin*, Dinsy Alphonse**

* PG Scholar, Department of Electronics and Communication Engineering, Sathyabama University, Chennai, India.

** Assistant Professor, Electronics and Telecommunication Engineering, Sathyabama University, Chennai, India.

Periodicity:December - February'2014
DOI : https://doi.org/10.26634/jcom.1.4.2715

Abstract

Lung cancer has been the largest cause of cancer deaths. In this Computed Tomography (CT) images are used which can be more efficient than X-ray. Hence, a lung cancer detection system using image processing is used to classify the presence of lung cancer in CT images. In this study MATLAB have been used. The process such as image preprocessing, Masking, Equalization and classifications are performed. To get more accurate results the sensitivity method is used.

Keywords

Cancer Detection, Image Preprocessing, Watershed Management Algorithm, Computed Tomography (CT), Contrast Enhanced (CE

How to Cite this Article?

Asmilin, J.S., and Alphonse, D. (2014). Detection Of Lung Nodule Using Watershed Management Technique. i-manager’s Journal on Computer Science, 1(4), 22-25. https://doi.org/10.26634/jcom.1.4.2715

References

[1]. Shengchen and kenjisuzuki (2003), "Computerized detection of lung nodule by means of virtual dual energy radiography", IEEE, Vol 60, February.

[2]. Bram van ginneken, Bart M. Terhaar romeny and Max A. Viergever (2001), "Computer aided diagnosis in chest radiography: A survey", IEEE, Vol 20, December.

[3]. Laurents hogeweg, Clara I. Sanchez and Bram Van Ginneken (2013),"Suppression of Transluctent Elongated Structures: Applications in Chest Radiography", IEEE, Vol 32, November.

[4]. Hiroyuki yoshida (2004), "Local contralateral subtraction based on bilateral symmetry of lung for reduction of false positives in computerized detection of pulmonary nodule', IEEE, vol 51, May.

[5]. Manual G. Penedo, Maria J. Carreira, Antonio Mosquera and diego cabello, "Computer aided diagnosis: A neural network based approach to lung nodule detection”. IEEE

[6]. Paola compaedeli Elena Casiraghi and Diana Artioli, (2006), "A fully automated method for lung nodule detection from postero-Anterior chest radiographs", IEEE, Vol 25,December.

[7]. kenji Suzuki, Iaso horiba and noboru sugie, "Neural edge enhancer for supervised edge enhancement from noisy images”, IEEE, Vol 25, December 2003.

[8]. Nidhal Bouaynaya ,An Schonfeld (2008), "Theoretical foundations of spatially variant mathematical morphology part II: Gray level images," IEEE, Vol 30, May.

[9]. H. Zhao, S.C.Lo, M. Freed Man and Y. Wang (2002), "Enhanced lung cancer detection in temporal subtraction chest radiography using directional edge filtering techniques"presented at the Proc.SPIE Med Image Process, San Diego, CA.

[10]. F.Li, R.Engelmann, K. Doi and H. Macmohan (2008), "Improved detection of small lung cancers with dual energy subtraction chest radiography," IEEE, Vol 190, April.

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25

Detection of Lung Nodule Using Watershed Management Technique

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content: