Integrated Atlas Based Localisation Features in Lungs Images

* PG Scholar, Department of Computer Science and Engineering,
** Assistant Professor, Department of Computer Science and Engineering, MNSK College of Engineering, Pudukottai District, India.
Periodicity:September - November'2013


Segmentation of the pulmonary lobes is relevant in clinical practice and particularly challenging for cases with severe diseases or incomplete fissures. In this work, an automated segmentation approach is presented that performs a transformation on Computed Tomography (CT) scans to subdivide the lungs into lobes. Content- based image retrieval has been a major research area with major focus on features extraction, due to its impact on image retrieval performance. When applying this in the medical field, required different feature extraction method that integrate some domain specific knowledge for effective image retrieval. Here a novel method called atlas based segmentation is proposed. Atlas methods usually require the use of image registration in order to align the atlas image or images to a new, unseen image. This method provides complementary information from past cases with confirmed diagnoses, to lung tissue classification and quantification in CT images. The system exploits the location of the pathological lung tissue and allows significant improvement in terms of early retrieval precision when compared to the approach based on global features only.


Computed Tomography, Segmentation, Atlas Based Segmentation.

How to Cite this Article?

Bensingh, N., and Amunchakkaravarthi, N. (2013). Integrated Atlas Based Localisation Features In Lungs Images. i-manager’s Journal on Computer Science, 1(3), 31-36.


[1]. Binsheng Zhao, Gordon Gamsu Michelle S. Ginsberg, Li Jiang, and Lawrence H. Schwartz. (2003). “Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm”, Journal of Applied Clinical Medical Physics, Vol 4, No 3.
[2]. Bianca Lassen, Jan-Martin Kuhnigk, Ola Friman, Stefan Krass, Heinz-Otto Peitgen. (2010). “Automatic segmentation of lung lobes in ct images based on fissures, vessels, and bronchi”, IEEE.
[3]. Shiying Hu, Eric A. Hoffman and Joseph M. Reinhardt. (2001). “Automatic Lung Segmentation for Accurate Quantitation of Volumetric X-Ray CT Images”, IEEE Transactions on Medical Imaging, Vol. 20, No. 6.
[4]. E. M. van Rikxoort and B. van Ginneken, “Automatic segmentation of the lungs and lobes from thoracic CT scans” An article on Diagnostic Image Analysis Group, Department of Radiology, Radboud University Nijmegen Medical Centre, Netherlands.
[5]. Wook- Jin Choi and Tae-Sun Choi. (2013). “Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach”, Article Entropy, Vol 15.
[6]. Z. Faizal Khan S.N. Kumar V. Kavitha. (2011). “Efficient Algorithm to Enhance Lung Lobe Images using Fuzzy Filtering”, International Journal of Computer Applications Volume 25, No.6.
[7]. Jan-Mar tin Kuhnigk*, Horst K. Hahn, Milo Hindennach, Volker Dicken, Stefan Krass, and Heinz-Otto Peitgen. (2003). “Lung lobe segmentation by anatomyguided 3D watershed Transform” An article on, SPIE Vol. 5032.
[8]. Anitha and Sridhar. (2010). “Segmentation of Lung Lobes and Nodules in CT Images”, An International Journal(SIPIJ), Vol.1, No.1.
[9]. Bianca Lassen*, Eva M. Van Rikxoort, Michael Schmidt, Sjoerd Kerkstra, Bram Van Ginnekan and Jan –Martin kuhnigk. (2013). “Automatic Segmentation of the Pulmonary Lobes From Chest CT Scans Based on Fissures, Vessels and Bronchi” IEEE Trans. on Medical Imaging Vol:32 , No:2 .
[10]. Lola11 (2011). [Online]. Available:
[11]. R. Wiemker, T. Buelow, and T. Blaffert, (2005). “Unsupervised extraction of the pulmonary interlobar fissures from high resolution thoracic CT data,” Comput. Assist. Radiol. Surg., Vol. 1281, pp. 1121–1126.
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