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Comparative Study of Lung ROI Segmentation with Different Morphological Structuring Element Sizes in Noise Environment

V. Kalpana*, S. Varadarajan**

* Assistant Professor, Department of Electronics and Instrumentation Engineering, SVEC, Tirupathi, India.

** Professor, Department of Electronics and Communication Engineering, SVU, Tirupathi, India.

Periodicity:January - March'2014
DOI : https://doi.org/10.26634/jdp.2.1.2719

Abstract

Lung cancer is the most challenging problem chasing the scientific and medical fields in several countries around the world. Several medical modalities are specialised to diagnosis the extinct of disease via PET, MRI, CT, US and DICOM. Automated Computer Aided Diagnosing (CAD) system has shown its improvements in the detection of lung cancer using advanced radiology. CAD system merges geometric and intensity models to improve particular area of anatomical structure. Nodules are comparatively smaller than 3-4 cm in diameter and are common abnormalities that are adjacent to vessels or chest wall. Detection of non-spherical shaped nodules is a primary difficulty. Sixty percent of all nodules are not harmful but malignant nodules may be lung cancer tumours. To identify these nodules the methods like intensity thresholding or model based might fail. In this paper the nodules are extracted from the DICOM lung image in the noisy environment such as Gaussian, salt and pepper, Poisson and speckle using morphology and watershed algorithm. The nodules are extracted from the lung portion using different edge detection operators such as Gaussian, Laplacian, prewitt, LOG, Unsharp, average and Sobel in presence of noise and various sizes of the structuring element. These results support to examine and calculate the influence of noises on the DICOM images in extracting the nodules.

Keywords

Noise, DICOM, Nodules, Morphology, Structuring Element, Watershed.

How to Cite this Article?

Kalpana.V., and Varadarajan.S. (2014), Comparative Study of Lung ROI Segmentation With Different Morphological Structuring Element Sizes in Noise Environment. i-manager’s Journal on Digital Signal Processing, 2(1), 7-13. https://doi.org/10.26634/jdp.2.1.2719

References

[1]. Zhao Yu qian, Gui Wei-hua, (2005). ”Medical Images edge detection Based on Mathematical Morphology” Proceedings of 2005 IEEE Engineering in Medicine and th Biology 27 Annual Conference.

[2]. H. Chidiac, D. Ziou, (1999). “Classification of image Edges”, Vision interface'99, Troise-Rivieres, Canada, pp. 17-24.

[3]. M. Heath, S. Sarker, T. Sanocki and K. Bowyer, (1996). “Comparison of Edge Detectors: A Methodology and Initial Study”, Proceedings of CVPR'96 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 143-148.

[4]. Speeuwers, L. j. Heijden, F. Vander, (1992). “Evaluation of Edge Detectors using Average Risk”, Robust Computer Vision (Eds: Forstner/Ruwiedel), Wichmann, Karlsruhe:.

[5]. Tuba Sirin, Mehmet Izzet Saglam et al. “A Comparative Evaluation of Competitive Learning Algorithms for Edge detection Enhancement”,

[6]. Shin. M. C, Goldgof. D. B, Bowyer.K.W, Nikiforou.S, (2001). ”Comparision of edge detection algorithms using structure from motion task” Systems, Man and Cybernetics, Part B, IEEE Transactions, Vol 31,Issue 4, pp.589-601.

[7]. E. Clavier, S. Clavier, J. Labiche, (1999). “Image Sorting-Image Classification: A Global Approach”, icdar '99, Proceedings of the Fifth International IEEE Conference on Document Analysis and Recognition, pp. 123-126.

[8]. RamanMainiand J. S. Sohal, (2006). ”Performance Evaluation of Prewitt Edge Detector for Noisy Images”, GVIP Journal, Vol-6, Issue-3.

[9]. Mohsen Sharifi, Mahmoud Fathy, (2002). “A Classified and Comparitive Study of Edge detection Algorithms”, Proceedings of International Conference on Information Technology: Coding and Computing (ITCC'02) IEEE.

[10]. Mohamed Roushdy, (2006). ”Comparitive Study of Edge detection Algorithms Applying on the Gray Sacle Noisy Image using Morphological Filter”, GVIP Journal, Vol- 6, Issue-4.

[11]. R. M. Haralick, L. g. Slhapiro, (1992). “Computer and Robot Vision”, Vision 1, Addition- Wesley Publishing Company Inc.

[12]. Albovik, (2000). ”Handbook of Image and video Processing”, Academic Press.

[13]. P. Garcia, F. Pla, I. Garcia, (1999). “Detecting Edges in colour Images using dichromatic Differences”, Seventh International IEEE Conference on Image processing and ITS Applications, Vol. 1, pp.363-367.

[14]. K. Timmermann and R. Novak, (1999). ”Multiscale modelling and estimation of Poisson processes with applications to photon-limited imaging”, IEEE, Trans Information Theory., Vol.45, No.3, pp.846-852.

[15]. S. G. Chang, Y. Binand, M.Vetterli, (2006). ”Adaptive wavelet thresholding for image denoising and compression”, IEEE Trans. On Image Processing, Vol.9, No.9, pp. 1532-1546.

[16]. L. Gagnon and F. D. Smaili, (1996). ”Speckle Noise Reduction of Airborne SAR Images with Symmetric Daubechies Wavelets”, SPIE Proc. #2759, pp. 1424.

[17]. Cronin, P. et al., (2008). Solitary Pulmonary Nodules: Meta-analytic Comparison of Cross-sectional Imaging Modalities for Diagnosis of Malignancy. Radiology. 246:772-782.

[18]. H. E Durdick, (1996). Digital imaging: theory and applications, McGraw-hill.

[19]. Z. Wang and D. Hang, (1999). ” Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images”, IEEE Trans. on Circuits and Systems –II: Analog and Digital Signal Processing, Vol. 46. No. 1, pp.78- 80.

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