Segmentation of Lungs from Chest X-ray using Euler Number-based Thresholding, Morphological Operators and Greedy Snakes
Ebenezer Jangam1, A. Chandrasekhar Rao2, Uppalapati Srilakshmi3, D. Yakobu4
1Ebenezer Jangam, Department of Computer Science, Vignan’s Foundation for Science Technology & Research, Guntur (Andhra Pradesh), India.
2A. Chandrasekhar Rao, Department of CSE, IIT (ISM) Dhanbad (Jharkhand), India.
3Uppalapati Srilakshmi, Department of Computer Science, Vignan’s Foundation for Science Technology & Research, Guntur (Andhra Pradesh), India.
4D. Yakobu, Department of Computer Science, Vignan’s Foundation for Science Technology & Research, Guntur (Andhra Pradesh), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 114-117 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10220275S419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A Computer-Aided Diagnosis (CAD) system is required to precisely detect diseases from the given chest x-ray. Lung segmentation is the basic step performed in the detection of diseases from the chest x-ray. In this paper, we use euler number-based thresholding method for lung region segmentation from CXR images. Morphological operations and greedy snakes are used to improve the accuracy of segmentation. The proposed method is experimented on two datasets: JRST and India. JRST contains 247 chest X- rays and India set contains 100 chest X-rays. An overall accuracy of 96.25% was achieved. The proposed method is compared with state of art methods and it gives high accuracy and high performance.
Keywords: Boundary Detection; Chest Radiography; Chan-Vese; Lung Field Segmentation; Snake Segmentation.
Scope of the Article: Data Visualization using IoT