Lung nodule detection and diagnosis using circle detection through plain radiographs

Authors

Ria Rodette De La Cruz, De La Salle University, Manila
Trizia Roby Ann Roque, De La Salle University, Manila
John Daryl Rosas, De La Salle University, Manila
Charles Vincent M. Vera Cruz, De La Salle University, Manila
Macario O. Cordel II, De La Salle University, Manila
Joel P. Ilao, De La Salle University, Manila

College

College of Computer Studies

Department/Unit

Computer Technology

Document Type

Archival Material/Manuscript

Publication Date

3-2013

Abstract

In this paper, we present a system that locates pulmonary nodules in digital chest radiographs through pattern recognition. Digital radiographs that are already diagnosed with lung nodules underwent histogram equalization in order to address varying illumination levels across different regions in the radiographs, and make the radiograph samples more comparable. Laplacian of Gaussian filtering is next applied in order to highlight the edges of pathological features like nodule-shaped blobs in each radiograph. Circular Hough Transform (CHT) was utilized in tandem with pixel-based image processing techniques in locating possible nodules. These system reports the count and sizes of the candidate nodules. We report an overall system accuracy of 73.33% when classifying digitized radiographs as either with nodules or without nodules.

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Recommended Citation

De La Cruz, R., Roque, T., Rosas, J., Vera Cruz, C. M., Cordel, M. O., & Ilao, J. P. (2013). Lung nodule detection and diagnosis using circle detection through plain radiographs. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/5441

Note

"Presented at the Research Congress 2013, De La Salle University manila, March 7-9, 2013."

Keywords

Pattern recognition systems; Image processing—Digital techniques; Radiography, Medical—Digital techniques; Lungs—Radiography; Lungs—Diseases—Diagnosis

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