Lung tuberculosis detection using x-ray images

Title

Lung tuberculosis detection using x-ray images

Creator

Antony B.; Nizar Banu P.K.

Description

This research work is based on the various experiments performed for the detection of lung tuberculosis using various methods like filtering, segmentation, feature extraction and classification. The results obtained from these experiments are discussed in this paper. Lung tuberculosis is a bacterial infection that causes more deaths in the world than any other infectious disease. Two billion people are infected with tuberculosis all around the world. Lung tuberculosis is a disease caused by a bacteria known as Mycobacterium tuberculosis or Tubercle bacillus. This research work strives to identify methods by which patients, who require second opinion for an already identified result, can save a lot of money. Once we receive X-ray image an input, pre-processing methods like Gaussian filter, median filter is applied. These filters help to remove unwanted noise and aid to get fine textural features. The output obtained from this is taken as an input and applied to water shed segmentation and gray level segmentation which helps to focus on the lung area of the obtained results. Output from these segmentation methods is fused to get a Region of Interest (ROI). From the ROI, the statistical features like area, major axis, minor axis, eccentricity, mean, kurtosis, skewness and entropy are extracted. Finally, we use KNN, Sequential minimal optimization (SMO), simple linear regression classification methods to detect lung tuberculosis. The results obtained in this paper suggests KNN classifier performs well than the other two classifiers. Research India Publications.

Source

International Journal of Applied Engineering Research, Vol-12, No. 24, pp. 15196-15201.

Date

2017-01-01

Publisher

Research India Publications

Subject

Canny edge detection; Graylevel threshold; Median filtering; Tuberculosis; Watershed segmentation; X-Ray

Coverage

Antony B., Department of Computer Science, Christ deemed to be University, Hosur Road, Bhavani Nagar, Bengaluru, 560029, Karnataka, India; Nizar Banu P.K., Department of Computer Science, Christ deemed to be University, Hosur Road, Bhavani Nagar, Bengaluru, 560029, Karnataka, India

Rights

Restricted Access

Relation

ISSN: 9734562

Format

Online

Language

English

Type

Article

Identifier

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057640992&partnerID=40&md5=409910db5874ad45fde5fea8099644d6