Local and global transformations to improve learning of medical images applied to chest radiographs

Vidya, M.; Krishnan, V Manikanda; Anirudh, G; Kundeti, Srinivasa Rao; Vijayananda, J.

doi:10.1117/12.2512717

Cited by 1 publication

(1 citation statement)

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“…The region growing method of chest segmentation is described in Kiran et al (2019). The normalization of the x-ray input chest image is made due to the poor contrast or due to glare (Guendel et al, 2019;Vidya et al, 2019;Hoffman, Kothari, & Wang, 2014). Then gamma correction is done as a luminance correction (Chen & Ramli, 2003;Deivalakshmi, Saha, & Pandeeswari, 2017).…”

Section: Methodsmentioning

confidence: 99%

A new merged segmentation technique used for X-ray chest images

Perumal¹,

Paul²

2021

3C Tecnología

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Image processing techniques of a chest X-ray image includes noise removal followed by segmentation, feature extraction to locate regions and classification. The chest image appears differently when viewed from different angles or under different lightings. Diagnosing TB stays a challenge. The customary medical specialty, even so count, variety ways, they are gradual and frequently unreliable. In ancient poster anterior chest radiographs, several clinical and diagnostic functions build use of computationally designed algorithms that assist in scientific diagnostic analysis by victimization acquisition of pictures. The Digital image may be a necessary medium for analyzing, annotating, patient's demographics coverage in screening of TB via chest radiography. This paper deals with the essential segmentation methods of TB. In our methodology, this disease can be fastly and accurately identifiable by the merge segmentation methods of K means with Marker-based Watershed segmentation which has highest precision and recall values when compared to the several other segmentation methods which is been is discussed. More than 80 chest Xray images output for recall and precision is discussed here.

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