Lung Segmentation for Chest Radiograph by Using Adaptive Active Shape Models

Lee, Jiann-Shu; Wu, Hsing-Hsien; Yuan, Ming-Zheng

doi:10.1109/ias.2009.353

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…Our active contour model was then based on these features. By using the lung ROI to get the translation and scaling parameters, a method [28] was made to predict an appropriate initial lung border for ASM deformation. Using k-means clustering and silhouette-based cluster validation, an adaptive ASM was made to change as the shape of the lung changed.…”

Section: Introductionmentioning

confidence: 99%

Lung Segmentation of Chest Radiograph Using Circular Window Based Local Contrast Thresholding (CWLCT) and Adaptive Median Outlier (CWAMO)

Kanade,

Helonde,

Burade

et al. 2023

ITM Web Conf.

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Certain chest illnesses, such as TB, adenocarcinoma, squamous cell carcinoma, large cell carcinoma, atelectasis, etc., can be diagnosed in chest radiographs, and the development of a CAD system relies in part on accurate lung segmentation. In order to partition the lungs in chest radiographs, this work introduces an unsupervised learning approach based on a circular window and local thresholding. The procedure involves pre-processing, a preliminary estimate of the lung field, and the elimination of noise. Images are initially scaled down to 1024x1024 and enhanced using adaptive histogram equalization. Then chest radiographs are binarized using the proposed method. Based on the geometrical and special characteristics, lungs are separated from the chest radiographs. The final step in picture segmentation is the use of morphological processes. Local thresholding, omitting extraneous body parts, filling in gaps, and filtering regions based on their attributes all contribute to preliminary estimates of the lung field. Morphological processes are used as a means of eliminating background noise. A public bone shadow eliminated JSRT dataset consisting of 247 chest x-rays is used to measure the performance of the proposed method. The effectiveness of the proposed method results’ performance is evaluated by comparing it with Active Shape Model (ASM) based lung segmentation for various performance metrics such as F-score, overlap percentage, accuracy rate, sensitivity, specificity, and precision rates. All the parameters for the proposed method are over and above 90%. Our investigations indicate that the suggested method is an unsupervised learning approach that does not require any training.

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Section: Introductionmentioning

confidence: 99%