Lung field segmentation using weighted sparse shape composition with robust initialization

Abstract

The experimental results show that the proposed deformable segmentation model is more robust and accurate than the traditional appearance and shape model on the JSRT database. Our method also shows higher accuracy than most state-of-the-art methods.

Robust pulmonary segmentation for chest radiography, combining enhancement, adaptive morphology and innovative active contours

“…Xiong study presented an average OR of 95.5%, while our proposed method presented almost 98%, with a margin of accuracy 2% higher. In addition, the parameter of edge accuracy, ACD (Table 1), of our proposed approach, in which averaged, deviation, minimum and maximum values obtained were 0.69, 0.95, 0.08 and 6.05, respectively, are better or similar to the best results of the outcome presented by Xiong et al (Dai et al, 2017;Xiong et al, 2017), (1.43, 0.74, 0.68 and 5.96, respectively). In addition, it is possible to evaluate with more detail the overcoming of the presented method by comparing the averaged values of OR and ACD of others lung segmentation methods of literature using the same database as shown in Table 2.…”

“…On the other hand, our proposed method requires a single image of the patient to perform the segmentation with superior accuracy. Another recent study, using the same database for evaluation, was presented by Xiong et al (Dai et al, 2017;Xiong et al, 2017). Xiong study presented an average OR of 95.5%, while our proposed method presented almost 98%, with a margin of accuracy 2% higher.…”

“…The accuracy presented by our methodology could be corroborated by the analysis of the overlap (OR), which was close to 98% (Table 1). This result is almost 6% above the standard automatic segmentation methods that used the same database in their studies (Dai et al, 2017), and 2% higher than the best methods found in the literature, using these or other images (Shi et al, 2008;Xiong et al, 2017). Moreover, in order to reach the overlap of 96%, the highest found in literature (Shi et al, 2008), Shi and colleagues presented a methodology, which depends on a series of examinations of the same patient region.…”

“…In this evaluation, the positions of the edges of the were compared, where both edges of each of the 247 images were performed by the Canny method (Ikhsan et al, 2014). In the evaluation, the averaged and standard deviation of the Average Contour Distance (ACD), as calculated by Xiong et al (Candemir et al, 2014;Xiong et al, 2017), and the maximum error ( ) Emax between the edges of the segmented images and their Gold Standards were calculated. The values of averaged, standard deviation (std), maximum (max) and minimum (min) of the ACD and Emax indices obtained between the comparisons of edges of the 247 images segmented with their edge gold standards are presented in Table 1 (where the accuracy, precision and robustness f the method are confirmed).…”

“…Dai et al (2017) recognized standards for segmenting organs in thorax-rays through network training and correction. Also, in the work by Xiong et al (2017), the lung segmentation occurred by a weighted sparse shape composition with robust initialization.…”