A 3D multi-scale Block LBP Filter for lung nodule enhancement based on the CT images

Xu, Fan; Zhang, Wenjing; Li, Xinyue; Hu, Xiao; Peng, Shaohu; Nam, Hyun Do; Zhang, Mian-Feng

doi:10.1109/cisp-bmei.2016.7852931

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Wiener filter with contrast limited adaptive histogram equalization (CLAHE) is applied in [14]. The selective enhancement filter is applied in [18]- [20]. A multi-scale selective enhancement filter was applied in [14].…”

Section: A Noise Denoising and Image Enhancementsmentioning

confidence: 99%

“…Gabor filter was examined in [17]. Local binary patterns (LBP) filter was used in [18], fuzzy filter adopted in [19], the sequential filter was implemented in [20] was used as a pre-processing step Shell filter examined in [21].…”

Section: A Noise Denoising and Image Enhancementsmentioning

confidence: 99%

“…In the end, two lung regions were extracted from the labeled image, the largest and next largest connected regions, and the rest of the segmented regions set as the background [23], [24]. The threshold segmentation of the lungs is the work of [18], [19], [21]- [23], [25]- [27], [28]- [36]. Otsu threshold [28], [37], segmentation on the basis of the iterative threshold [9], [38], [39], adaptive threshold [26], [40], The literature has an adaptive 3-D fuzzy threshold [41].…”

Section: B Lung Segmentationmentioning

confidence: 99%

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Computer-aided diagnostic system kinds and pulmonary nodule detection efficacy

Kadhim

Motlak²,

Abdalla³

2022

IJECE

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This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.

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