Extracting a Bounded Region from a Map Using Flood Fill Algorithm

Shuaeb, S M Abdullah Al; Kamruzzaman, Md.; Ali, Mohammad Hazrat

doi:10.9734/ajrcos/2021/v7i130170

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…After filling the gaps, the regions surrounding the chest were filled with white color using a modified version of the flood fill algorithm. 8 The algorithm was applied to the left-, top-, and right-edge pixels. The ordinary flood fill algorithm connects and colors pixels with similar brightness.…”

Section: Methodsmentioning

confidence: 99%

Can the generalizability issue of artificial intelligence be overcome? Pneumothorax detection algorithm

Verdi,

Yılmaz,

Doğan Mülazimoğlu

et al. 2023

Journal of Investigative Medicine

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The generalisability of artificial intelligence (AI) models is a major issue in the field of AI applications. Therefore, we aimed to overcome the generalisability problem of an AI model developed for a particular centre for pneumothorax detection by using a small dataset for external validation. Chest radiographs of patients diagnosed with pneumothorax (n=648) and those without pneumothorax (n=650) who visited the Ankara University Faculty of Medicine (AUFM; centre 1) were obtained. A deep learning-based pneumothorax detection algorithm (PDA-Alpha) was developed using the AUFM dataset. For implementation at the Health Sciences University (HSU; centre 2), PDA-Beta was developed through external validation of PDA-Alpha using 50 radiographs with pneumothorax obtained from HSU. Both PDA algorithms were assessed using the HSU test dataset (n=200) containing 50 pneumothorax and 150 non-pneumothorax radiographs. We compared the results generated by the algorithms with those of physicians to demonstrate the reliability of the results. The areas under the curve for PDA-Alpha and PDA-Beta were 0.993 (95% confidence interval (CI): 0.985–1.000) and 0.986 (95% CI: 0.962–1.000), respectively. Both algorithms successfully detected the presence of pneumothorax on 49/50 radiographs; however, PDA-Alpha had seven false-positive predictions, whereas PDA-Beta had one. The positive predictive value increased from 0.525 to 0.886 after external validation (p=0.041). The physicians’ sensitivity and specificity for detecting pneumothorax were 0.585 and 0.988, respectively. The performance scores of the algorithms were increased with a small dataset; however, further studies are required to determine the optimal amount of external validation data to fully address the generalisability issue.

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Section: Methodsmentioning

confidence: 99%

Can the generalizability issue of artificial intelligence be overcome? Pneumothorax detection algorithm

Verdi,

Yılmaz,

Doğan Mülazimoğlu

et al. 2023

Journal of Investigative Medicine

View full text Add to dashboard Cite

show abstract

“…Based on the area lling method, starting from one pixel P (x, y), P (x, y) spreads in eight surrounding vector directions (Table 2) to achieve the denoising effect [44]. The spreading logic is: if there is no lling, ll it [45]; if it is lled, continue to search for the neighboring pixels in each of the eight vector directions with these eight pixels (Fig.…”

Section: Spread the Connected Domain Within The Knotmentioning

confidence: 99%

Knots searching and location of Chinese Fir (Cunninghamia lanceolata(lamb.) hook) constructional lumber from Fujian province, China: using contour fitting and extraction method

Zhang

et al. 2022

Preprint

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This study focuses on studying knots searching and location of Fujian Chinese Fir(FCF) constructional lumber(CL), Combining with the surface characteristics of FCF, a method for locating defects in the knots of FCF CL was developed. Combining with the surface characteristics of FCF CL, searching and locating the knots position at the trough of the profile line. The effect of knot in different position on the modulus of elasticity and the bending strength of FCF CL was studied by mechanical testing machine. This method can be used to calculate the surface defects of lumber quickly and comprehensively, so as to achieve the classification and evaluation of FCF CL according to the standard. This study is of great significance in intelligent detection, efficient production and full utilization of CL, and promotes the safe, stable and green application and development of building materials.

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Machine Vision for Knot Detection and Location in Chinese Fir Lumber

Ji,

Zhang,

Wang

et al. 2024

Forest Products Journal

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In order to be utilized in the design of a wood building, the lumber must pass grade. Machine-vision inspection grading offers higher efficiency and accuracy than traditional manual visual grading. In this paper, a fast and accurate method for identifying defects in large-size structural lumber based on machine vision of Fujian Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) constructional lumber (FCF CL) is proposed. Specifically, the grey matrix of the captured images on the surface of the sawn timber is initially scanned and the pixel weights on the edges of the image greyness variables are calculated. A matrix-valued torus was formed by fitting the knot edge profile and analyzing changes in the gradient values at the knot's edge, as well as calculating the directional derivative's rate of change. The knot three-dimensional mapping curves were projected onto the plane to form horizontal rise contours. Observe from the contour map of the whole large-size sawn timber, and extract the positional information of the knot where there is a trough (groove). The test results show that the rRMSE (Relative Root Mean Square Error) measured at the x axis position of knots is within 0.49 percent; the rRMSE measured at the y axis is 0.35 percent, which has high detection accuracy and meets the production requirements. We also investigated the effect of knots in different positions on the modulus of elasticity and the bending strength of FCF CL, with a view to establishing a link between machine-vision knot detection and mechanical properties of sawn timber in our next work.

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Extracting a Bounded Region from a Map Using Flood Fill Algorithm

Cited by 3 publications

References 5 publications

Can the generalizability issue of artificial intelligence be overcome? Pneumothorax detection algorithm

Can the generalizability issue of artificial intelligence be overcome? Pneumothorax detection algorithm

Knots searching and location of Chinese Fir (Cunninghamia lanceolata(lamb.) hook) constructional lumber from Fujian province, China: using contour fitting and extraction method

Machine Vision for Knot Detection and Location in Chinese Fir Lumber

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