Extraction of Road Network in Urban Area from Orthophoto Using Deep Learning and Douglas-Peucker Post-Processing Algorithm

Bimanjaya, Alfian; Handayani, Hepi Hapsari; Rachmadi, Reza Fuad

doi:10.1088/1755-1315/1127/1/012047

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…As for example, Bimanjaya et al used the same approach of refinement for the extraction of road network in urban area. 18 Several tests were performed to achieve a balance between the number of points delivered by the polygon contour from the binary mask, and the required accuracy in annotating the contour of the region of interest.…”

Section: Refinement and Landmarksmentioning

confidence: 99%

Automatic segmentation of histogical images of the brain of mice

Cisneros,

Lalande,

Yalcin

et al. 2024

Medical Imaging 2024: Digital and Computational Pathology

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The mouse brain offers unique features for the study of genes involved in human brain development. Specifically, genetic manipulation, such as gene inactivation, so easily achieved in the mouse, allows us to explore the effects of genes on brain morphogenesis. Using a high-throughput neuroanatomical screen on coronal and parasagittal brain sections involving 1566 mutants lines developed by the International Mouse Phenotyping Consortium, was published a list of 198 genes whose inactivation lead to neuroanatomical phenotypes. To achieve this milestone, tens of thousands of hours of segmentation were necessary since manual segmentation of a single brain takes approximately 1 hour. Our work consisted in applying deep learning methods to produce automated segmentation of 24 anatomical regions which were used in the aforementioned screen. The dataset comprises about 2000 annotated images each of 1 GB in size which required compression. Training was achieved for each region of interest and with two image resolution (512x256 and 2048x1024) using a U-Net and an Attention U-Net architecture. At 2048x1024, an overall DSC (Dice Score Coefficient) of 0.90 ± 0.01 was achieved for all 24 regions with the best performance for the total brain (DSC 0.99 ± 0.01) and the worst for the fibers of the pons (DSC 0.71 ± 0.18). Using a one command line, the end-user is now able to pre-analyze images automatically then runs the existing analytical pipeline made of ImageJ macros to validate the automatically generated regions of interest resulting. We estimate the time saved by 6 to 10 times.

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Section: Refinement and Landmarksmentioning

confidence: 99%

Automatic segmentation of histogical images of the brain of mice

Cisneros,

Lalande,

Yalcin

et al. 2024

Medical Imaging 2024: Digital and Computational Pathology

View full text Add to dashboard Cite

show abstract

“…By iteratively calculating distances and selecting the point with the maximum distance from a line segment, the algorithm removes redundant points and simplifies the contour while retaining its essential characteristics. For example, Bimanjaya et al used the same approach of refinement for the extraction of road network in urban area [18]. Several tests were performed to achieve a balance between the number of points delivered by the polygon contour from the binary mask, and the required accuracy in annotating the contour of the region of interest.…”

Section: Refinement and Landmarksmentioning

confidence: 99%

Automatic Segmentation of Histological Images of Mouse Brains

Cisneros,

Lalande,

Yalcin

et al. 2023

Algorithms

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Using a high-throughput neuroanatomical screen of histological brain sections developed in collaboration with the International Mouse Phenotyping Consortium, we previously reported a list of 198 genes whose inactivation leads to neuroanatomical phenotypes. To achieve this milestone, tens of thousands of hours of manual image segmentation were necessary. The present work involved developing a full pipeline to automate the application of deep learning methods for the automated segmentation of 24 anatomical regions used in the aforementioned screen. The dataset includes 2000 annotated parasagittal slides (24,000 × 14,000 pixels). Our approach consists of three main parts: the conversion of images (.ROI to .PNG), the training of the deep learning approach on the compressed images (512 × 256 and 2048 × 1024 pixels of the deep learning approach) to extract the regions of interest using either the U-Net or Attention U-Net architectures, and finally the transformation of the identified regions (.PNG to .ROI), enabling visualization and editing within the Fiji/ImageJ 1.54 software environment. With an image resolution of 2048 × 1024, the Attention U-Net provided the best results with an overall Dice Similarity Coefficient (DSC) of 0.90 ± 0.01 for all 24 regions. Using one command line, the end-user is now able to pre-analyze images automatically, then runs the existing analytical pipeline made of ImageJ macros to validate the automatically generated regions of interest resulting. Even for regions with low DSC, expert neuroanatomists rarely correct the results. We estimate a time savings of 6 to 10 times.

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“…The Douglas-Peucker (DP) algorithm, as a classical method in the research of basic curve feature extraction and compression, has the advantages of high computational efficiency and strong visibility; thus, it is appropriate for the extraction and dimension reduction of load curve features. However, the threshold setting of the algorithm has some limitations on the rationality of feature extraction [15]. Therefore, this study proposes a short-term load forecasting method based on the fuzzy optimization load feature identification (recognition) combined model.…”

Section: Introductionmentioning

confidence: 99%

Short-term load forecasting method based on fuzzy optimization combined model of load feature recognition

Xie,

Zhu,

et al. 2024

Electr Eng

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With the continuous development of smart grid construction and the gradual improvement of power market operation mechanisms, the importance of power load forecasting is continually increasing. In this study, a short-term load prediction method based on the fuzzy optimization combined model of load feature recognition was designed to address the problems of weak generalization ability and poor prediction accuracy of the conventional feedforward neural network prediction model. First, the Douglas–Peucker algorithm and fuzzy optimization theory of load feature recognition were analyzed, and the combined prediction model was constructed. Second, data analysis and pre-processing were performed based on the actual historical load data of a certain area and the corresponding meteorological and calendar rule information data. Finally, a practical example was used to test and analyze the short-term load forecasting effect of the fuzzy optimization combined model. The calculation results proved that the presented fuzzy optimization combined model of load feature recognition outperformed the conventional model in terms of computational efficiency and specific performance; therefore, the proposed model supports further development of actual power load prediction.

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Extraction of Road Network in Urban Area from Orthophoto Using Deep Learning and Douglas-Peucker Post-Processing Algorithm

Cited by 5 publications

References 23 publications

Automatic segmentation of histogical images of the brain of mice

Automatic segmentation of histogical images of the brain of mice

Automatic Segmentation of Histological Images of Mouse Brains

Short-term load forecasting method based on fuzzy optimization combined model of load feature recognition

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