Hysteresis thresholding based edge detectors for inscriptional image enhancement

Sornam, M.; Kavitha, M.; Nivetha, M.

doi:10.1109/iccic.2016.7919568

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…To obtain the final segmented image, the edge probability map produced by the CellsDeepNet model was binarized and skeletonized to generate the one-pixel wide borders of the endothelial cells. In this step, the Hysteresis thresholding method was utilized to generate the binary image from the obtained edge probability map [33]. Using the Hysteresis thresholding method, all pixels with intensity value above the upper threshold T up are defined as cell boundary pixels.…”

Section: Network Architecturementioning

confidence: 99%

CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells

Al-Waisy¹,

Alruban

Al-Fahdawi

et al. 2022

Mathematics

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The quantification of corneal endothelial cell (CEC) morphology using manual and semi-automatic software enables an objective assessment of corneal endothelial pathology. However, the procedure is tedious, subjective, and not widely applied in clinical practice. We have developed the CellsDeepNet system to automatically segment and analyse the CEC morphology. The CellsDeepNet system uses Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast of the CEC images and reduce the effects of non-uniform image illumination, 2D Double-Density Dual-Tree Complex Wavelet Transform (2DDD-TCWT) to reduce noise, Butterworth Bandpass filter to enhance the CEC edges, and moving average filter to adjust for brightness level. An improved version of U-Net was used to detect the boundaries of the CECs, regardless of the CEC size. CEC morphology was measured as mean cell density (MCD, cell/mm2), mean cell area (MCA, μm2), mean cell perimeter (MCP, μm), polymegathism (coefficient of CEC size variation), and pleomorphism (percentage of hexagonality coefficient). The CellsDeepNet system correlated highly significantly with the manual estimations for MCD (r = 0.94), MCA (r = 0.99), MCP (r = 0.99), polymegathism (r = 0.92), and pleomorphism (r = 0.86), with p < 0.0001 for all the extracted clinical features. The Bland–Altman plots showed excellent agreement. The percentage difference between the manual and automated estimations was superior for the CellsDeepNet system compared to the CEAS system and other state-of-the-art CEC segmentation systems on three large and challenging corneal endothelium image datasets captured using two different ophthalmic devices.

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Section: Network Architecturementioning

confidence: 99%

CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells

Al-Waisy¹,

Alruban

Al-Fahdawi

et al. 2022

Mathematics

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“…The last step consists of an automatic enhancement of the classification map. Our method is inspired by the hysteresis thresholding algorithm (Sornam et al, 2016). The novelty consists in combining deep learning-based classification results approved by an additional external data source, with unsupervised classification results, using the principles similar to hysteresis thresholding.…”

Section: Automatic Correction Of the Classification Map To Fit The Cloud-free Mosaicmentioning

confidence: 99%

Marrying Deep Learning and Data Fusion for Accurate Semantic Labeling of Sentinel-2 Images

Fonteix¹,

Swaine²,

Leras³

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The understanding of the Earth through global land monitoring from satellite images paves the way towards many applications including flight simulations, urban management and telecommunications. The twin satellites from the Sentinel-2 mission developed by the European Space Agency (ESA) provide 13 spectral bands with a high observation frequency worldwide. In this paper, we present a novel multi-temporal approach for land-cover classification of Sentinel-2 images whereby a time-series of images is classified using fully convolutional network U-Net models and then coupled by a developed probabilistic algorithm. The proposed pipeline further includes an automatic quality control and correction step whereby an external source can be introduced in order to validate and correct the deep learning classification. The final step consists of adjusting the combined predictions to the cloud-free mosaic built from Sentinel-2 L2A images in order for the classification to more closely match the reference mosaic image.

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Comparison of Threshold Methods Useful in Handwritten Marathi Character Recognition

Khobragade

Lanjewar

Makesar

2021

Algorithms for Intelligent Systems

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Hysteresis thresholding based edge detectors for inscriptional image enhancement

Cited by 4 publications

References 9 publications

CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells

CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells

Marrying Deep Learning and Data Fusion for Accurate Semantic Labeling of Sentinel-2 Images

Comparison of Threshold Methods Useful in Handwritten Marathi Character Recognition

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