Minimum error thresholding

Kittler, Josef; Illingworth, J.

doi:10.1016/0031-3203(86)90030-0

Cited by 1,911 publications

(948 citation statements)

References 5 publications

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“…We assume Gaussian distribution due to the histogram of the similarity. Thus, we apply the KI (Kittler and Illingworth) thresholding, which is a typical threshold method that assumes and applies the Gaussian distribution of the dataset, to the similarity weight image (Kittler and Illingworth, 1986).…”

Section: Threshold Selection Processmentioning

confidence: 99%

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

Jeon¹,

Kim²,

Kim³

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Among the various remote sensing sensors compared to the electro-optical sensors, SAR (Synthetic Aperture Radar) is very suitable for assessing damaged areas induced by disaster events owing to its all-weather day and night acquisition capability and sensitivity to geometric variables. The conventional CD (Change Detection) method that uses two-date data is typically used for mapping damage over extensive areas in a short time, but because data from only two dates are used, the information used in the conventional CD is limited. In this paper, we propose a novel CD method that is extended to use data consisting of two pre-disaster SAR data and one post-disaster SAR data. The proposed CD method detects changes by using a similarity weight image derived from the neighborhood information of a pixel in the data from the three dates. We conducted an experiment using three single polarization ALOS PALSAR (Advanced Land Observing Satellite/Phased Array Type L-Band) data collected over Miyagi, Japan which was seriously damaged by the 2011 east Japan tsunami. The results demonstrated that the mapping accuracy for damaged areas can be improved by about 26% with an increase of the g-mean compared to the conventional CD method. These improved results prove the performance of our proposed CD method and show that the proposed CD method is more suitable than the conventional CD method for detecting damaged areas induced by disaster. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Threshold Selection Processmentioning

confidence: 99%

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

Jeon¹,

Kim²,

Kim³

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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“…Representative subvolumes of normal tissue were identified visually, and the background noise in these subvolumes was approximated by a Gaussian distribution. 33,34 The marked subvolumes containing leaks were then thresholded to the noise-mean þ 3s.d.. Each voxel with intensity level greater than the threshold was counted as part of the leak, and the threshold-subtracted intensity counted as the leak intensity. Total leak volume was calculated by summing the number of voxels that were within the leak.…”

Section: Image Processingmentioning

confidence: 99%

Cerebral Vascular Leak in a Mouse Model of Amyloid Neuropathology

Tanifum

Starosolski

Fowler

et al. 2014

J Cereb Blood Flow Metab

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In Alzheimer's disease (AD), there is increasing evidence of blood-brain barrier (BBB) compromise, usually observed as 'microbleeds' correlated with amyloid plaque deposition and apoE-e4 status, raising the possibility of nanotherapeutic delivery. Molecular probes have been used to study neurovascular leak, but this approach does not adequately estimate vascular permeability of nanoparticles. We therefore characterized cerebrovascular leaks in live APP þ transgenic animals using a long circulating B100 nm nanoparticle computed tomography (CT) contrast agent probe. Active leaks fell into four categories: (1) around the dorsomedial cerebellar artery (DMCA), (2) around other major vessels, (3) nodular leaks in the cerebral cortex, and (4) diffuse leaks. Cortical leaks were uniformly more frequent in the transgenic animals than in age-matched controls. Leaks around vessels other than the DMCA were more frequent in older transgenics compared with younger ones. All other leaks were equally prevalent across genotypes independent of age. Ten days after injection, 4 to 5 mg of the dose was estimated to be present in the brain, roughly a half of which was in locations other than the leaky choroid plexus, and associated with amyloid deposition in older animals. These results suggest that amyloid deposition and age increase delivery of nanoparticle-borne reagents to the brain, in therapeutically relevant amounts.

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“…Most of the thresholding algorithms are based on parametric approaches that assume a predefined statistical model in all change and no-change classes [2,3,5,10,12]. But, due to the dynamic behavior of the changing area and its complex nature, assuming a predefined statistical model for change class may not be valid in all cases.…”

Section: Change Mappingmentioning

confidence: 99%

“…Most of the above thresholding algorithms are applicable to bi-level thresholding [2,3,9,10]. The histogram of the image is assumed to have one valley between two peaks.…”

Section: Introductionmentioning

confidence: 99%

Coupling of Thresholding and Region Growing Algorithm for Change Detection in Sar Images

Mishra¹,

Susaki²

2013

PIER

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Abstract-In this research paper, we propose supervised and unsupervised change detection methodologies focused on the analysis of multitemporal Synthetic Aperture Radar (SAR) images. These approaches are based on three main steps: (1) a comparison of multitemporal image was carried out by normalized difference ratio (NDR) operator; (2) implementing a novel supervised or unsupervised thresholding and (3) generating the change map by coupling of thresholding along with a region growing algorithm. In the first step, two filtered multitemporal images were used to generate NDR image that was subjected to analysis. In the second step, by assuming a Gaussian distribution in the no-change area, we identified the pixel range that fits the Gaussian distribution better than any other range iteratively to detect the no-change area that eventually separates the change areas. In the supervised method, several sample no-change pixels were selected and the mean (μ) and the standard deviation (σ) were obtained. Then, μ ± 3σ was applied to select the best threshold values. Finally, a traditional thresholding algorithm was modified and implemented with the coupling of the region growing algorithm to consider the spatial information to generate the change map. The Gaussian distribution was assumed because it better fits the conditional densities of the no-change class in the NDR image. The effectiveness of the proposed methods was verified with the simulated images and the real images associated with geographical locations. The results were compared with the manual trial and error procedure (MTEP) and traditional unsupervised expectation-maximization (EM) method. Both proposed methods gave similar results with MTEP and significant improvement in Kappa coefficient in comparison to the traditional EM method was found in both geographical locations. 520 Mishra and SusakiThe coupling of the modified thresholding with the region growing algorithm is very effective with all methods.

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Minimum error thresholding

Cited by 1,911 publications

References 5 publications

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

Cerebral Vascular Leak in a Mouse Model of Amyloid Neuropathology

Coupling of Thresholding and Region Growing Algorithm for Change Detection in Sar Images

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