Pixel Relaxation Labelling Using A Diminishing Neighbourhood Effect

Lee, T.; Richards, John A.

doi:10.1109/igarss.1989.578881

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Remote sensing problems, such as land cover classification at the rural-urban fringe, have been analyzed by assuming a probabilistic relaxation of spatial information [16][17][18][19]. More recent research has incorporated context information by using a Markov random field (MRF), which models the spatial correlations between neighboring ROIs by modeling the joint probability of observation and the corresponding class for processing of remote sensing images [20,21].…”

Section: Introductionmentioning

confidence: 99%

Regional Context-Sensitive Support Vector Machine Classifier to Improve Automated Identification of Regional Patterns of Diffuse Interstitial Lung Disease

et al. 2011

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We propose the use of a context-sensitive support vector machine (csSVM) to enhance the performance of a conventional support vector machine (SVM) for identifying diffuse interstitial lung disease (DILD) in high-resolution computerized tomography (HRCT) images. Nine hundred rectangular regions of interest (ROIs), each 20 × 20 pixels in size and consisting of 150 ROIs representing six regional disease patterns (normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation), were marked by two experienced radiologists using consensus HRCT images of various DILD. Twenty-one textual and shape features were evaluated to characterize the ROIs. The csSVM classified an ROI by simultaneously using the decision value of each class and information from the neighboring ROIs, such as neighboring region feature distances and class differences. Sequential forward-selection was used to select the relevant features. To validate our results, we used 900 ROIs with fivefold cross-validation and 84 whole lung images categorized by a radiologist. The accuracy of the proposed method for ROI and whole lung classification (89.88 ± 0.02%, and 60.30 ± 13.95%, respectively) was significantly higher than that provided by the conventional SVM classifier (87.39 ± 0.02%, and 57.69 ± 13.31%, respectively; paired t test, p < 0.01, and p < 0.01, respectively). We conclude that our csSVM provides better overall quantification of DILD.

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Section: Introductionmentioning

confidence: 99%

Regional Context-Sensitive Support Vector Machine Classifier to Improve Automated Identification of Regional Patterns of Diffuse Interstitial Lung Disease

et al. 2011

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“…Noting also that the nearest neighbours should be most influential, with those further out being less important, a useful variation is to reduce the values of the neighbour weights d n as iteration proceeds so that after say 5 to 10 iterations they have been brought to zero. Further iterations will then have no effect, and degradation in labelling accuracy cannot occur (Lee and Richards, 1989). Figure 8.9 illustrates a simple application of relaxation labelling, in which a hypothetical image of 100 pixels has been classified into just two classes -grey and white.…”

Section: The Final Step -Stopping the Processmentioning

confidence: 99%

“…Figure 8.10c shows the final labelling, which has an accuracy of 72.4%. Full details of this example are available in Lee and Richards (1989).…”

Section: Examplesmentioning

confidence: 99%