Defuzzification of spatial fuzzy sets by feature distance minimization

Sladoje, Nataša; Lindblad, Joakim; Nyström, Ingela

doi:10.1016/j.imavis.2010.08.007

Cited by 11 publications

(4 citation statements)

References 38 publications

(66 reference statements)

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“…Extending the distances to fuzzy volumes Different approaches have been proposed to measure the spatial distance between fuzzy images. The approaches described in [ 45 ] are based on defuzzification (finding a crisp representation) either by minimizing the feature distance, which leads to the problem of selecting the features, or by finding crisp representations with a higher resolution which leads to multiplication of the grid dimensions and therefore negatively impacts the efficiency of time consuming algorithms, like HD and AVD . For this evaluation tool, we use a discrete form of the approach proposed in [ 46 ] i.e.…”

Section: Introductionmentioning

confidence: 99%

Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

2015

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BackgroundMedical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics.ResultFirst we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project.ConclusionWe propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.

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Section: Introductionmentioning

confidence: 99%

Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

2015

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“…Liver is based on a binary segmented CT image [41]. Bone image is obtained from a histological CT image of a bone implant, inserted in a leg of a rabbit [36]. Cell is a binary segmented fluorescence image of a calcein-stained Chinese hamster ovary cell [24,39].…”

Section: Resultsmentioning

confidence: 99%

Limited-view binary tomography reconstruction assisted by shape centroid

Lukić

Balázs

2021

Vis Comput

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“…However, such attempts suffer from limitations that we try to overcome in our approach. For instance, in Sladoje, Lindbald & Nyström (2011) in order to find the threshold a model of the membership function is found and the threshold is calculated within an α-cut, such that the α-cut value is manually and heuristically chosen rather than in a systematic way as proposed in our approach. The choice of the most appropriate threshold using this method is thus very difficult.…”

Section: Sðm; A; B; Cþmentioning

confidence: 99%

A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

Ali

Yang

et al. 2020

PeerJ Computer Science

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Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively.

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Defuzzification of spatial fuzzy sets by feature distance minimization

Cited by 11 publications

References 38 publications

Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

Limited-view binary tomography reconstruction assisted by shape centroid

A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

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