Dynamic Measurement of Computer Generated Image Segmentations

Levine, Martin D.; Nazif, Ahmed M.

doi:10.1109/tpami.1985.4767640

Cited by 313 publications

(142 citation statements)

References 12 publications

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“…These methods characterize different segmentation algorithms by simply computing the goodness measures based on the segmented image without the a priori knowledge of the correct segmentation. [20] The application of these evaluation methods exempts the requirement for references, so that they can be used for on-line evaluation.…”

Section: Empirical Goodness Methodsmentioning

confidence: 99%

“…[23] The uniformity of a feature over a region can be computed on the basis of the variance of that feature evaluated at every pixel belonging to that region. [20] In particular, for a gray-level image f(x,y), let R i be ith segmented region, A i be the area of R i , then the gray-level uniformity measure (GU) of f(x,y) is:…”

Section: Goodness Based On Intra-region Uniformitymentioning

confidence: 99%

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A survey on evaluation methods for image segmentation

Zhang

1996

Pattern Recognition

916

501

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This paper studies different methods proposed so far for segmentation evaluation. Most methods can be classified into three groups: the analytical, the empirical goodness and the empirical discrepancy groups. Each group has its own characteristics. After a brief description of each method in every group, some comparative discussions about different method groups are first carried out. An experimental comparison for some empirical (goodness and discrepancy) methods commonly used is then performed to provide a rank of their evaluation abilities. In addition, some special methods are also discussed. This study is helpful for an appropriate use of existing evaluation methods and for improving their performance as well as for systematically designing new evaluation methods.

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Section: Empirical Goodness Methodsmentioning

confidence: 99%

Section: Goodness Based On Intra-region Uniformitymentioning

confidence: 99%

A survey on evaluation methods for image segmentation

Zhang

1996

Pattern Recognition

916

501

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show abstract

“…Unsupervised evaluation methods evaluate a segmented image based on how well it matches a broad set of characteristics of segmented images as desired by humans [20]- [25]. For evaluating proposed segmentation technique, we choose inter-region contrast, intra-region uniformity and combination of intra-region and inter-region disparity suggested by Levine and Nazif [23].…”

Section: Appraisal Normsmentioning

confidence: 99%

PET Image Segmentation Based on Gabor Annulus Filtering and Region Growing

Devi¹,

Mini²

2016

IJCTE

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Abstract-Positron emission tomography (PET) image segmentation is a difficult task due to low resolution, high contrast, large variability of pathology, high uncertainties in object boundaries and the inherent noise of PET images. A two staged automatic segmentation technique based on Gabor Annulus filtering and region growing is proposed here. Gabor Annulus filtering is applied on preprocessed image and the result is the input for region growing based on automatic seed selection and threshold determination. Unsupervised evaluation schemes are used for appraising the proposed technique. 93.5% of the lesions considered for validation have been obtained correctly in the segmented image.Index Terms-Gabor annulus filtering, intra-inter disparity, intra region uniformity, inter region contrast, positron emission tomography, region growing.

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“…While in domains such as edge detection this may be useful, in general the lack of a general theory of image segmentation limits these methods; empirical goodness methods, which compute some manner of 'goodness' metric such as uniformity within regions [3], contrast between regions [4], shape of segmented regions [12]; and finally, empirical discrepancy methods, which evaluate segmentation algorithms by comparing the segmented image against a manually-segmented reference image, which is often referred to as ground truth, and computes error measures.…”

Section: Previous Workmentioning

confidence: 99%

Performance Evaluation of Image Segmentation

Monteiro¹,

Campilho

2006

Lecture Notes in Computer Science

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Abstract. In spite of significant advances in image segmentation techniques, evaluation of these methods thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images that are evaluated by some method, or it is otherwise left to subjective evaluation by the reader. We propose a new approach for evaluation of segmentation that takes into account not only the accuracy of the boundary localization of the created segments but also the under-segmentation and over-segmentation effects, regardless to the number of regions in each partition. In addition, it takes into account the way humans perceive visual information. This new metric can be applied both to automatically provide a ranking among different segmentation algorithms and to find an optimal set of input parameters of a given algorithm.

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Dynamic Measurement of Computer Generated Image Segmentations

Cited by 313 publications

References 12 publications

A survey on evaluation methods for image segmentation

A survey on evaluation methods for image segmentation

PET Image Segmentation Based on Gabor Annulus Filtering and Region Growing

Performance Evaluation of Image Segmentation

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