Unsupervised morphological granulometric texture segmentation of digital mammograms

Baeg, S.; Batman, Sinan; Dougherty, Edward R.; Kamat, Vishnu G.; Kehtarnavaz, Nasser; S, Kim; Popov, Antony T.; Sivakumar, Krishnamoorthy; Shah, R.

doi:10.1117/1.482685

Cited by 16 publications

(3 citation statements)

References 21 publications

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“…The opening, γ B (f), of f by a compact set B at the point (x, y) is the supremum, at the point (x, y), of all translates of B in threedimensional space that fit beneath the graph of f. A granulometry generated by B takes the form γ tB (f), and the size distribution Ω(t) is defined by the volume beneath the graph of γ B (f). We assume that f has bounded support so that Ω(t) = 0 for sufficiently large t. Gray-scale granulometric moments have been used successfully for texture-based classification in medical applications (Chen et al, 1993;Chen and Dougherty, 1994;Baeg et al, 1999). For two reasons, we use a square structuring element: (1) the sieve used for the real grains has a square mesh, and (2) there are fast implementations for square structuring elements.…”

Section: Morphological Granulometriesmentioning

confidence: 99%

Morphological Granulometric Analysis of Sediment Images

Balagurunathan

Dougherty

Frančišković-Bilinski

et al. 2011

Image Anal Stereol

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Sediments are routinely analyzed in terms of the sizing characteristics of the grains of which they are composed. Via sieving methods, the grains are separated and a weight-based size distribution constructed. Various moment parameters are computed from the size distribution and these serve as sediment characteristics. This paper examines the feasibility of a fully electronic granularity analysis using digital image processing. The study uses a random model of three-dimensional grains in conjunction with the morphological method of granulometric size distributions. The random model is constructed to simulate sand, silt, and clay particle distributions. Owing to the impossibility of perfectly sifting small grains so that they do not touch, the model is used in both disjoint and non-disjoint modes, and watershed segmentation is applied in the non-disjoint model. The image-based granulometric size distributions are transformed so that they take into account the necessity to view sediment fractions at different magnifications and in different frames. Gray-scale granulometric moments are then computed using both ordinary and reconstructive granulometries. The resulting moments are then compared to moments found from real grains in seven different sediments using standard weight-based size distributions

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Section: Morphological Granulometriesmentioning

confidence: 99%

Morphological Granulometric Analysis of Sediment Images

Balagurunathan

Dougherty

Frančišković-Bilinski

et al. 2011

Image Anal Stereol

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“…They form the computation engine of an``electronic second opinion'' system to allow computer prompts of textural abnormalities in digitized or digital mammograms [19,20]. …”

Section: Conclusion and Summarymentioning

confidence: 99%

A Constant-time Algorithm for Erosions/Dilations with Applications to Morphological Texture Feature Computation

et al. 2000

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“…In fact, microcalcification on X-ray mammogram is a significant mark for early detection of breast cancer. Texture analysis methods can be applied to detect clustered microcalcification in digitized mammograms [2]. In order to improve the predictive accuracy of the classifier, the original number of feature set is reduced into smaller set using feature reduction techniques.…”

Section: Information Systems and Rough Setsmentioning

confidence: 99%

Rough Sets in Biomedical Informatics

Popov¹,

Stoykov²

2013

BIOMATH

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Abstract-The intent of this paper is to face the essentials of granular computing and in its major componentthe rough sets theory, introduced by Pawlak, since any rough set represents an information granule. As a part of modern soft computing paradigm, rough sets have been introduced as an interval-like extension of the usual sets with main applications in the intelligent systems. The proposed rough approach provides efficient algorithms for finding hidden patterns in data, finds minimal sets of data (data reduction), evaluates significance of data. Applications in medicine via DICOM standard are presented, as well as ideas for applications to microbiology.

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Unsupervised morphological granulometric texture segmentation of digital mammograms

Cited by 16 publications

References 21 publications

Morphological Granulometric Analysis of Sediment Images

Morphological Granulometric Analysis of Sediment Images

A Constant-time Algorithm for Erosions/Dilations with Applications to Morphological Texture Feature Computation

Rough Sets in Biomedical Informatics

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