2016
DOI: 10.1063/1.4958602
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Analysis of shape features for lesion classification in breast ultrasound images

Abstract: Abstract. Classification accuracy in image processing is highly related to previous steps such as feature extraction and feature selection. In breast ultrasound imaging, lesion classification is performed based on several criteria, including edge regularity. This research aims at implementing and evaluating some shape features for edge regularity classification. Breast lesion is divided into two classes based on edge regularity : regular and non-regular. Several shape features is implemented and evaluated by m… Show more

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Cited by 6 publications
(2 citation statements)
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“…The parameter "compactness" is measured by dividing spheroid area by its squared perimeter, with the circle being the object with the most compact shape and, therefore, with the maximum value of compactness, which is 1 [48]. Finally, solidity, which is an indicator of the roughness of the spheroidal surface, was determined in order to assess spheroids' regularity [49].…”
Section: Morphological Analysismentioning
confidence: 99%
“…The parameter "compactness" is measured by dividing spheroid area by its squared perimeter, with the circle being the object with the most compact shape and, therefore, with the maximum value of compactness, which is 1 [48]. Finally, solidity, which is an indicator of the roughness of the spheroidal surface, was determined in order to assess spheroids' regularity [49].…”
Section: Morphological Analysismentioning
confidence: 99%
“…Major axis length mengalami kendala ketika dilakukan normalisasi rotasi karena sensitif terhadap noise [7]. Persamaan aspek rasio [8][9] adalah rasio antara major axis length dengan minor axis length, sehingga aspek rasio juga tidak dapat dijadikan sebagai fitur yang invariant tehadap rotasi [10]. Perhitungan GLCM sensitif terhadap noise [11].…”
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