Iterative fuzzy segmentation for an accurate delimitation of the breast region

Touil, Asma; Kalti, Karim

doi:10.1016/j.cmpb.2016.04.020

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…Fig. 8 shows the energy of each image at FCM of second algorithm, so we can see that the frequency reaches (100) but the actual effect is limited between (15)(16)(17) which explains that second algorithm action lasts longer than that of first algorithm, but the accuracy in breaking down the second algorithm is better than what first algorithm gives as shown in the results of Tables 1 and 2. The algorithms proposed will be compared with previous studies to fully evaluate the performance of the proposed algorithms, where the comparison was made on the measure of accuracy. We compared the studies using the MIAS database only (work scope).…”

Section: Results Of Second Algorithmmentioning

confidence: 91%

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Mammography Images Segmentation via Fuzzy C-mean and K-mean

Kamil¹,

Salih²

2019

IJIES

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Breast Cancer is one of the common and dangerous among women at the age of forty, so it is better for woman to have mammography testing as a significant step for the early detection of breast cancer and is diagnosis for treatment; There is an important need to an algorithm is used to determine the boundaries of the tumor in a finite accuracy. In this work, two algorithms were built depending on clustering approach as segmentation method. In the first algorithm has employed (K-mean) method, whilst in the second algorithm has employed fuzzy c-mean method (FCM). In both, the lazy snapping algorithm was used as an additional step to improve the segmentation performance of the detection of abnormal area. The proposed methods have been tested using mini-MIAS database, after assessment the results obtained. it indicates the accuracy of segmentation first algorithm, are 91.18% and accuracy of second algorithm is 94.12%. from results, it concludes that the proposed second algorithm is capable of estimate breast abnormal region boundary at high accuracy because it used fuzzy logic technique.

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Section: Results Of Second Algorithmmentioning

confidence: 91%

“…The division execution from experimentations shows that our methods outflanks the other looked at methods. Touil and K. Kalti, [16] proposed another region-based method to appropriately portion breast and background regions in mammographic images. These locales are assessed by an Iterative Fuzzy Breast Segmentation method (IFBS).…”

Section: Introductionmentioning

confidence: 99%