Automatically density based breast segmentation for mammograms by using dynamic K-means algorithm and Seed Based Region Growing

Elmoufidi, Abdelali; Fahssi, Khalid El; Andaloussi, Said Jai; Sekkaki, Abderrahim

doi:10.1109/i2mtc.2015.7151324

Cited by 21 publications

(10 citation statements)

References 20 publications

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“…Region growing [32] is a classic image segmentation method. The algorithm can usually segment the connected region with the same features, and provide excellent boundary information and segmentation results.…”

Section: Nodules Extractionmentioning

confidence: 99%

An automatic detection method for lung nodules based on multi-scale enhancement filters and 3D shape features

2019

KSII TIIS

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In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.

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Section: Nodules Extractionmentioning

confidence: 99%

An automatic detection method for lung nodules based on multi-scale enhancement filters and 3D shape features

2019

KSII TIIS

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“…Many Abundant studies have been made based on diagnosing breast cancer, based on mammograms. Region of interest (ROI) is selected in [1] by using Dynamic k-means method. ROI is detected in [2] using seed based region growing and k-means method.…”

Section: Related Workmentioning

confidence: 99%

Adaptively and Automatic Detection of Breast Cancer Using Adaptive K-Means Clustering Algorithm and Classification Based on GLCM and Geometric Features Using SVM

2016

IJAERD

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Mammogram Images is very useful to detect a breast cancer because of high effectiveness and low cost. Paper presents an algorithm for detection of Breast Cancer Tumor in Mammogram. The proposed approach follows step by step procedures such as (a) Image Pre-processing (b) Image Segmentation (c) Image Post-processing (d) Feature Extraction and (e) The Classification Using SVM classifier. Images from the data set are initially preprocessed using Median Filter to Remove Noise and Adaptive Histogram Equalization to enhance image contrast. Then Segmentation is Done by Adaptive K-means Clustering Algorithm to Detect ROI. Then Region of Interest (ROI) is determined and postprocessing is Done to Remove Impurities of Segmentation. Many Various features like Gray Level Co-occurrence Matrix (GLCM) features, Statistical Features Are Extracted for Further Classification Support Vector Machine (SVM) classifier is trained Using above features. The mammography data set has been taken from the Mammographic Image Analysis Society (MIAS). Proposed System Gives Accuracy up to 94.28%.

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“…Accomplished accuracy 94%. A. Elmoufidi, K. El Fahssi, et al [13] shows a strategy for segment and distinguishes the limit of various breast tissue districts in mammograms by utilizing Seed Based Region Growing (SBRG) method and dynamic K-means clustering algorithm. G. K. Kanungo, et al [14] to diminish false outcomes, image segmented is carried out for discover breast cancer mass.…”

Section: Introductionmentioning

confidence: 99%

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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Automatically density based breast segmentation for mammograms by using dynamic K-means algorithm and Seed Based Region Growing

Cited by 21 publications

References 20 publications

An automatic detection method for lung nodules based on multi-scale enhancement filters and 3D shape features

An automatic detection method for lung nodules based on multi-scale enhancement filters and 3D shape features

Adaptively and Automatic Detection of Breast Cancer Using Adaptive K-Means Clustering Algorithm and Classification Based on GLCM and Geometric Features Using SVM

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

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