Mass segmentation in mammograms based on the minimisation of energy and active contour model

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

doi:10.1109/memea.2014.6860106

Cited by 2 publications

(2 citation statements)

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“…But to have quantitative results, we take mammogram image contain lesions and calculate the rate overlapped between the regions segmented by our method and the critical regions segmented by a radiologist. The evaluation criterion is the overlapped area ratio [21] [26]. The proposed method is tested by using the Mini-MIAS database [20] as previously mentioned in section II-(A), the same database has also been used in other researches, such as mass segmentation, microcalcification detection, and automatic mammogram classification [24].…”

Section: Resultsmentioning

confidence: 99%

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

Elmoufidi

Fahssi

Andaloussi

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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This paper presents a method for segment and detects the boundary of different breast tissue regions in mammograms by using dynamic K-means clustering algorithm and Seed Based Region Growing (SBRG) techniques. Firstly, the K-means clustering is applied for dynamically and automatically generated the seeds points and determines the thresholds' values for each region. Secondly, the region growing algorithm is used with previously generated input parameters to divide mammogram into homogeneous regions according to the intensity of the pixel. The main goal of this method is to automatically segment and detect the boundary of different disjoint breast tissue regions in image mammography. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and qualitative and quantitative evaluation of density changes. So, using a computer-aided detection/diagnosis (CAD/CADx) system as supplement to the radiologists' assessment has an important role. In order to evaluate our proposed method we used the wellknown Mammographic Image Analysis Society (MIAS) database. The obtained qualitative and quantitative results demonstrate the efficiency of this method and confirm the possibility of its use in improving the computer-aided detection/diagnosis.

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Section: Resultsmentioning

confidence: 99%

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

Elmoufidi

Fahssi

Andaloussi

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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“…The ROC shows that the Prewitt edge detection, sober, and LoG were 79.0%, 72.0%, and 71.0%, respectively. Khalid et al [131] proposed mass segmentation in mammograms based on energy minimisation and an active contour model. The method uses two approaches: (a) level set theory and (b) minimisation of the active contours energy.…”

Section: Biomed Research Internationalmentioning

confidence: 99%

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Michael

et al. 2021

BioMed Research International

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Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.

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Mass segmentation in mammograms based on the minimisation of energy and active contour model

Cited by 2 publications

References 0 publications

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

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

Breast Cancer Segmentation Methods: Current Status and Future Potentials

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