Automatic breast masses boundary extraction in digital mammography using spatial fuzzy c-means clustering and active contour models

Mencattini, Arianna; Salmeri, Marcello; Casti, Paola; Raguso, Grazia; L‘Abbate, Samuela; Chieppa, L; Ancona, Antonietta; Mangieri, Fabio; Pepe, Maria

doi:10.1109/memea.2011.5966747

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…Overlap criteria have been used by several mass segmentation techniques [18] [19] and is described by equation 6. We give below the principle of our evaluation method.…”

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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“…Overlap criteria have been used by several mass segmentation techniques [18] [19] and is described by equation 6. We give below the principle of our evaluation method.…”

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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“…In this paper, we used the boundary signature-based features techniques [23] to extract features from thermogram images. A boundary signature is defined as a 1-D representation of this boundary.…”

Section: Feature Extraction Stagementioning

confidence: 99%

Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques

2020

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Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%.

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“…Nowadays, cancer is a most eminent public health problem in the world. According to the world health organization (WHO), breast cancer and meningioma brain tumor are considered as most common primary cancer in adults [1]. Moreover, they recur despite aggressive treatment, leading to substantial morbidity.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and segmentation of medical images for MRI and Digital mammogram

Tinhinane¹,

Boudrioua²,

Aloui³

2019

Med. Technol. J.

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Developing a computer aided diagnosis system (CAD) is an extremelychallenging task. One of the major goals of CAD is to help the radiologist to makegood decisions by detecting and analyzing characteristics of benign and malignant lesions. In this context, we present accurate and automatic method that, detect and extract malignancy descriptors of breast and meningioma brain tumor.Our applied an algorithm that uses enhancement image based on homomorphicfiltering and adaptive histogram equalization technique. This work was proposedby Zhang Chaofu et al. [3]. A region of interest is determinated using K meansclustering. And then, we employed basically wavelet transform to extract pertinent features for meningioma tumor, geometric and texture characteristics forbreast tumor in order to classify malignancy lesion.

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Automatic breast masses boundary extraction in digital mammography using spatial fuzzy c-means clustering and active contour models

Cited by 11 publications

References 7 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

Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques

Feature extraction and segmentation of medical images for MRI and Digital mammogram

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