A review of automatic mass detection and segmentation in mammographic images

Oliver, Arnau; Freixenet, Jordi; Martı́, Joan; Pérez, Elsa; Pont, Josep; Denton, Erika R. E.; Zwiggelaar, Reyer

doi:10.1016/j.media.2009.12.005

Cited by 368 publications

(246 citation statements)

References 250 publications

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“…In [62], authors illustrated a detailed study of mammograms segmentation techniques that can be done either by using a unique view of the breast, or by considering multiple views.…”

Section: B Segmentation Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

A New CAD System for Breast Microcalcifications Diagnosis

Boulehmi¹,

Mahersia²,

Hamrouni³

2016

ijacsa

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In this paper a new approach of breast microcalcifications diagnosis on digital mammograms is introduced. The proposed approach begins with a preprocessing procedure aiming artifacts and pectoral muscle removal based on morphologic operators and contrast enhancement based on galactophorous tree interpolation.The second step of the proposed CAD system consists on segmenting microcalcifications clusters, using Generalized Gaussian Density (GGD) estimation and a Bayesian backpropagation neural network.The last step is microcalcifications characterization using morphologic features which are used to feed a neuro-fuzzy system to classify the detected breast microcalcifications into benign and malignant classes.

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“…In [62], authors illustrated a detailed study of mammograms segmentation techniques that can be done either by using a unique view of the breast, or by considering multiple views.…”

Section: B Segmentation Techniquesmentioning

confidence: 99%

“…However, each technique still presents some disadvantages [62]. For example, region-based approaches depend on the seed selection and the algorithm ending conditions.…”

Section: ) Single View Lesions Detectionmentioning

confidence: 99%

A New CAD System for Breast Microcalcifications Diagnosis

Boulehmi¹,

Mahersia²,

Hamrouni³

2016

ijacsa

View full text Add to dashboard Cite

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“…Mass segmentation is crucial to the latter feature extraction and classification. Different algorithms for early lesion area detection have been widely studied, and the most common used are classical threshold method, active contour model [3][4][5][6][7][8], region growing [4], watershed [3,4], and template matching [4,9] methods. Dubey et al [3] used the level set and marker-controlled watershed methods respectively to segment mass, and experimental results showed that watershed could achieve better results.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Dong

Lǚ

et al. 2015

J Digit Imaging

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Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew's correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73 %, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.

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“…There is a wealth of ideas, concepts, and research results, as exemplified by four review papers recently published [1][2][3][4]. Various algorithms have been proposed and even their classification is a matter of personal experience and preferences.…”

Section: Introductionmentioning

confidence: 99%

“…Various algorithms have been proposed and even their classification is a matter of personal experience and preferences. Below, we follow the terminology developed by Oliver et al [1]. Detection of cancerous masses is defined as the identification of potential lesions within all the parenchymal background.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm

Bator

Nieniewski

2011

J Digit Imaging

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Optimization of brightness distribution in the template used for detection of cancerous masses in mammograms by means of correlation coefficient is presented. This optimization is performed by the evolutionary algorithm using an auxiliary mass classifier. Brightness along the radius of the circularly symmetric template is coded indirectly by its second derivative. The fitness function is defined as the area under curve (AUC) of the receiver operating characteristic (ROC) for the mass classifier. The ROC and AUC are obtained for a teaching set of regions of interest (ROIs), for which it is known whether a ROI is true-positive (TP) or false-positive (F). The teaching set is obtained by running the mass detector using a template with a predetermined brightness. Subsequently, the evolutionary algorithm optimizes the template by classifying masses in the teaching set. The optimal template (OT) can be used for detection of masses in mammograms with unknown ROIs. The approach was tested on the training and testing sets of the Digital Database for Screening Mammography (DDSM). The free-response receiver operating characteristic (FROC) obtained with the new mass detector seems superior to the FROC for the hemispherical template (HT). Exemplary results are the following: in the case of the training set in the DDSM, the true-positive fraction (TPF)=0.82 for the OT and 0.79 for the HT; in the case of the testing set, TPF=0.79 for the OT and 0.72 for the HT. These values were obtained for disease cases, and the false-positive per image (FPI)=2.

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A review of automatic mass detection and segmentation in mammographic images

Cited by 368 publications

References 250 publications

A New CAD System for Breast Microcalcifications Diagnosis

A New CAD System for Breast Microcalcifications Diagnosis

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm

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