Mammogram Segmentation by Contour Searching and Mass Lesions Classification With Neural Network

Cascio, Donato; Fauci, F.; Magro, R.; Raso, G.; Bellotti, R.; Carlo, Francesco De; Tangaro, Sabina; Nunzio, Giorgio De; Quarta, Maurizio; Forni, G.; Lauria, A.; Fantacci, Maria Evelina; Retico, Alessandra; Masala, G. L.; Oliva, Piernicola; Bagnasco, S.; Cheran, S. C.; Torres, E. López

doi:10.1109/tns.2006.878003

Cited by 94 publications

(23 citation statements)

References 14 publications

(12 reference statements)

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“…Some other approaches are based on the edge detection of mammogram components [5,10,21,25,28,34,37] We also find, in this category, clustering based approaches. They consist of detecting clusters which may represent an eventual tumor [12,48].…”

Section: ) Single View Lesions Detectionmentioning

confidence: 69%

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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Section: ) Single View Lesions Detectionmentioning

confidence: 69%

A New CAD System for Breast Microcalcifications Diagnosis

Boulehmi¹,

Mahersia²,

Hamrouni³

2016

ijacsa

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“…The author reported a detection rate of 80% with 1.1 FPs/I. Cascio et al [23] first used an edge-based algorithm to segment the boundary of a ROI. Then geometrical features and shape features were extracted.…”

Section: Mass Detectionmentioning

confidence: 99%

A Survey of Computer-aided Detection of Breast Cancer with Mammography

Yanfeng¹,

Chen²,

Chen³

et al. 2016

J Health Med Inform

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Computer-aided detection (CAD) systems can be served as a second view for radiologist. An overview of recent development in CAD methods are presented in this paper. Abnormalities detection, abnormalities classification and content-based image retrieval (CBIR) are briefly reviewed. For the abnormalities detection, micro-calcification detection, mass detection and multi-view based detection are introduced. For the abnormalities classification, micro-calcification classification and mass classification are given.

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“…The classifiers can be combined to improve the classification rate: in (Constantinidis, 2001) five different classifiers such as multivariate Gaussian classifier (MVG), radial basis function (RBF), Q-vector median (QVM), 1-nearest neighbour (1NN) and hyperspheric Parzen Windows (PZN) are combined to detect masses in mammograms. Cascio et al in (Cascio, 2006) performed a method for detecting masses in mammographic images consisting of two steps: image segmentation by contour searching and mass lesions classifications with neural network. A method for automatic detection of mammographic masses is performed by Domìnguez and Nandi (Domìnguez, 2008), it is based on regions segmentation and ranking.…”

Section: State Of the Artmentioning

confidence: 99%

An Unsupervised Method for Suspicious Regions Detection in Mammogram Images

Insalaco

Bruno

Farruggia

et al. 2015

Proceedings of the International Conference on Pattern Recognition Applications and Methods

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Abstract:Over the past years many researchers proposed biomedical imaging methods for computer-aided detection and classification of suspicious regions in mammograms. Mammogram interpretation is performed by radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method to automatically detect suspicious regions in mammogram images. The method consists mainly of two steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background region from the breast profile region. In greater detail, gray levels mapping transform and histogram specifications are used to enhance the visual representation of mammogram details. Then, local keypoints and descriptors such as SURF have been extracted in breast profile region. The extracted keypoints are filtered by proper parameters tuning to detect suspicious regions. The results, in terms of sensitivity and confidence interval are very encouraging.

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Mammogram Segmentation by Contour Searching and Mass Lesions Classification With Neural Network

Cited by 94 publications

References 14 publications

A New CAD System for Breast Microcalcifications Diagnosis

A New CAD System for Breast Microcalcifications Diagnosis

A Survey of Computer-aided Detection of Breast Cancer with Mammography

An Unsupervised Method for Suspicious Regions Detection in Mammogram Images

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