Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

Quintanilla-Domínguez, J.; Ojeda-Magaña, Benjamín; Marcano-Cedeño, Alexis; Cortina-Januchs, M. G.; Vega-Corona, A.; Andina, Diego

doi:10.1186/1687-6180-2011-91

Cited by 11 publications

(12 citation statements)

References 33 publications

(46 reference statements)

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“…In addition, other clustering techniques aim Fig. 1 Schematic presentation of the BI-RADS microcalcification morphologies [9] at segmenting targeted MCs from their surrounding pixels based on identifying adequate threshold values [12,[28][29][30], converging active contours [12,[28][29][30] or minimizing an objective function [6,16,23,[31][32][33].…”

Section: Related Workmentioning

confidence: 99%

A new conditional region growing approach for microcalcification delineation in mammograms

Touil

Kalti

Conze

et al. 2021

Med Biol Eng Comput

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Microcalcifications (MCs) are considered as the first indicator of breast cancer development. Their morphology, in terms of shape and size, is considered as the most important criterion that determines their malignity degrees. Therefore, the accurate delineation of MC is a cornerstone step in their automatic diagnosis process. In this paper, we propose a new conditional region growing (CRG) approach with the ability of finding the accurate MC boundaries starting from selected seed points. The starting seed points are determined based on regional maxima detection and superpixel analysis. The region growing step is controlled by a set of criteria that are adapted to MC detection in terms of contrast and shape variation. These criteria are derived from prior knowledge to characterize MCs and can be divided into two categories. The first one concerns the neighbourhood searching size. The second one deals with the analysis of gradient information and shape evolution within the growing process. In order to prove the effectiveness and the reliability in terms of MC detection and delineation, several experiments have been carried out on MCs of various types, with both qualitative and quantitative analysis. The comparison of the proposed approach with state-of-the art proves the importance of the used criteria in the context of MC delineation, towards a better management of breast cancer.

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Section: Related Workmentioning

confidence: 99%

A new conditional region growing approach for microcalcification delineation in mammograms

Touil

Kalti

Conze

et al. 2021

Med Biol Eng Comput

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“…The division of the groups in this way is particularly interesting when we try to get information about a particular subset. For example, in the work of [25] they show how important the identification of the atypical data cloud is, in order to arrive at a medical diagnosis.…”

Section: Application Of the Typicality Concept To Numerical Data Setsmentioning

confidence: 99%

Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm

Ojeda-Magaña

Ruelas

Nakamura

et al. 2013

Mathematical Problems in Engineering

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We take the concept of typicality from the field of cognitive psychology, and we apply the meaning to the interpretation of numerical data sets and color images through fuzzy clustering algorithms, particularly the GKPFCM, looking to get better information from the processed data. The Gustafson Kessel Possibilistic Fuzzy c-means (GKPFCM) is a hybrid algorithm that is based on a relative typicality (membership degree, Fuzzy c-means) and an absolute typicality (typicality value, Possibilistic c-means). Thus, using both typicalities makes it possible to learn and analyze data as well as to relate the results with the theory of prototypes. In order to demonstrate these results we use a synthetic data set and a digitized image of a glass, in a first example, and images from the Berkley database, in a second example. The results clearly demonstrate the advantages of the information obtained about numerical data sets, taking into account the different meaning of typicalities and the availability of both values with the clustering algorithm used. This approach allows the identification of small homogeneous regions, which are difficult to find.

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“…In addition, in [20], partitional clustering algorithms are considered for image segmentation because of the great similarity between segmentation and clustering, although clustering was developed for feature space, whereas segmentation was developed for the spatial domain of an image. Also in [21], a method using selforganizing map (SOM)-based spectral clustering is proposed for agriculture management.…”

Section: Introductionmentioning

confidence: 99%

Identification of piecewise affine systems based on fuzzy PCA-guided robust clustering technique

Khanmirza

Nazarahari

Mousavi

2016

EURASIP J. Adv. Signal Process.

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Hybrid systems are a class of dynamical systems whose behaviors are based on the interaction between discrete and continuous dynamical behaviors. Since a general method for the analysis of hybrid systems is not available, some researchers have focused on specific types of hybrid systems. Piecewise affine (PWA) systems are one of the subsets of hybrid systems. The identification of PWA systems includes the estimation of the parameters of affine subsystems and the coefficients of the hyperplanes defining the partition of the state-input domain. In this paper, we have proposed a PWA identification approach based on a modified clustering technique. By using a fuzzy PCAguided robust k-means clustering algorithm along with neighborhood outlier detection, the two main drawbacks of the well-known clustering algorithms, i.e., the poor initialization and the presence of outliers, are eliminated. Furthermore, this modified clustering technique enables us to determine the number of subsystems without any prior knowledge about system. In addition, applying the structure of the state-input domain, that is, considering the time sequence of input-output pairs, provides a more efficient clustering algorithm, which is the other novelty of this work. Finally, the proposed algorithm has been evaluated by parameter identification of an IGV servo actuator. Simulation together with experiment analysis has proved the effectiveness of the proposed method.

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Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

Cited by 11 publications

References 33 publications

A new conditional region growing approach for microcalcification delineation in mammograms

A new conditional region growing approach for microcalcification delineation in mammograms

Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm

Identification of piecewise affine systems based on fuzzy PCA-guided robust clustering technique

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