Automatic Detection of Microcalcifications in ROI Images Based on PFCM and ANN

Quintanilla-Domínguez, J.; Ojeda-Magaña, Benjamín; Marcano-Cedeño, Alexis; Barrón-Adame, J. M.; Vega-Corona, A.; Andina, Diego

doi:10.1080/1931308x.2013.838070

Cited by 9 publications

(13 citation statements)

References 30 publications

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“…The first one concerns the comparison between obtained MC mask (candidate MC mask) and the GT mask. The second one concerns the comparison between the candidate MC mask of the proposed approach ('Our') and other masks resulting from the application of three reported literature approaches: Quintanilla et al [6] ('Q'), Zhang et al [24] ('Zh') and Melendez et al [44] ('M'). It also addresses two different issues: the reliable MC localization and delineation problems.…”

Section: Resultsmentioning

confidence: 99%

“…Microcalcifications (MCs) are defined, by radiologists, as a tiny Asma Touil asmaa.touil@gmail.com; asma.touil@imt-atlantique.fr Extended author information available on the last page of the article. calcium deposit within the breast tissue [6]. They represent the breast changes that occurred as a result of age, old injuries, cysts or inflammations [7].…”

Section: Breast Cancer and Microcalcificationsmentioning

confidence: 99%

“…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%

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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: Resultsmentioning

confidence: 99%

Section: Breast Cancer and Microcalcificationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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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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“…Anyway, detecting all microcalcifications clusters is not an easy task, as there is often poor contrast on mammograms between microcalcifications and the surrounding tissue. Since microcalcifications belonging to the same cluster have similar properties (or features ) such as grey level intensity, the main objective of a clustering process is to evaluate this similarity according to a distance measure between the microcalcifications and the prototypes of groups, and each microcalcification is assigned to the group with the nearest or most similar prototype [ 20 ].…”

Section: The Fuzzy C-means Implemented With Features (Fcm-wf)mentioning

confidence: 99%

“…Quintanilla-Dominguez et al . [ 20 ] presented a method for the automatic detection of microcalcifications implemented by feature extraction and sub-segmentation steps. The feature extraction step is improved using a top-hat transform such that microcalcifications can be highlighted.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy technique for microcalcifications clustering in digital mammograms

et al. 2014

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BackgroundMammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control.MethodsIn this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from target dimensions and to optimize the recognition efficiency. A clustering method, based on a Fuzzy C-means (FCM), has been developed. The described method, Fuzzy C-means with Features (FCM-WF), was tested on simulated clusters of microcalcifications, implying that the location of the cluster within the breast and the exact number of microcalcifications are known.The proposed method has been also tested on a set of images from the mini-Mammographic database provided by Mammographic Image Analysis Society (MIAS) publicly available.ResultsThe comparison between FCM-WF and standard FCM algorithms, applied on both databases, shows that the former produces better microcalcifications associations for clustering than the latter: with respect to the private and the public database we had a performance improvement of 10% and 5% with regard to the Merit Figure and a 22% and a 10% of reduction of false positives potentially identified in the images, both to the benefit of the FCM-WF. The method was also evaluated in terms of Sensitivity (93% and 82%), Accuracy (95% and 94%), FP/image (4% for both database) and Precision (62% and 65%).ConclusionsThanks to the private database and to the informations contained in it regarding every single microcalcification, we tested the developed clustering method with great accuracy. In particular we verified that 70% of the injected clusters of the private database remained unaffected if the reconstruction is performed with the FCM-WF. Testing the method on the MIAS databases allowed also to verify the segmentation properties of the algorithm, showing that 80% of pathological clusters remained unaffected.

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Intelligent Computing Application for Cloud Enhancing Healthcare Services

Haldorai¹,

Ramu

2021

Computer Networks, Big Data and IoT

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Automatic Detection of Microcalcifications in ROI Images Based on PFCM and ANN

Cited by 9 publications

References 30 publications

A new conditional region growing approach for microcalcification delineation in mammograms

A new conditional region growing approach for microcalcification delineation in mammograms

Fuzzy technique for microcalcifications clustering in digital mammograms

Intelligent Computing Application for Cloud Enhancing Healthcare Services

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