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Automatic detection of microcalcification based on morphological operations and structural similarity indices

Touil Asma, Kalti Karim, Conze Pierre-Henri, Solaiman Basel, Mahjoub Mohamed Ali

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1155--1173

EN

In this paper, a new method for automatic detection of microcalcifications in digitized mammograms is proposed. Based on mathematical morphology theory to deal with the problem of low contrast between microcalcifications and their surrounding pixels, it uses various structuring elements of different sizes to reduce the sensibility to microcalcification diversity sizes. The obtained morphological results are converted to a suspicion map based on an image quality assessment metric called structural similarity index (SSIM). This continuous map is, then, locally analyzed using superpixels to automatically estimate threshold values and finally detect potential microcalcification areas. The proposed method was evaluated using the publiclyavailable INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to state-of-the-art methods.

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Segmentation and feature extraction for reliable classification of microcalcifications in digital mammograms

Wróblewska A., Boniński P., Przelaskowski A., Kazubek M.

Opto-Electronics Review

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2003

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Vol. 11, No. 3

227-235

EN

Microcalcifications are one of more important signs enabling detection of breast cancer at an early stage. The main goal of the research was designing and realization of a system for automatic detection and classification of microcalcifications, taking advantage of the proposed automatic feature selection algorithm. The first step of the detection algorithm is to segment the individual objects : potential microcalcifications. This is achieved by applying opening by reconstruction top-hat technique and image thresholding based on approximation of an image local histogram with a probability density function of Gauss distribution. Selected features of the segmented objects are used as inputs to neural networks. The first classifier verifies the initial detection and the others assess a diagnosis of the input objects. The algorithm results are locations of suggested microcalcifications and optionally automatic diagnosis. The presented form of the system was verified in clinical tests using diagnosed databases (DDSM from the University of South Florida and own digitised database of mammograms). The achieved results are promising and comparable with other known systems. Efficiency of microcalcifications detection was up to 90%.

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Morphological method for extraction of microcalcifications in mammograms for breast cancer diagnosis

Nieniewski M.

Machine Graphics and Vision

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1999

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Vol. 8, No. 3

427-448

EN

The paper presents a new morphological method for extraction of microcalcifications in mammograms for breast cancer diagnosis. The proposed method is based on the use of the morphological detector together with morphological pyramid for detection of local irregularities of brightness in a wide range of size and shapes. The binary maps obtained from the pyramid indicate locations of the condidates for microcalcifications in the mammogram. Independently, the gray level reconstruction of the original mammogram is carried out in order to obtain the axact shape of h-domes, which depic regional maxima (hills) of brithness in the image. By thresholding the image of h-domes, one obtains a binary map of h-domes. Subsequently, a bimary reconstruction is carried out, in which the binary map of h-domes is used as a mask, and the map obtained from the pyramid after some modification is used as the marker. As a result of the reconstruction, the required map of microcalcifications is extracted. A number of tests of the proposed method on various mammograms are presented.