Morphologic Analysis of Microcalcifications

Lanyi, Marton

doi:10.1007/978-3-642-70192-4_12

Cited by 11 publications

(1 citation statement)

References 16 publications

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“…A variety of features have been studied in the literature to characterize microcalcifications and classify these abnormalities into malignant and benign, such as shape, morphological, cluster, intensity-based, and texture features [9], [17]. Early research showed how the morphological characteristics of microcalcifications could be used to differentiate between malignant and benign cases [18]. The shape and morphological features are mainly extracted from individual microcalcifications and describe the morphological characteristics of individual microcalcifications, such as roughness, size, and shape [7], [10], [19]- [21].…”

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confidence: 99%

Topological Modeling and Classification of Mammographic Microcalcification Clusters

Chen

Strange

Oliver

et al. 2015

IEEE Trans. Biomed. Eng.

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confidence: 99%

Topological Modeling and Classification of Mammographic Microcalcification Clusters

Chen

Strange

Oliver

et al. 2015

IEEE Trans. Biomed. Eng.

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Extraction and computational estimation of malignant microcalcification on mammography

Yabashi

Hata

Kubo

et al. 1989

Systems & Computers in Japan

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Mammography is one of the tools in the diagnosis of breast cancer. By a new method of mammography based on the soft X‐ray and the ultrafine X‐ray film, the X‐ray exposure is reduced and the fine internal structure of the breast can be observed. With the increase of the subjects for breast cancer, it is required to improve the accuracy and efficiency of the diagnosis. A diagnosis‐supporting equipment is required which provides an image display to assist in recognition of pathological changes, and provides an objective feature evaluation. This paper considers the microcalcification which is important in the diagnosis of the early breast cancer. The microcalcification appears as opaque points on the mammograph, and when the points are concentrated locally, it is diagnosed as malignant. After the emphasis by Laplacian processing, cell division was applied. The cell with aggregated microcalcification was extracted, and the image display was made so that the feature difference between cancerous and noncancerous lesions can be recognized. To arrive at a numerical representation of the feature difference, the following parameters were considered: (1) effective distance between cells; and (2) rate of change of the number of cells with regard to the change of the threshold. As the result of processing 37 cases, including 14 benign cases and three normal cases, up to 14 of 15 malignant cases were able to be separated from other benign and normal cases, using those two parameters.

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Computer-Aided Diagnosis of Mammographic Calcification Clusters: Impact of Segmentation

Kallergi

Heine

Tembey

Topics in Biomedical Engineering International Book Series

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Medical image analysis is an area that has always attracted the interest of engineers and basic scientists. Research in the field has been intensified in the last 15-20 years. Significant work has been done and reported for breast cancer imaging with particular emphasis on mammography. The reasons for the impressive volume of work in this field include (a) increased awareness and education of women on the issues of early breast cancer detection and mammography, (b) the potential for significant improvements both in the fields of imaging and management, and (c) the multidisciplinary aspects of the problems and the challenge presented to both engineers and basic scientists.The importance of mammography and computer applications in mammography has been and continues to be the topic of numerous workshops, conferences, and publications [1,2]. There seems to be sufficient evidence that mammogra- 708Kallergi, Heine, and Tembey arguments that support an opposite view [3]. It should be noted that breast cancer was the second major cause of death for women in 2003 and mammography has been responsibly for a mortality reduction of 20-40% [4,5]. Despite its success, mammography still has a false negative rate of 10-30% and great variability [6].Calcifications are one of the main and earliest indicators of cancer in mammograms. They are present in 50-80% of all mammographically detected cancers but pathologic examinations reveal an even greater percentage [7]. Most of the minimal cancers and in-situ carcinomas are detected by the presence of calcifications [7]. A review of the literature on missed breast cancers indicates that calcifications are not commonly found among the missed lesions [8]. Although perception errors are not excluded, particularly in the case of microcalcifications (size < 1 mm), the technique of screen/film mammography (SFM) has been significantly improved over the years offering high-contrast and high-resolution mammograms that make calcification perception relatively easy. A greater and continuing problem for radiologists, with a major impact on the specificity of the diagnosis, is the mammographic differentiation between benign and malignant clustered calcifications. Almost all cases with calcifications are recommended for biopsy but only about 15-34% of these prove to be malignant [9]. The biopsies necessary to make the determination between benign and malignant disease represent the largest category of induced costs of mammography screening and a major source of concern for radiologists, surgeons, and patients. The advent of full field direct digital mammography will probably amplify this problem by providing more details and revealing breast abnormalities at very early stages [10].In the last 20 years, researchers have developed various computer schemes for analyzing mammograms with calcifications, masses, and other breast abnormalities in an effort to improve mammography and breast cancer detection and diagnosis [11]. Computer algorithms can be divided in three groups depending on their final goal:...

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Morphologic Analysis of Microcalcifications

Cited by 11 publications

References 16 publications

Topological Modeling and Classification of Mammographic Microcalcification Clusters

Topological Modeling and Classification of Mammographic Microcalcification Clusters

Extraction and computational estimation of malignant microcalcification on mammography

Computer-Aided Diagnosis of Mammographic Calcification Clusters: Impact of Segmentation

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