Comparison of breast structure and findings by X-ray mammography, ultrasound, cytology and histology: A retrospective study

Laine, Heikki; Rainio, J.; Arko, Harri; Tukeva, Timo

doi:10.1016/0929-8266(95)00087-9

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…Sonography was more effective for women younger than 35 years old (Bassett et al 1991). The results (Laine et al 1995) suggest that the more dense the breast parenchyma, the higher is the detection accuracy of malignant tumors using US. The accuracy rate of breast US imaging has been reported to be 96 to 100% in the diagnosis of simple benign cysts (Jackson 1990).…”

Section: Introductionmentioning

confidence: 84%

Breast ultrasound image enhancement using fuzzy logic

Guo

Cheng

Huang

et al. 2006

Ultrasound in Medicine & Biology

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Section: Introductionmentioning

confidence: 84%

Breast ultrasound image enhancement using fuzzy logic

Guo

Cheng

Huang

et al. 2006

Ultrasound in Medicine & Biology

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“…Sonography was more effective for women younger than 35 years old of age [7]. The results [8] show that the denser the breast parenchyma, the higher the detection accuracy of malignant tumors using US. The accuracy rate of breast ultrasound imaging has been reported to be 96-100% in the diagnosis of simple benign cysts [9].…”

Section: Introductionmentioning

confidence: 88%

A Novel Approach to Breast Ultrasound Image Segmentation Based on the Characteristics of Breast Tissue and Particle Swarm Optimization

Guo¹,

Cheng²,

Tian³

et al. 2008

Proceedings of the 11th Joint Conference on Information Sciences (JCIS)

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Breast cancer occurs to over 8% women during their lifetime, and is a leading cause of death among women. Sonography is superior to mammography in its ability to detect focal abnormalities in the dense breasts and has no side-effect. In this paper, we proposed a novel automatic segmentation algorithm based on the characteristics of breast tissue and the eliminating particle swarm optimization (EPSO) clustering analysis. The characteristics of mammary gland in breast ultrasound (BUS) images are analyzed and utilized, and a method based on step-down threshold technique is employed to locate the mammary gland area. The EPSO clustering algorithm employes the idea of "survival of the superior and weeding out the inferior". The experimental results demonstrate that the proposed approach can segment BUS image with high accuracy and low computational time. The EPSO clustering method reduces the computational time by 32.75% compared with the standard PSO clustering algorithm. The proposed approach would find wide applications in automatic lesion classification and computer aided diagnosis (CAD) systems of breast cancer.

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“…The most common screening methods are mammography and sonography. Compared to mammography, breast ultrasound examinations have several advantages [5]. Breast ultrasound examinations can obtain any section image of breast, and observe the breast tissues in real-time and dynamically.…”

Section: Breast Cancer Detection Methodsmentioning

confidence: 99%

“…A breast cancer CAD scheme separates suspicious regions that may contain masses from the background parenchyma -the tissue characteristic of an organ, as distinguished from associated connective or supporting tissues [2][3][4][5][6]. In other words, such schemes partition the mammogram into several nonintersecting regions and extract regions of interest (ROIs) and suspicious mass candidates from the ultrasound image.…”

Section: Proposed Methodsmentioning

confidence: 99%

An Adaptive K-means Clustering Algorithm for Breast Image Segmentation

Patel¹,

Sinha²

2010

IJCA

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Breast cancer is one of the major causes of death among women. Small clusters of micro calcifications appearing as collection of white spots on mammograms show an early warning of breast cancer. Early detection performed on X-ray mammography is the key to improve breast cancer diagnosis. In order to increase radiologist's diagnostic performance, several computer-aided diagnosis (CAD) schemes have been developed to improve the detection of primary identification of this disease. In this paper, an attempt is made to develop an adaptive k-means clustering algorithm for breast image segmentation for the detection of micro calcifications and also a computer based decision system for early detection of breast cancer. The method was tested over several images of image databases taken from BSR APPOLO for cancer research and diagnosis, India. The algorithm works faster so that any radiologist can take a clear decision about the appearance of micro calcifications by visual inspection of digital mammograms and detection accuracy has also improved as compared to some existing works.

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Comparison of breast structure and findings by X-ray mammography, ultrasound, cytology and histology: A retrospective study

Cited by 11 publications

References 3 publications

Breast ultrasound image enhancement using fuzzy logic

Breast ultrasound image enhancement using fuzzy logic

A Novel Approach to Breast Ultrasound Image Segmentation Based on the Characteristics of Breast Tissue and Particle Swarm Optimization

An Adaptive K-means Clustering Algorithm for Breast Image Segmentation

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