Extracting Micro-calcification Clusters on Mammograms for Early Breast Cancer Detection

Ma, Yuanjiao; Wang, Zi-Wu; Zheng, Jeffrey; Lian, Lu; Wang, Gang; Ma, Tianxin; Xie, Yinfu

doi:10.1109/icia.2006.305784

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…In order to interpret calcifications, a set of rules are implemented in the BI-RADS atlas. These guidelines provided support to the radiologist in differentiating doubtful calcifications from normally benign variations [ 18 , 19 ]. The examples include vascular and skin calcifications.…”

Section: Calcificationmentioning

confidence: 99%

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Chaudhury

Rakhra

Memon

et al. 2021

Computational and Mathematical Methods in Medicine

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Breast cancer is a strong risk factor of cancer amongst women. One in eight women suffers from breast cancer. It is a life-threatening illness and is utterly dreadful. The root cause which is the breast cancer agent is still under research. There are, however, certain potentially dangerous factors like age, genetics, obesity, birth control, cigarettes, and tablets. Breast cancer is often a malignant tumor that begins in the breast cells and eventually spreads to the surrounding tissue. If detected early, the illness may be reversible. The probability of preservation diminishes as the number of measurements increases. Numerous imaging techniques are used to identify breast cancer. This research examines different breast cancer detection strategies via the use of imaging techniques, data mining techniques, and various characteristics, as well as a brief comparative analysis of the existing breast cancer detection system. Breast cancer mortality will be significantly reduced if it is identified and treated early. There are technological difficulties linked to scans and people’s inconsistency with breast cancer. In this study, we introduced a form of breast cancer diagnosis. There are different methods involved to collect and analyze details. In the preprocessing stage, the input data picture is filtered by using a window or by cropping. Segmentation can be performed using k -means algorithm. This study is aimed at identifying the calcifications found in bosom cancer in the last phase. The suggested approach is already implemented in MATLAB, and it produces reliable performance.

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Section: Calcificationmentioning

confidence: 99%

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Chaudhury

Rakhra

Memon

et al. 2021

Computational and Mathematical Methods in Medicine

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“…A large amount of work is also done at the biomedical front to develop innovative means for effective diagnosis. Ma et al (2006) proposed a technology to extract micro-calcifications clusters with accurate edge effects to obtain much more hidden information which can't be detected by the naked eye on mammograms in order to help the doctors in diagnosing early breast cancer. Another approach makes use of fuzzy logic, vibroacoustography and probabilistic neural network on mammograms for computerized microcalcification detection for breast cancer (Cheng, Lui, and Freimanis, 1998;Alizad et al 2004;Karahaliou et al 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Diagnosis of breast cancer by modular evolutionary neural networks

Kala

Janghel

Tiwari

et al. 2011

IJBET

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Machine learning and pattern recognition play a vital role in the field of biomedical engineering, where the task is to identify or classify a disease based on a set of observations. The inability of a single method to effectively solve the problem gives rise to the use multiple models for solving the same problem in a 'Mixture of Experts' mode. Further the data may be too large for any system to effectively solve the problem. This motivates the use of computational modularity in the system where a number of modules independently solve part of the problem. In this paper we construct a Mixture of Experts model where a number of different techniques are applied to solve the same problem. The individual decision by each of these experts is fused by an integrator that gives the final output. Each of the units is a complex modular neural network. The first modularity clusters the entire input space into a set of modules. The second modularity divides the number of attributes. Each cluster is a neural network that solves the problem. The individual neural networks are evolved using Genetic Algorithms which optimizes both the architecture and the parameters. The complete system is used for the diagnosis of Breast Cancer. Experimental results show that the proposed system outperforms the traditional simple and hybrid approaches. The system on the whole is highly scalable to both number of attributes and data items.

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“…These are small deposits of calcium (and related) salts representing either warnings of malignancy or just benign formations. They are encountered in approximately 25% of mammograms and appear as bright spots or clusters of such spots, due to the high X-ray attenuation factor of calcium [4].…”

Section: Review Of Related Workmentioning

confidence: 98%

Identification of pre-processing technique for enhancement of mammogram images

Sharma

Tewari

2014

2014 International Conference on Medical Imaging, M-Health and Emerging Communication Systems (MedCom)

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This paper presents an analysis and identification of image pre-processing techniques suitable for mammogram images. Pre-processing is one of the preliminary stages used for mammogram image enhancement to aid an early identification of suspicious lesions and micro calcifications. Nine image preprocessing techniques are considered here for mammogram images. These techniques are implemented using Matlab. The results obtained are compared on the basis of Peak Signal to Noise Ratio (PSNR) for a set of 30 mammogram images. A high value of PSNR indicates better suitability of the pre-processing technique for further image processing. Anisotropic diffusion and Median filtering are suitable for noise removal. Power-law transformation, morphological processing, and un-sharp masking are giving better enhancement results in terms of achieved PSNR values as compared to other pre-processing techniques analyzed here. These identified pre-processing techniques, chosen carefully may give better results for further identification of masses, calcification, architectural distortion, bilateral asymmetry and aid in early detection of breast cancer.

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Extracting Micro-calcification Clusters on Mammograms for Early Breast Cancer Detection

Cited by 11 publications

References 6 publications

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Diagnosis of breast cancer by modular evolutionary neural networks

Identification of pre-processing technique for enhancement of mammogram images

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