Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

Sreeja, G. Bharatha; Rathika, P.; Devaraj, Deepashree

doi:10.5815/ijmecs.2012.03.08

Cited by 13 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sreeja, Rathika, and Devaraj () applied a wavelet‐based adaptive windowing technique to detect tumours from mammograms. The algorithm was validated with mammograms from the mini‐MIAS database and was demonstrated to be capable of detecting lesions of vastly different sizes.…”

Section: Introductionmentioning

confidence: 99%

Decomposition and recompilation of mammograms for breast tumour detection

Yeh

Chan

2018

Expert Systems

View full text Add to dashboard Cite

Breast tumours are the leading cause of cancer death among female adults. Early diagnosis, discovery and treatment are currently the optimal approach to reducing mortality rates and increasing the length of cancer patients' lives. Presently, mammography is the most effective screening method for early breast tumour detection. This paper proposes a decomposition and recompilation approach for the analysis of mammograms. The decomposition stage is based on multiband generation processes (MBGP), which are used to expand the dimensionality of a mammogram. Recompilation uses principal‐component analysis (PCA) to generate different views of the mammogram based on the orthonormal basis vectors. Using Otsu's thresholding method, the abnormal parts of mammographic images can be easily detected. In experiments, the average Jaccard values determined using the Bayes, Markov, Windows, and proposed method were 0.253, 0.228, 0.201, and 0.441, respectively, for images from the mini–MIAS; averages were 0.132, 0.186, 0.158, and 0.261 for images from the Digital Database for Screening Mammography. Thus, the proposed method outperformed the other methods. The main contributions of the current paper are as follows: A novel methodology consisting of multiband generation processes and principal‐component analysis for analyzing abnormities in mammograms is proposed. Two practical data sets were used to develop and evaluate the proposed approach.

show abstract

Section: Introductionmentioning

confidence: 99%

Decomposition and recompilation of mammograms for breast tumour detection

Yeh

Chan

2018

Expert Systems

View full text Add to dashboard Cite

show abstract

“…According to the international agency for research on cancer, around 79,000 women's per year affected by breast cancer in India [2]. The National Cancer Institute estimates that one of the eight women in the United States breast cancer will develop at some point during her lifetime [4].…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Directive Contrast Filter for Mammogram Images to Enhance Pleomorphic Calcification

Ramya¹,

Murugan²,

Murugan³

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…Presently breast cancer is a leading cause of death among women and second main cause of death after lung cancer [1][2][3][4][5][6][7]. Breast cancer is the one of the important factors of mortality in women over the world.…”

Section: Introductionmentioning

confidence: 99%

The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images

Ramani¹,

Vanitha²,

Valarmathy³

2013

IJIGSP

View full text Add to dashboard Cite

Presently breast cancer detection is a very important role for worldwide women to save the life. Doctors and radio logistic can miss the abnormality due to inexperience in the field of cancer detection. The pre-processing is the most important step in the mammogram analysis due to poor captured mammogram image quality. Pre-processing is very important to correct and adjust the mammogram image for further study and processing. There are Different types of filtering techniques are available for pre-processing. This filters used to improve image quality, remove the noise, preserves the edges within an image, enhance and smoothen the image. In this paper, we have performed various filters namely, average filter, adaptive median filter, average or mean filter, and wiener filter

show abstract

Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

Cited by 13 publications

References 33 publications

Decomposition and recompilation of mammograms for breast tumour detection

Decomposition and recompilation of mammograms for breast tumour detection

Non-Linear Directive Contrast Filter for Mammogram Images to Enhance Pleomorphic Calcification

The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images

Contact Info

Product

Resources

About