Extraction of Micro Calcification Using Non Extensive Property of Mammograms:

Mohanalin, J.; Kalra, Prem Kumar; Kumar, Nishant

doi:10.1109/iadcc.2009.4809086

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…3 MCs are often embedded and camouflaged by varying densities of breast parenchymal textures, especially in younger women. Their breast tissue is very dense which make almost (MCs) invisible (Mohanalin et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Microcalcifications segmentation from mammograms for breast cancer detection

Hadjidj

Amel

Belgherbi

et al. 2019

IJBET

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The presence of microcalcifications (MCs) in X-ray mammograms provides an important early sign of women breast cancer. However, their detection still remains very complex due to the diversity in shape, size, their distributions and to the low contrast between the cancerous areas and surrounding bright structures in mammograms. This paper presents an effective approach based on mathematical morphology for detection of MCs in digitised mammograms. The developed approach performs an initial step in order to extract the breast area and removing unwanted artefacts out of the mammogram. Subsequently, an enhancement process is applied to improve appearance and increase the contrast of images and to eliminate noise. Once the breast region has been found, a segmentation phase through morphological watershed is performed in order to detect MCs. The performance of our approach is evaluated using a total of 22 mammograms extracted from the MIAS mammographic database, showing the presence of MCs. The obtained results were compared with manual detection, marked by an expert mammographic radiologist. These results show that the system is very effective, especially in terms of sensitivity.

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Section: Introductionmentioning

confidence: 99%

Microcalcifications segmentation from mammograms for breast cancer detection

Hadjidj

Amel

Belgherbi

et al. 2019

IJBET

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“…Cheng and Jiang approaches are based on maximum fuzzy Shannon entropy principle. In 2009, Mohanalin 19,20 used Tsallis entropy to enhance the contrast of microcalci¯cations in mammograms. In 2010, Mohanalin 21 used normalized Tsallis entropy to enhance the contrast of micro calci¯cations in mammograms.…”

Section: Introductionmentioning

confidence: 99%

Observation of Visible and Near-UV M1 Transitions from Highly Charged Kr, Mo and Xe Ions in LHD and its Prospect to Impurity Spectroscopy for D-T Burning Plasmas

Katai

Morita

Goto

2007

Plasma and Fusion Research

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Mammography is the most reliable, e®ective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of¯nding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of micro-calci¯cations while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzi¯ed using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzi¯ed mammogram. Mathematical morphology provides tools for the extraction of microcalci¯cations even if the microcalci¯cations are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has and parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several di®erent kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBC SD ), target-to-background contrast enhancement measurement based on entropy (TBC E ), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.

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