An algorithm for pre-processing and segmentation of mammogram images

Ibrahim, Naglaa S. Ali; Soliman, Naglaa F.; Abdallah, M. I.; El‐Samie, Fathi E. Abd

doi:10.1109/icces.2016.7821997

Cited by 11 publications

(8 citation statements)

References 6 publications

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“…Two cases to be distinguished:  Global thresholding: In this case, the image is composed of two parts separated by a single threshold value, the background and the foreground. Each pixel of the image is compared to the threshold value, the pixels below the threshold are setting to zero (background) where the pixels above the threshold are setting to one (foreground) [27]- [29].  Local thresholding: In this case, the image contains various objects, it's characterized by a multimodal histogram.…”

Section: A Threshold-based Methodsmentioning

confidence: 99%

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Automatic Mammogram image Breast Abnormality Detection and localization based on the combination of k-means and Genetic algorithms methods

Malali¹,

Assir²

2020

IJATCSE

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Currently, most oncologists recommend mammography as an effective medical imaging modality for the screening of breast cancer. The aim is to reduce the mortality rate due to this disease. However, several imperfections contribute to the misclassification of breast lesions. Therefore, computer-aided diagnosis (CAD) systems are tools that allow the radiologists a second opinion to improve the diagnosis accuracy. In this paper, an accurate fully automatic method for breast abnormality extraction is presented. The extracted region of interest(ROI) can be subjectively classified by radiologists or used to automatically extract the features that allow for automatic classification. The proposed method consists of two steps: the preprocessing step for delimiting the ROI by removing all the artifacts including the pectoral muscle, followed by the contrast enhancement; the second step is devoted to the extraction of the suspicious area by segmenting the ROI using the k-means algorithm to avoid any initialization. Both contrast enhancement and ROI segmentation are performed by using a genetic algorithm (GA) to achieve good results. The simulation results show the accuracy of the proposed method by comparing the center coordinates and the radius enclosing the abnormality found with those provided with the database.

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Section: A Threshold-based Methodsmentioning

confidence: 99%

“…The background of mammogram images is often subject to a radiopaque artifact such as labels, wedges, and markers [29], [34]. Their existence affects the accuracy of the CAD's results.…”

Section: A Artifact Eliminationmentioning

confidence: 99%

Automatic Mammogram image Breast Abnormality Detection and localization based on the combination of k-means and Genetic algorithms methods

Malali¹,

Assir²

2020

IJATCSE

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“…This filter is a non-linear digital filter. It is applied in a 3 × 3 neighborhood connection [19]. The median filter is used widely due to its efficiency as it removes noise and keeps edges.…”

Section: Removal Of Digitization Noisementioning

confidence: 99%

An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

Soliman¹,

Ali²,

Aly³

et al. 2022

Computers, Materials &Amp; Continua

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Breast cancer is the most common type of cancer, and it is the reason for cancer death toll in women in recent years. Early diagnosis is essential to handle breast cancer patients for treatment at the right time. Screening with mammography is the preferred examination for breast cancer, as it is available worldwide and inexpensive. Computer-Aided Detection (CAD) systems are used to analyze medical images to detect breast cancer, early. The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations. Processing of mammogram images has four main steps: pre-processing, segmentation of the region of interest, feature extraction and classification of the images into normal or abnormal classes. This paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect masses. The pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter, removal of artifacts using morphological operations, and contrast enhancement using a fuzzy enhancement technique. The proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure (EME) and local contrast metrics. The comparison shows an outstanding performance of the proposed technique from the visual and numerical perspectives. The segmentation process is performed using Otsu's multiple thresholding method. This method segments the image regions into five classes with variable intensities using four thresholds. Its effectiveness is measured based on visual quality of the segmentation output, as it gives details about the image and positions of masses. The performance of the proposed framework is measured using Dice coefficient, Hausdorff, and Peak Signal-to-Noise Ratio (PSNR) metrics. The segmented tumor region with the proposed segmentation method is 81% of the ground truth region provided

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“…Designing of a CAD system consists of various steps such as pre-processing, Segmentation, Feature Extraction, Feature Selection and Designing efficient classifier [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. In the preprocessing phase removal of artefacts, noise etc.…”

Section: Introductionmentioning

confidence: 99%

Two-way threshold-based intelligent water drops feature selection algorithm for accurate detection of breast cancer

2021

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Variables to update the velocity of the water drops.(𝑎 𝑠 , 𝑏 𝑠 , 𝑐 𝑠 ): Variables to update the soil of the local path. 𝑀𝑎𝑥𝐼𝑡𝑒𝑟: Maximum number of iterations. 𝑖𝑛𝑖𝑡𝑆𝑜𝑖𝑙: Initial value of the local soil. 𝑖𝑛𝑖𝑡𝑉𝑒𝑙: Initial velocity associated with each of the water drop. 𝑉 𝑐 (𝐼𝑊𝐷 𝑟 ) : Feature list visited by each water drop 𝑟. 𝑖𝑛𝑖𝑡𝑉𝑒𝑙 (𝐼𝑊𝐷 𝑟 ) : Velocity of the water drop 𝑟. 𝑠𝑜𝑖𝑙 (𝐼𝑊𝐷 𝑟 ) : Soil associated with the water drop 𝑟. 𝜌 𝑛 : Local soil updating parameter.𝜌 𝐼𝑊𝐷 : Global soil updating parameter.𝜀 𝑠 : Parameter to prevent zero division.

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An algorithm for pre-processing and segmentation of mammogram images

Cited by 11 publications

References 6 publications

Automatic Mammogram image Breast Abnormality Detection and localization based on the combination of k-means and Genetic algorithms methods

Automatic Mammogram image Breast Abnormality Detection and localization based on the combination of k-means and Genetic algorithms methods

An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

Two-way threshold-based intelligent water drops feature selection algorithm for accurate detection of breast cancer

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