Abstract
In recent times, breast mass is the most diagnostic sign for early detection of breast cancer, where the precise segmentation of masses is important to reduce the mortality rate. This research proposes a new multiobjective optimization technique for segmenting the breast masses from the mammographic image. The proposed model includes three phases such as image collection, image denoising, and segmentation. Initially, the mammographic images are collected from two benchmark datasets like Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS). Next, image normalization and Contrast-Limited Adaptive Histogram Equalization (CLAHE) techniques are employed for enhancing the visual capability and contrast of the mammographic images. After image denoising, electromagnetism-like (EML) optimization technique is used for segmenting the noncancer and cancer portions from the mammogram image. The proposed EML technique includes the advantages like enhanced robustness to hold the image details and adaptive to local context. Lastly, template matching is carried out after segmentation to detect the cancer regions, and then, the effectiveness of the proposed model is analysed in light of Jaccard coefficient, dice coefficient, specificity, sensitivity, and accuracy. Hence, the proposed model averagely achieved 92.3% of sensitivity, 99.21% of specificity, and 98.68% of accuracy on DDSM dataset, and the proposed model averagely achieved 92.11% of sensitivity, 99.45% of specificity, and 98.93% of accuracy on MIAS dataset.
Highlights
In recent decades, breast cancer is the most common cause of death among women worldwide
Several imaging modalities are applied for breast cancer detection like X-ray, ultrasound, magnetic resonance imaging (MRI), histology, positron emission tomography (PET), and computerized tomography (CT). [3]
The mammogram image is the best choice among other imaging modalities for breast cancer diagnosis, because of its high reliability and cost-effectiveness
Summary
Breast cancer is the most common cause of death among women worldwide. The motivation of the BioMed Research International research article is to propose a new multiobjective model to overcome the aforementioned problems and to enhance the performance of breast cancer segmentation. DDSM and MIAS datasets are used to validate the effectiveness of the proposed model, and normalization, CLAHE, and median filtering techniques are employed for enhancing the visual ability and contrast of the mammographic images. The CLAHE significantly improves the brightness of a mammographic image, which helps in better segmentation of noncancerous and cancerous regions. The proposed multilevel multiobjective EML technique is adaptive to local contexts and robust in preserving the image edge details, when compared to other algorithms, namely, k-means, fuzzy C-means (FCM) [16, 17], and traditional EML.
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