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Breast tumor detection and classification based on density

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Abstract

Breast cancer is the most widely disease in women and is considered one of the biggest causes of death in women. Early detection, classification, and diagnosis of this cancer are essential to reduce the death cases. The efficiency of the diagnosis of breast cancer depends on the accuracy of the segmentation of the tumor and its classification. Before the segmentation of mammogram images, it is necessary to extract the Region of Interest (ROI) because it can assume a significant job in the efficiency of tumor detection. In this study, the method of automatic extraction of ROI from the mammogram image is presented. After that, the preprocessing of both mammogram and MR images has been done which removes the noise and enhances the contrast of the images. On the processed images, segmentation of tumors from mammograms as well as Magnetic Resonance Imaging (MRI) breast images has been performed using automatic seed point extraction and threshold calculation in Seeded Region Growing (SRG). After the successful detection of breast tumor area, classification of the tumor as benign or malignant has been done. The test was performed on three publicly available data sets; RIDER breast MR Images, Mammographic Image Analysis Society (MIAS), and Digital Database for Screening Mammography (DDSM). The test showed that classification accuracy is better than previously accessible cutting edge techniques, which is 91.4%. The proposed method not only detects the shape and size of the tumor from both mammograms and MR Images but also efficiently classifies the tumor as benign or malignant.

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Acknowledgements

We would like to thank organizer of Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and RIDER Breast MR Imaging Society for sharing mammograms images with us so that we are able to test the performance of our algorithm.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, MANIT, Bhopal, India

    Neeraj Shrivastava & Jyoti Bharti

  2. Department of Computer Science & Engineering, IPSA, IES, Indore, India

    Neeraj Shrivastava

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  1. Neeraj Shrivastava
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Correspondence to Neeraj Shrivastava.

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Shrivastava, N., Bharti, J. Breast tumor detection and classification based on density. Multimed Tools Appl 79, 26467–26487 (2020). https://doi.org/10.1007/s11042-020-09220-x

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