Precancerous microcalcification detection of breast cancer mammogram images using linear time-invariant filtering Wiener method with Tophat transformation

Abstract

Breast microcalcifications, tiny calcium salt deposits, can develop anywhere in the breast tissue. Breast microcalcifications are a frequent mammographic finding. For a proper diagnosis, it is essential to spot microcalcifications in mammograms as soon as possible because they are a typical early indicator of breast cancer. Computer-based detection output can help the radiologist improve diagnosis accuracy because of their tiny size and tendency to be unseen to the radiologist doing the examination. Because of its small size, it is difficult to notice with the naked eye. The identification of microcalcification is essential for cancer prevention. In this paper, I proposed a linear time-invariant filtering Wiener method with Tophat transformation (LFWT) breast microcalcification detection method, applied mammographic image corrections using a Wiener filter to remove noise, and used Contrast Limited Adaptive Histogram Equitation (CLAHE) to improve mammographic image quality. The Wiener and CLAHE filter makes visible the cancer part. After image enhancement, Tophat morphological operators such as opening and closing are applied and the mask is detected. After that, the edges are extracted and selected according to the actual image. The diagnostic performance of the proposed model was evaluated with MIAS data and In comparison to other techniques for spotting microcalcifications in mammograms. These are Local Contrast Method (LCM), Relative Local Contrast Measure Method (RLCMM), and High-Boost-Based Multiscale Local Contrast Measure (HBBMLCM) techniques used to identify cancer microcalcification on mammography imgaes. The LFWT technique was found to be the most effective for the detection of microcalcification of breast cancer compared to the other three methods. The proposed LFWT technique detects all small and tiny spots. The images used in the LFWT method are taken from the MIAS dataset of the microcalcification for breast cancer detection. Before deploying the images, several steps were carried out to remove artifacts such as pectorals and clipping etc. The result is a breast cancer with nice smooth, safe margins and high quality. All MIAS breast cancer images were recorded and in each image, all microcalcifying spots were detected. In every image, where one or more microcalcifications were found in the mammography images, Microcalcifications were detected in the Mammogram images. Microcalcifications were found in either tumor or non-tumour images.