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A tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes.

The detectability of fibrils in mammographic phantom images by morphological enhancement was analysed in the present study. Materials that mimic fibrils were imaged by a digital mammography machine at 28 and 29 kVP. The images were processed by a dilation technique to produce second set of images. Receiver operating characteristic analysis was performed to compare the detection performance from the two sets of images. As compared to original images, the 28 kVP’s fibrils images from dilation technique become more prominence to be detected by observers. While at 29 kVP only a few observers can found the fibrils images from dilation technique. This study suggests morphological enhancement of mammography image did not increase the detection of low frequency signals of the images.

The accurate segmentation of tumours is a crucial stage of diagnosis and treatment, reducing the damage that breast cancer causes, which is the most common type of cancer among women, especially after the age of forty. The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with small tumours that are not clear boundaries and hidden under the tissues. As algorithms often lose an automatic path toward the boundaries of the tumour at try to determine the site of this type of tumour. The study aims to create a clear contrast between the tumour and the healthy breast area. For this purpose, we used a Gaussian filter as a pre-processing as it works to intensify the low-frequency components while reducing the high-frequency components as the breast structure is enhanced and noise suppression. Then, CLAHE was used to improve the contrast of the image, which increases the contrast between the tumour and the surrounding tissue and sharpens the edges of the tumour. Next, the tumour was segmented by using the Chan-Vese method with appropriate parameters defined. The proposed method was applied to all abnormal mammogram images taken from a publicly available mini-MIAS database. The proposed model was tested in two ways, the first is statistical that got results (90.1, 94.8, 95.5, 92.1, 99.5) for Jaccard, Dice, PF-Score, precision, and sensitivity respectively. And the other is based on the segmented region's characteristics that results showed the algorithm could identify the tumour with high efficiency.

Mammograms are the images used by radiologists to diagnose breast cancer. In this diagnosis, the pectoral muscle appears in mammograms in  oblique mediolateral views (MLO) of the right breast and another in the left breast appears in cranio-caudal views which are marked with (CC). Considering that the pectoral muscle has the same density as the small, suspicious masses in the image, its presence in the image being processed could also require detection procedures. In this paper, we present a new general framework for pectoral muscle suppression which is the first work in the analysis of a mammography image. As a result, we proceed to four stages of image processing. The first step is to orient the image if necessary, then use a pre-processing which is to enhance the contrast of the image, and remove the digital lines of the image by morphological filters, apply a filter median. The third step involves segmenting all of the pectoral muscles, which involves threshold the entire image. The final step is to perform a pectoral muscle removal according to the orientation of the muscle in the image, which will be based on the development of the Hough transform for the recognition of borderline detections of the pectoral muscle. Some results obtained on the different images are discussed and compared with other methods (risk assessments). Evaluation of our method shows a significant improvement in performance in removing the pectoral muscle. Keywords: Breast cancer, Mammogram, Pectoral muscle, Hough transform.