Robust methods for precise segmentation of breast region or volume from breast X-ray images, including mammogram and tomosynthetic image, is crucial for applications of these medical images. However, this task is challenging because the acquired images not only are inherent noisy and inhomogeneous, but there are also connected or overlapped artifacts, or noises on the images as well, due to loc...

This paper proposes a novel approach to initial lesion detection in ultrasound breast images. The objective is to automate the manual process of region of interest (ROI) labeling in computer-aided diagnosis (CAD). We propose the use of hybrid filtering, multifractal processing, and thresholding segmentation in initial lesion detection and automated ROI labeling. We used 360 ultrasound breast im...

Breast cancer is one of the leading causes of fatality in women. Mammogram is the effectual modality for early detection of breast cancer. Increased mammographic breast density is a moderate independent risk factor for breast cancer, Radiologists have estimated breast density using four broad categories (BI-RADS) swearing on visual assessment of mammograms. The aim of this paper is to review ap...

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is a medical imaging tool used to detect and diagnose breast disease. A DCE-MR image is a series of three-dimensional (3D) breast MRI scans. It is acquired to form a 4D image (3D spatial + time), before and after the injection of paramagnetic contrast agents. DCE-MRI allows the analysis of the intensity variation of ma...

Spectroscopic diffuse optical tomography (DOT) can directly image the concentrations of physiologically significant chromophores in the body. This information may be of importance in characterizing breast tumours and distinguishing them from benign structures. This paper studies the accuracy with which lesions can be characterized given a physiologically realistic situation in which the backgro...

This project is to develop an automated breast density segmentation system. Breast density has been shown to be a key indicator of breast cancer risk. Currently the primary computational system for analyzing breast density is a semiautomated segmentation system that requires a radiologist to set thresholds for the segmentation algorithm. This results in reader variability, which reduces the val...

Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracie...

Introduction: Lung cancer is one of the most common causes of cancer-related deaths worldwide. Nowadays PET/CT plays an essential role in radiotherapy planning specially for lung tumors as it provides anatomical and functional information simultaneously that is effective in accurate tumor delineation. The optimal segmentation method has not been introduced yet, however several ...

Breast cancer is one of the usual cancers among the women in the worldwide population. The research paper is developing of a reliable tool to detect earlier signs of the breast cancer in mammograms. Accuracy rate of breast cancer in mammogram depends on image segmentation. Doctors and radiologists can miss the abnormality, due to inexperience's in the field of breast cancer detection. The ...