Signal Processing Techniques and Computer-Aided Detection Systems for Diagnosis of Breast Cancer – A Review Paper

Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

“…However, the mechanism of these factors in the development of breast cancer remains unknown [6]. Calcifications and masses are identified as two types of breast tumors [3] as can be seen in Figures 3 (a) and (b). A mass is a space-occupying lesion with features such as location, density, and margin.…”

“…Benign calcifications are usually identical, large in size (diameter around 1-4 mm), coarse, round or oval-shaped, and dispersed or diffused. Microscopic, stellate-shaped, clustered in branches, innumerable (more than 5 in numbers) microcalcifications of different size and shape are found to be as malignant [3].…”

“…Therefore, Gabor or Wavelet transform is advantageous for its ability to identify the spatial location. Even though wavelet is not translation invariant, this can be overcome with curvelet analysis [3]. Besides all these conventional procedures, researchers proposed a new feature extraction method namely Square Centroid Lines Gray Level Distribution method (SCLGM) and Run Difference Method (RDM) in [43].…”

“…Each node is associated with gain or weight that can be adjusted to get the required output from the given input. Learning, and recall is the two working phases [3]. Weight adaptation of the nodes is done to train the ANN about the task during the learning phase either through supervised or unsupervised methods [19,50].…”

“…There are several available methods for imaging of the breast, such as mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), positron-emission tomography (PET) and microwave imaging as illustrated in Figure 1. A CAD system first reads a medical image before it sequentially performs pre-processing, segmentation, feature extraction, and classification activities [3] 2337 medical image to identify the normal and abnormal tissues. The CAD system must also be able to classify malignant tumors from abnormal cases.…”