**1. Introduction**

Cancer remains the killer disease in the world, and currently it has become a dangerous public health problem in many countries. In all kinds of cancer, the

problem arises when cancer cells begin to grow in uncontrolled manner or do not die when they should do so. In addition, breast cancer is a malignant tumor that is considered the most common type of cancer occurs in women and the second type of cancer in general. It has been announced, that more than 2 million new cases have been registered worldwide in 2018 [1]. Awareness of symptoms and the need for screening are very important to reduce the risk of cancer [2].

**2. The proposed CAD system**

*DOI: http://dx.doi.org/10.5772/intechopen.94026*

processing steps as shown in **Figure 1**.

reduce noise in the mammogram images.

breast profile, we follow the next four steps:

*Flow chart of CAD system for breast cancer classification.*

*2.1.2 Artifacts suppression and background separation*

**2.1 Image pre-processing**

breast cancer.

*2.1.1 Noise removal*

value.

**Figure 1.**

**51**

The proposed Computer-aided diagnostic system (CAD) that is used to classify the breast tissue in mammograms as malignant or benign is divided into three basic

*Medical Image Classification Using the Discriminant Power Analysis (DPA) of Discrete Cosine…*

This step represents an important one in most CAD systems. The image preprocessing helps strongly in the selection of the region of interest (ROI) that contains the abnormalities. It is performed to remove the unwanted objects, which include artifacts, labels, background noises and to suppress the pectoral muscle (**Figure 2**). The use of efficient image processing methods is an indispensable step for achieving a high accuracy classification in CAD systems for the diagnosis of

There are various types of noises affected on mammogram images, such as Salt and pepper noise, Speckle noise, Gaussian noise and Poisson noise. Therefore, it is important to remove the noises to enhance the image quality on the preprocessing step. Traditionally, the median filter is a well-known used filter for this kind of noises, due to its nonlinear behavior, its simplicity and capability to preserve edges [26]. The median filter replaces each pixel value by the median of all the neighboring pixels values in a window. In this chapter, we used a (3x3) median filter to

In order to remove all unwanted objects in the selected image and separate the

Step (1) Thresholding of the mammogram image by 0.0706 normalized

In medical imaging, it has been shown that early detection and proper treatment of breast cancer reduces the mortality rate by 20–40% [3]. The use of Mammography, represents an effective tool in the early detection of the breast cancer. As a result, many computer-aided diagnostic (CAD) systems have been developed using digital image processing techniques applied to mammography images. These systems are very useful to help radiologists in the early detection of breast cancers and then to classify the breast tumor as malignant or benign [4–6].

In general, any CAD system can be composed of three different steps: image preprocessing step, features extraction and selection step and finally the classification step. For the breast cancer detection and classification, many works have been presented to improve the efficiency of the CAD systems. In the pre-processing step, the pectoral muscle removal and the region of interest (ROI) extraction rest a big challenge. Numerous segmentation algorithms have been also proposed to suppress the pectoral muscle [7–10]. However, there is no universal segmentation algorithm that can give acceptable results for all cases.

In the features extraction step, different techniques can be used like, shape and texture features [11, 12], morphological and texture features [13], independent component analysis (ICA) [14], the discrete cosine transform (DCT) [15], the discrete wavelet transform (DWT) [16, 17] and other transforms. In [18], the authors used non-subsampled contourlet transformation together with discrete wavelet transform with gray level co-occurrence matrix for texture features extraction. Salabat Khan et al. used a Gabor filter blank (GBF) optimized by Particle Swarm Optimization (PSO) for the extraction of Gabor characteristics [19]. Mughal B et al. used the backpropagation neural network on the hat transformation with gray level co-occurrence matrix (GLCM) features [20].

For the classification step, the most used classifiers are Artificial Neural Networks (ANN), Support Vector Machine (SVM), Naïve Bayes (NB) and *k*-Nearest Neighbors (KNN). Recently, in [21], the authors used deep learning architecture that is known as You Only Look Once (YOLO). In [22], the authors proposed a deep Convolutional Neural Network (CNN). In [23], Agnes et al., used Multiscale All Convolutional Neural Network (MA-CNN).

In this chapter, we propose a new computer-aided diagnostic system to classify breast tumors as malignant or benign. In the pre-processing step, we have proposed a new algorithm to select a limited triangular region that contains the pectoral muscle to be eliminated, and then apply the SRG segmentation algorithm. Features extraction and selection are also very important processes to improve the system performances in classification and pattern recognition methods. By using discrete Fourier transform or discrete cosine transform, we obtain a frequency domain representation of the image that can be considered as a set of features for pattern recognition problems. While the FFT give complex coefficients, the DCT provide real values in the frequency domain. We have used the DCT transform for feature extraction, and we have proposed the selection of the most significant features using the (DPA) algorithm [24]. Finally, we have evaluated the performances of the algorithm using SVM, ANN, NB and KNN classifiers and the MIAS database mammograms [25].

*Medical Image Classification Using the Discriminant Power Analysis (DPA) of Discrete Cosine… DOI: http://dx.doi.org/10.5772/intechopen.94026*
