Intelligent Breast Tumor Detection System With Texture and Contrast Features

Abstract

According to a research report by the World Health Organization (WHO), breast cancer is the most common type of cancer in women, while the mortality rate of breast cancer of females over 40 years old is extremely high. If detected early, it can be treated early, and the mortality rate of breast cancer can be reduced. Meanwhile, the image processing and pattern recognition technology has been adopted to select suspicious regions, provides alerts to assist in doctors' diagnosis, and reduces misdiagnosis rates due to fatigue of doctors, and improves diagnostic accuracy. Hence, this paper proposed an intelligent breast tumor detection system with texture and contrast features. This system consists of three parts: preprocessing, feature extraction, and learning algorithm. The goal of preprocessing is to obtain a good image quality and a real breast area. In the feature extraction, we extract the two features to describe the breast tumor. These features include Laws' Mask features which are the representation of the texture and modification average distance (MAD) feature which is the representation of the contrast. Each region of interest (ROI) image block will be extracted by these two features. And we will extract useful feature from all extracted features. We hope that a small quantity of feature can be used in our proposed system. Next, we use neural network as learning algorithm to detect the tumor with extracted features. Finally, in the experimental results, we use three databases to verify our proposed system, and two radiologists participated in that process and designed final verification study. Thus, we understand from the experimental results that a detection rate as high as 98% can be achieved by using only a few features and the simplest artificial neural network rather than a large number of features and a complex classifier. The success of the system will improve the accuracy of the existing detection methods, assist medical diagnosis, and decrease the time of the judgment effective by doctors.

Ischemic Stroke Detection System with a Computer-Aided Diagnostic Ability Using an Unsupervised Feature Perception Enhancement Method

“…Using our method, we first input a brain CT image like the one shown in Figure 2(a) to our system. To perform preprocessing, we use the cubic curve contrast enhancement method [ 13 , 24 ] to improve the image contrast and make its texture clearer. Figure 2(b) shows the cubic function of the contrast enhancement curve.…”

“…Lin and Chin [ 12 ] proposed a new algorithm for the detection and compensation of backlight images which addresses the weaknesses of conventional backlight image processing methods such as oversaturation and lack of contrast. This method can produce high image contrast and is used in medical image contrast enhancement [ 13 ]. Ušinskas et al [ 14 ] used 18 joint textural features to search for ischemic stroke patterns in CT slices, but the automated thresholding for each image, at the time of this study, was not complete.…”

Degenerative Disc Segmentation and Diagnosis Technology Using Important Features From Mri of Spine in Images

“…Here we use the cubic curve contrast enhancement method 16,17 to enhance the image contrast more obviously and its texture clearer. First, we use median¯lter to remove image noise.…”

The Recognition of Basal Ganglia Area in the Brain MRI Image with Hybrid Classifier

“…Wu proposed [7] using Cubic Curve for contrast-enhanced images and using texture feature and contrast feature to achieve breast cancer detection. Many researchers proposed different feature extraction method to describe different medical image characteristics.…”