Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review

Abstract

Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.

Automatic Diagnosing of Suspicious Lesions in Digital Mammograms

“…Second, we've used an automatic technique to take away further background, and detected mammogram orientation. From where, the pectoral muscle is within the top corner in right or left, the seed point of SRG is J [5,5] or J [5, y-5], (were J: is the mammogram when the background has removed, [x,y]=size(J)) and we've used a minimal threshold value for giving a good result with all type of mammogram (Fatty , Fatty-glandular, Dense-glandular) . Third, we used 2D median filter in a 3-by-3 neighborhood connection to remove additional objects (artifact and noise).…”

“…These systems are employed as a supplement to the radiologists' assessment. Generally, the procedure to develop a Computer-AidedDiagnosis (CAD) system, for diagnosing of suspicious regions in mammograms takes place in four steps: 1) Preprocessing step: this step is to prepare the mammograms for the next steps of operations (segmentaton, classification); 2)Detection of regions of interest :This step is to analyze the mammogram and extract the necessary information, for example, segmentation which divides the mammogram into multiple segments, edge detection which finds the edges of objects and helps us to find regions of interest; 3) Features extraction and selection of ROIs detected: In this step , we can identify specific patterns, shapes, density and texture; 4) Classification of ROIs: The purpose of this step is to classify the mammograms as Normal or Abnormal and malignant or benign [5] [6]. In this paper, we've proposed an automatic method to detect and diagnosing of suspicious lesions in mammogram.…”

Predicting Breast Cancer Tumor by Means of Ammographic Image Processing

“…As it is diagnosed very late, this often causes very expensive treatments and may lead to serious problems. The widespread technique used for detecting breast cancer is Mammography, but it is very difficult to differentiate between breast tumors due to structural similarities between them [5]. So early detection of breast cancer helps in full recovery of the disease.…”

Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research

“…The purpose of computer-aided dagnosis (CAD) is to improve the accuracy and consistency of medical image diagnosis through computational support used as reference [4]. Recently, detection and differential diagnosis CAD systems have been developed to reduce the expense and to improve the capability of radiologist in interpretation of medical images and differentiation between benign and malignant tissues [6].…”