scholarly journals A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Jwan N. Saeed
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Image Segmentation ◽  
Active Contour ◽  
Breast Ultrasound ◽  
Common Cause ◽  
Cad Systems ◽  
Detection Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis. However, in the field of image processing, image segmentation is a tough task particularly in ultrasound (US) images due to challenges associated with their nature. This paper presents a survey on several techniques of ultrasonography images segmentation including threshold based, region based, watershed, active contour and learning based techniques, their merits, and demerits. This can provide significant insights for CAD developers or researchers to advance this field.

scholarly journals Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Saleem Z. Ramadan
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
The United States ◽  
Computer Aided Diagnosis ◽  
Current Level ◽  
Breast Cancer Detection ◽  
Cad Systems ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

scholarly journals CAD Based Method for Detection of Breast Cancer

2018 ◽  
Vol 11 (3) ◽  
pp. 154-168
Author(s):  
Indra Kanta Maitra ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Breast Cancer ◽  
Medical Image ◽  
Breast Tissue ◽  
Medical Image Processing ◽  
Deterministic Algorithm ◽  
Computational Algorithms ◽  
Cad Systems ◽  
Computer Aided ◽  
Aided Diagnosis

Breast cancer affecting the women is known to cause high mortality unless detected in right time. Detection requires Mammography followed by biopsy of the tumour or lesions present in the breast tissue. Contemporary Mammographic hardware has incorporated digitization of output imagesfor increasing the scope for implementation of computational methods towards Computer Aided Diagnostics (CAD).CAD systems require Medical Image Processing, a multi-disciplinary science that involves development of computational algorithms on medical images. Histopathological slides are examined for determination of malignancy after biopsy is performed. Digital Images are required to be registered and enhanced prior to application of any deterministic algorithm. This paper provides both effective and efficient improvements over existing algorithms and introduces some innovative ideas based on image segmentation process to develop computer aided diagnosis tools that can help the radiologists in making accurate interpretation of the digital mammograms.

IMAGE PROCESSING AND COMPUTER AIDED DIAGNOSIS IN DIGITAL MAMMOGRAPHY: A CLINICAL PERSPECTIVE

1993 ◽  
Vol 07 (06) ◽  
pp. 1493-1503 ◽  
Author(s):  
ETTA D. PISANO ◽  
FAINA SHTERN
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Cancer Detection ◽  
Digital Mammography ◽  
Computer Aided Diagnosis ◽  
Breast Cancer Detection ◽  
Digital Systems ◽  
Computer Assisted ◽  
Computer Aided ◽  
Aided Diagnosis

Mammographic technology has improved dramatically in the last two decades. The advent of digitally acquired mammograms offers the possibility of further improvements in early breast cancer detection. Specifically, digital acquisition systems decouple the process of X-ray photon detection from image display by using a primary detector that directly quantifies transmitted photons. Digital systems also allow a wide dynamic range so that a wider range of tissue contrast can be appreciated. Digital systems have the capacity to bring revolutionary advantages to breast cancer detection and management (1) image processing for increased lesion conspicuity; (2) computer-aided diagnosis for enhanced radiologic interpretation; (3) teleradiology, or image transmission, as a means of bringing world-class expertise to community hospitals and remote areas; (4) improved image access and communication through digital image archiving and transmission; and (5) dynamic, or “real time” imaging for use during biopsy and localization procedures. In this article, the authors review the literature 011 the use of image processing and computer assisted diagnosis for digital mammography. Future research goals in the development of digital mammography are also discussed.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

An automatic Computer-Aided Diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD)

2021 ◽  
Vol 69 ◽  
pp. 102914
Author(s):  
Raouia Mokni ◽  
Norhene Gargouri ◽  
Alima Damak ◽  
Dorra Sellami ◽  
Wiem Feki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Multimodal Fusion ◽  
Diagnosis System ◽  
Automatic Computer ◽  
Computer Aided ◽  
Aided Diagnosis
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