Computer-Aided Detection and Classification of Mass from BUS Images | Theory and Experiments

Poulami Raha (u. a.)

ISBN 10: 613734214X / ISBN 13: 9786137342145

Published by LAP LAMBERT Academic Publishing, 2018

About this Item

Computer-Aided Detection and Classification of Mass from BUS Images | Theory and Experiments | Poulami Raha (u. a.) | Taschenbuch | 136 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786137342145 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 111671699

Bibliographic Details

• Title: Computer-Aided Detection and Classification of Mass from BUS Images | Theory and Experiments
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 613734214X
• ISBN 13: 9786137342145
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 221 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Computer-Aided Detection (CAD) of breast cancer helps the radiologists in early diagnosis of breast cancer with good accuracy in a very cost-effective way. There is a growing interest for breast ultrasound (BUS) diagnosis owing to its efficiency and portability. However, the presence of speckle noise, low contrast and blurred boundary of mass in a BUS image make it challenging to determine the mass. In the current work, a CAD system is proposed for the diagnosis of breast cancer from BUS images. The methodology includes the phases of preprocessing, segmentation, feature extraction and classification of BUS images. The preprocessing algorithm used in this work efficiently removes noise and enhances the contrast of BUS images. Segmenting an accurate region of interest in turn results in efficient feature extraction and classification of BUS images into benign and malignant ones. The proposed CAD system has been tested on Matlab platform with several images to obtain reasonably good accuracy, specificity, and sensitivity. Moreover, hardware/software co-simulation of preprocessing and active contour based segmentation algorithm on Xilinx Zynq has been performed using Vivado HLS.

About the Author

Poulami Raha completed her Master of Science (by Research) from the Department of Electronics and Electrical Communication Engineering (E&ECE), Indian Institute of Technology (IIT) Kharagpur in 2017and B.Tech in ECE from SIEM, Siliguri under West Bengal University of Technology in 2013. Her research areas are Image Processing and VLSI design.