Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis

Breast cancer is one of the leading cause of cancer related deaths in women and early detection is crucial for reducing mortality rates. In this paper, we present a novel and fully automated approach based on tissue transition analysis for lesion detection in breast ultrasound images. Every candidat...

Full description

Saved in:
Bibliographic Details
Published in2014 22nd International Conference on Pattern Recognition pp. 1185 - 1188
Main Authors Biwas, Soma, Fei Zhao, Xiaoxing Li, Mullick, Rakesh, Vaidya, Vivek
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2014
Subjects
Breast
Cancer
Lesions
Markov processes
Medical diagnostic imaging
Ultrasonic imaging
Online AccessGet full text

Cover

More Information
Summary:Breast cancer is one of the leading cause of cancer related deaths in women and early detection is crucial for reducing mortality rates. In this paper, we present a novel and fully automated approach based on tissue transition analysis for lesion detection in breast ultrasound images. Every candidate pixel is classified as belonging to the lesion boundary, lesion interior or normal tissue based on its descriptor value. The tissue transitions are modeled using a Markov chain to estimate the likelihood of a candidate lesion region. Experimental evaluation on a clinical dataset of 135 images show that the proposed approach can achieve high sensitivity (95 %) with modest (3) false positives per image. The approach achieves very similar results (94 % for 3 false positives) on a completely different clinical dataset of 159 images without retraining, highlighting the robustness of the approach.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2014.213