ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis

Biwas, Soma and Zhao, Fei and Li, Xiaoxing and Mullick, Rakesh and Vaidya, Vivek (2014) Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis. In: 22nd International Conference on Pattern Recognition (ICPR), AUG 24-28, 2014, Swedish Soc Automated Image Anal, Stockholm, SWEDEN, pp. 1185-1188.

[img] PDF
2014_22nd_Int_Con_on_Pat_Rec_1185_2014.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
Official URL: http://dx.doi.org/10.1109/ICPR.2014.213


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.

Item Type: Conference Proceedings
Series.: International Conference on Pattern Recognition
Additional Information: Copy right for this article belongs to the IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 24 Sep 2015 06:38
Last Modified: 24 Sep 2015 06:38
URI: http://eprints.iisc.ac.in/id/eprint/52422

Actions (login required)

View Item View Item