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Proceedings Paper

Breast ultrasound lesions classification: a performance evaluation between manual delineation and computer segmentation
Author(s): Moi Hoon Yap; Chuin Hong Yap
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Paper Abstract

Breast cancer is a threat to women worldwide. Manual delineation on breast ultrasound lesions is time-consuming and operator dependent. Computer segmentation of ultrasound breast lesions can be a challenging task due to the ill-defined lesions boundaries and issues related to the speckle noise in ultrasound images. The main contribution of this paper is to compare the performance of the computer classifier on the manual delineation and computer segmentation in malignant and benign lesions classification. This paper we implement computer segmentation using multifractal approach on a database consists of 120 images (50 malignant lesions and 70 benign lesions). The computer segmentation result is compared with the manual delineation using Jaccard Similarity Index (JSI). The result shows that the average JSI of 0.5010 (±0.2088) for malignant lesions and the average JSI of 0.6787 (±0.1290) for benign lesions. These results indicate lower agreement in malignant lesions due to the irregular shape while the higher agreement in benign lesions with regular shape. Further, we extract the shape descriptors for the lesions. By using logistic regression with 10 fold cross validation, the classification rates of manual delineation and computer segmentation are computed. The computer segmentation produced results with sensitivity 0.780 and specificity 0.871. However, the manual delineation produced sensitivity of 0.520 and specificity of 0.800. The results show that there are no clear differences between the delineation in MD and CS in benign lesions but the computer segmentation on malignant lesions shows better accuracy for computer classifier.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 978718 (24 March 2016); doi: 10.1117/12.2208797
Show Author Affiliations
Moi Hoon Yap, Manchester Metropolitan Univ. (United Kingdom)
Chuin Hong Yap, Univ. of Malaya (Malaysia)


Published in SPIE Proceedings Vol. 9787:
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Matthew A. Kupinski, Editor(s)

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