Title :
Learning-based automatic breast tumor detection and segmentation in ultrasound images
Author :
Peng Jiang ; Jingliang Peng ; Guoquan Zhang ; Erkang Cheng ; Megalooikonomou, Vasileios ; Haibin Ling
Author_Institution :
Shandong Provincial Key Lab. of Software Eng., Shandong Univ., Jinan, China
Abstract :
Ultrasound (US) images have been widely used in the diagnosis of breast cancer in particular. While experienced doctors may locate the tumor regions in a US image manually, it is highly desirable to develop algorithms that automatically detect the tumor regions in order to assist medical diagnosis. In this paper, we propose a novel algorithm for automatic detection of breast tumors in US images. We formulate the tumor detection as a two step learning problem: tumor localization by bounding box and exact boundary delineation. Specifically, the proposed method uses an AdaBoost classifier on Harr-like features to detect a preliminary set of tumor regions. The preliminarily detected tumor regions are further screened with a support vector machine using quantized intensity features. Finally, the random walk segmentation algorithm is performed on the US image to retrieve the boundary of each detected tumor region. The proposed method has been evaluated on a data set containing 112 breast US images, including histologically confirmed 80 diseased ones and 32 normal ones. The data set contains one image from each patient and the patients are from 31 to 75 years old. Experiments demonstrate that the proposed algorithm can automatically detect breast tumors, with their locations and boundary shapes retrieved with high accuracy.
Keywords :
biological tissues; biomedical ultrasonics; cancer; feature extraction; image retrieval; image segmentation; learning (artificial intelligence); mammography; medical image processing; object detection; support vector machines; tumours; AdaBoost classifier; Harr-like features; US images; age 31 yr to 75 yr; automatic breast tumor detection; automatic breast tumor segmentation; boundary shapes; breast cancer; medical diagnosis; quantized intensity features; random walk segmentation; support vector machine; tumor localization; two step learning problem; ultrasound images; Accuracy; Breast tumors; Feature extraction; Image segmentation; Support vector machines; Ultrasonic imaging; AdaBoost; Breast Tumor Detection; Random Walks; Support Vector Machine; Ultrasound Image;
Conference_Titel :
Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4577-1857-1
DOI :
10.1109/ISBI.2012.6235878