Accurate detection and segmentation of suspicious regions within the complex and irregular tissues of the breast, as depicted with ultrasonic B scans, typically require human analysis and decision making. Tissue characterization methods for classifying suspicious regions often depend on identifying and then accurately segmenting these regions. Motivated by an ultimate goal to automate this critical identification and segmentation step for tissue characterization problems, this work examines ultrasonic signal characteristics between various regions of breast tissue broadly classified as normal tissue and breast lesions. This paper introduces a nonparametric model based on order statistics (OS) estimated from multiresolution (MR) decompositions of energy-normalized subregions. Experimental results demonstrate the classification performance of the OS-based features extracted from the tumor and normal tissue regions in multiple scans from 84 patients, which resulted in a total of 204 tumor regions (from 43 malignant and 161 benign) and 816 normal tissue regions. Performance results indicate that OS-based features achieved an area under the receiver-operator characteristic curve of 91% in the discrimination between breast lesions and surrounding normal tissues.
|Number of pages||10|
|State||Published - Apr 2006|
Bibliographical noteFunding Information:
We acknowledge the support of the National Cancer Institute and the National Institutes of Health Grant No. PO1-CA52823, the contributions of Dr. Jack Reid and Dr. P. M. Shankar from Drexel University in organizing the program project that created the data set used in this work.
- Breast tissue classification
- Order statistics
- Ultrasound signals
- Wavelet multi-resolution image analysis
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging