Share Email Print
cover

Proceedings Paper

Automatic tissue segmentation of breast biopsies imaged by QPI
Author(s): Hassaan Majeed; Tan Nguyen; Mikhail Kandel; Virgilia Marcias; Minh Do; Krishnarao Tangella; Andre Balla; Gabriel Popescu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel’s label and the histogram of these textons in that pixel’s neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel’s neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.

Paper Details

Date Published: 9 March 2016
PDF: 6 pages
Proc. SPIE 9718, Quantitative Phase Imaging II, 971817 (9 March 2016); doi: 10.1117/12.2209142
Show Author Affiliations
Hassaan Majeed, Beckman Institute of Advanced Science and Technology (United States)
Univ. of Illinois at Urbana-Champaign (United States)
Tan Nguyen, Beckman Institute of Advanced Science and Technology (United States)
Univ. of Illinois at Urbana-Champaign (United States)
Mikhail Kandel, Beckman Institute of Advanced Science and Technology (United States)
Univ. of Illinois at Urbana-Champaign (United States)
Virgilia Marcias, Univ. of Illinois at Chicago (United States)
Minh Do, Univ. of Illinois at Urbana-Champaign (United States)
Krishnarao Tangella, Christie Clinic (United States)
Univ. of Illinois at Urbana-Champaign (United States)
Andre Balla, Univ. of Illinois at Chicago (United States)
Gabriel Popescu, Beckman Institute for Advanced Science and Technology (United States)
Univ. of Illinois at Urbana-Champaign (United States)


Published in SPIE Proceedings Vol. 9718:
Quantitative Phase Imaging II
Gabriel Popescu; YongKeun Park, Editor(s)

© SPIE. Terms of Use
Back to Top