A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma

Ying Lai, Satish Viswanath, Jennifer Baccon, David Ellison, Alexander R. Judkins, Anant Madabhushi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Medulloblastoma (MB) is the most common brain tumor in children. There are four distinct subtypes of MB, but patients with anaplastic/large cell have the worst prognosis. Since the morbidity is highly correlated with treatment for MB, the ability to distinguish aggressive (such as anaplastic/large cell) MB is crucial. We present a scheme that leverages quantitative image texture features (Haar, Haralick, and Laws) and classifier ensembles (random forests) to automatically classify histological images from MB resection as being anaplastic/large cell or non-anaplastic/large cell. Preliminary results for our scheme when applied to patch-based classification of MB specimens yield an AUC of 0.91.

Original languageEnglish (US)
Title of host publication2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011
DOIs
StatePublished - Jun 16 2011
Event37th Annual Northeast Bioengineering Conference, NEBEC 2011 - Troy, NY, United States
Duration: Apr 1 2011Apr 3 2011

Publication series

Name2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011

Other

Other37th Annual Northeast Bioengineering Conference, NEBEC 2011
CountryUnited States
CityTroy, NY
Period4/1/114/3/11

All Science Journal Classification (ASJC) codes

  • Bioengineering

Fingerprint Dive into the research topics of 'A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma'. Together they form a unique fingerprint.

Cite this