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

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

doi:10.1109/NEBC.2011.5778641

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

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

Department of Pathology and Laboratory Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 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 language	English (US)
Title of host publication	2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011
DOIs	https://doi.org/10.1109/NEBC.2011.5778641
State	Published - Jun 16 2011
Event	37th Annual Northeast Bioengineering Conference, NEBEC 2011 - Troy, NY, United States Duration: Apr 1 2011 → Apr 3 2011

Publication series

Name	2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011

Other

Other	37th Annual Northeast Bioengineering Conference, NEBEC 2011
Country/Territory	United States
City	Troy, NY
Period	4/1/11 → 4/3/11

All Science Journal Classification (ASJC) codes

Bioengineering

Access to Document

10.1109/NEBC.2011.5778641

Cite this

Lai, Y., Viswanath, S., Baccon, J., Ellison, D., Judkins, A. R., & Madabhushi, A. (2011). A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma. In 2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011 Article 5778641 (2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011). https://doi.org/10.1109/NEBC.2011.5778641

@inproceedings{ec4e7432c3c54f12819e9e55a2bb8caf,

title = "A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma",

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.",

author = "Ying Lai and Satish Viswanath and Jennifer Baccon and David Ellison and Judkins, {Alexander R.} and Anant Madabhushi",

year = "2011",

month = jun,

day = "16",

doi = "10.1109/NEBC.2011.5778641",

language = "English (US)",

isbn = "9781612848273",

series = "2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011",

booktitle = "2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011",

note = "37th Annual Northeast Bioengineering Conference, NEBEC 2011 ; Conference date: 01-04-2011 Through 03-04-2011",

}

Lai, Y, Viswanath, S, Baccon, J, Ellison, D, Judkins, AR & Madabhushi, A 2011, A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma. in 2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011., 5778641, 2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011, 37th Annual Northeast Bioengineering Conference, NEBEC 2011, Troy, NY, United States, 4/1/11. https://doi.org/10.1109/NEBC.2011.5778641

A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma. / Lai, Ying; Viswanath, Satish; Baccon, Jennifer et al.
2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011. 2011. 5778641 (2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma

AU - Lai, Ying

AU - Viswanath, Satish

AU - Baccon, Jennifer

AU - Ellison, David

AU - Judkins, Alexander R.

AU - Madabhushi, Anant

PY - 2011/6/16

Y1 - 2011/6/16

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=79958716129&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79958716129&partnerID=8YFLogxK

U2 - 10.1109/NEBC.2011.5778641

DO - 10.1109/NEBC.2011.5778641

M3 - Conference contribution

AN - SCOPUS:79958716129

SN - 9781612848273

T3 - 2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011

BT - 2011 IEEE 37th Annual Northeast Bioengineering Conference, NEBEC 2011

T2 - 37th Annual Northeast Bioengineering Conference, NEBEC 2011

Y2 - 1 April 2011 through 3 April 2011

ER -

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

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this