Comparative analysis of spatial and transform domain methods for meningioma subtype classification
Qureshi, Hammad A. and Rajpoot, Nasir M. (Nasir Mahmood) (2010) Comparative analysis of spatial and transform domain methods for meningioma subtype classification. In: Medical Image Understanding and Analysis (MIUA 2010), Coventry, UK, 6-7 July 2010. Published in: Proceedings of Medical Image Understanding and Analysis 2010 pp. 209-213.
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Pattern recognition in histopathological image analysis requires new techniques and methods. Various techniques have been presented and some state of the art techniques have been applied to complex textural data in histological images. In this paper, we compare the novel Adaptive Discriminant Wavelet Packet Transform (ADWPT) with a few prominent techniques in texture analysis namely Local Binary Patterns (LBP), Grey Level Co-occurrence Matrices (GLCMs) and Gabor Transforms. We show that ADWPT is a better technique for Meningioma subtype classification and produces classification accuracies of as high as 90%.
|Item Type:||Conference Item (Paper)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
|Divisions:||Faculty of Science > Computer Science|
|Library of Congress Subject Headings (LCSH):||Histology, Pathological -- Computer programs, Pattern recognition systems, Meningioma -- Classification -- Computer programs|
|Journal or Publication Title:||Proceedings of Medical Image Understanding and Analysis 2010|
|Publisher:||University of Warwick|
|Official Date:||July 2010|
|Page Range:||pp. 209-213|
|Access rights to Published version:||Open Access|
|Conference Paper Type:||Paper|
|Title of Event:||Medical Image Understanding and Analysis (MIUA 2010)|
|Type of Event:||Conference|
|Location of Event:||Coventry, UK|
|Date(s) of Event:||6-7 July 2010|
 O.S. Al-Kadi. Texture measures combination for improved meningioma classification of histopathological images. Pattern Recognition, 2010.
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