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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
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Advanced statistical analysis to classify high dimensionality textural probability-distribution matrices.

Jack Prothero1, Jean-Baptiste Vimort2, Antonio Ruellas3

  • 1Dept. of Statistics and Operations Research, University of North Carolina at Chapel Hill, Hanes Hall, Campus Box 3260, Chapel Hill, NC, USA 27599.

Proceedings of Spie--The International Society for Optical Engineering
|June 4, 2019
PubMed
Summary
This summary is machine-generated.

Diagnosing early Temporomandibular Joint (TMJ) Osteoarthritis (OA) is challenging. Analyzing raw texture data from medical scans, rather than down-sampled summaries, significantly improves the detection of early TMJ OA changes.

Keywords:
High Dimensionality Low Sample Size DataStatistical analysisSubchondral bone remodelingTemporomandibular Joint OsteoarthritisTexture analysis

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Area of Science:

  • Medical Imaging
  • Biostatistics
  • Oral and Maxillofacial Surgery

Background:

  • Temporomandibular Joint (TMJ) Osteoarthritis (OA) causes pain and disability.
  • Early diagnosis of TMJ OA is difficult.
  • Subchondral bone texture changes occur early in TMJ OA progression.

Purpose of the Study:

  • To investigate the diagnostic potential of raw probability-distribution matrices derived from TMJ OA medical scans.
  • To apply novel statistical methods for High Dimensionality Low Sample Size Data (HDLSSD) to analyze texture data.
  • To compare the discriminatory power of probability-distribution matrices against traditional summary features.

Main Methods:

  • Computed probability-distribution matrices from TMJ OA medical scans.
  • Employed novel statistical methods for High Dimensionality Low Sample Size Data (HDLSSD).
  • Analyzed and compared the discriminatory power of raw texture matrices versus summary features.

Main Results:

  • Raw probability-distribution matrices contain crucial information for TMJ OA diagnosis.
  • These matrices demonstrated significant discriminatory power in early TMJ OA detection.
  • Down-sampling texture data for analysis leads to loss of important diagnostic information.

Conclusions:

  • Probability-distribution matrices from texture analysis are vital for diagnosing TMJ OA.
  • Retaining raw texture data is essential for accurate early detection of TMJ OA.
  • Novel HDLSSD methods are effective for analyzing complex medical imaging texture data.