Chapter

Digital Mammography

Volume 4046 of the series Lecture Notes in Computer Science pp 332-339

Classifying Masses as Benign or Malignant Based on Co-occurrence Matrix Textures: A Comparison Study of Different Gray Level Quantizations

  • Gobert N. LeeAffiliated withCarnegie Mellon UniversityDepartment of Intelligent Image Information, Division of Regeneration and, Advanced Medical Sciences, Graduate School of Medicine, Gifu University
  • , Takeshi HaraAffiliated withCarnegie Mellon UniversityDepartment of Intelligent Image Information, Division of Regeneration and, Advanced Medical Sciences, Graduate School of Medicine, Gifu University
  • , Hiroshi FujitaAffiliated withCarnegie Mellon UniversityDepartment of Intelligent Image Information, Division of Regeneration and, Advanced Medical Sciences, Graduate School of Medicine, Gifu University

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Abstract

In this paper, co-occurrence matrix based texture features are used to classify masses as benign or malignant. As (digitized) mammograms have high depth resolution (4096 gray levels in this study) and the size of a co-occurrence matrix depends on Q, the number of gray levels used for image intensity (depth) quantization, computation using co-occurrence matrices derived from mammograms can be expensive. Re-quantization using a lower value of Q is routinely performed but the effect of such procedure has not been sufficiently investigated. This paper investigates the effect of re-quantization using different Q. Four feature pools are formed with features measured on co-occurrence matrices with Q ∈{400}, Q ∈{100}, Q ∈{50} and Q ∈{400, 100, 50}. Classification results are obtained from each pool separately with the use of a genetic algorithm and the Fisher’s linear discriminant classifier. For Q ∈{400, 100, 50}, the best feature subsets selected by the genetic algorithm and of size k=6,7,8 have a leave-one-out area under the receiver operating characteristic (ROC) curve of 0.92, 0.93 and 0.94, respectively. Pairwise comparisons of the area index show that the differences in classification results for Q ∈{400, 100, 50} and Q ∈{50} are significant (p<0.06) for all k while that for Q ∈{400, 100, 50} and Q ∈{400} or Q ∈{100} are not significant.