Sparse feature analysis for detection of clustered microcalcifications in mammogram images

465
-1

Published on

Sparse Feature Analysis for Detection of Clustered Microcalcifications in Mammogram Images.

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
465
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Sparse feature analysis for detection of clustered microcalcifications in mammogram images

  1. 1. SPARSE FEATURE ANALYSIS FOR DETECTION OF CLUSTERED MICROCALCIFICATIONS IN MAMMOGRAM IMAGES Wonyong Eom, Wesley De Neve, and Yong Man Ro Image and Video Systems Lab Korea Advanced Institute of Science and Technology (KAIST) Daejeon, South Korea e-mail: ymro@ee.kaist.ac.kr website: http://ivylab.kaist.ac.kr - FeaturesI. INTRODUCTION Table. 1. Feature types and dimensionality- Observation Features Dimension • Computer-aided detection (CADe) of clustered microcalcifications First Order Statistics (FOS) 16 (MCs) in mammogram image is one of the most effective tools for Rotation Invariant Moment (RIM) 16 detecting early-stage breast cancer. Spatial Gray Level Difference (SGLD) 52 Gray Level Run Length (GLRL) 20 • A number of CADe systems have recently introduced the use of Laws’ Texture Features (LAW) 250 sparse representation based classification (SRC). Uniform Local Binary Patterns (LBP) 118- Problem statement - Evaluation - Few attempts have thus far been made to achieve an in-depth • the area under the ROC curve (AUC) understanding of the influence of SRC on the effectiveness of these • the sparsity concentration of the true class (SCTC) CADe systems.- Contributions δ T ( x) 1 SCTC (x) = ∈ [0, 1], - We compare and analyze the influence of commonly used features, x1 different feature combinations and different dictionary construction where x represents the sparse coefficient vector, and where δT techniques on the effectiveness of an SRC-based CADe systems. denotes the true class part of x 2. Feature comparison II. METHOD Table. 2. Effectiveness of SRC-based detection of MC according the feature used 1. Dictionary construction Feature AUC SCTC FOS 0.8885 0.7923 f1 RIM 0.8546 0.7855 SGLD 0.8986 0.8011 GLRL 0.8814 0.7891 … f [[ Feature LAW 0.9403 0.8402 f 2 normalization/ … Feature … ROI LBP 0.9322 0.8168 detection extraction concatenation … … 3. Feature combination comparison … … … … Table. 3. Effectiveness of SRC-based detection of MCs according to the feature … ROI combination used fi Feature Combination AUC SCTC … … FOS+RIM 0.9047 0.8112 … Mammogram Dictionary SGLD+GLRL+LAW+LBP 0.9374 0.8324 Fig. 1. Creating a dictionary of image features FOS+LAW 0.9326 0.83152. Sparse representation based classification LAW+LBP 0.9483 0.8392 All features 0.9525 0.8401 Malignant Normal 4. Effectiveness of dictionary size ROIs ROIs - We gradually reduced the number of dictionary atoms from 90% of the available samples to 50% of the available samples. - This implied that smaller dictionaries were subsets of larger dictionaries. Feature extraction Test sample Dictionary Sparse coefficient - We made use of Laws’ texture feature. vector Residual for Residual for malignant ROI normal ROI Dictionary Classify as Fig. 2. Sparse representation based classification methodIII. EXPERIMENTS1. Experimental setup - Dataset • We made use of 180 malignancy-containing X-ray images randomly taken from the Digital Database for Screening Mammography(DDSM). • From these images, we obtain 434 malignant ROIs and 2556 normal Fig. 3. Different ROC curves according to the number of dictionary atoms tissue region using a contrast-based method for candidate region IV. CONCLUSIONS detection. - Our experimental results show that the use of texture features is more • We used 10 percent of the positive samples and 10 percent of the effective than the use of shape and morphology features. negative samples for the purpose of testing, while the remaining - SRC based MCs detection with LAW and the combination LAW+LBP is samples were used for the purpose of dictionary construction. We highly promising. repeated this ten times with different test sets, and then averaged - Our experimental results show that the more atoms in the dictionary, the results obtained for each run in order to compute the final the higher the discriminative power of SRC-based CADe system. results. International Forum on Medical Imaging in Asia (IFMIA), November 2012, Daejeon(Korea)

×