Automated GrowCut for Segmentation of Endoscopic Images Farah Deeba, Francis M. Bui, Khan A. Wahid Department Of Electrical And Computer Engineering University Of Saskatchewan

Motivation

Vicious Cycle of Stagnant Progress in Automated Screening Sub-optimum CAD based screening Reluctance of physician for CAD based Screening

Bridging the Gap between Clinical Practice and Research Classify Test Images Semantic Annotation of Images Update Training Data Train Classifier Feedback of Physician Manually Annotated Image Decision Semantic Annotation of Images Closed-loop Decision Making Framework

Annotation using GrowCut Segmentation Originally, GrowCut is an interactive segmentation algorithm, which requires manual seed to begin with. The manually labeled seeds then attack the neighborhood and try to spread over by propagating their labels. The competition among the neighboring cells is determined by a pre-defined state transition rule.

GrowCut: Inter- and Intra-Observer Variability

Proposed Algorithm: Automatic GrowCut (AGC)  Objective 1.Reduce Variability; 2.Reduce Manual Labor; 3.Essential Element for Closed-loop Decision Making;

Proposed Algorithm: Overview Segmentation using GrowCut Labeling the Seeds Automatic Seed Selection Input Image

First Step: Automatic Seed Selection  Blind Segmentation using k-means clustering;  Find the optimum number of clusters using Davies-Bouldin Criterion;  Seed selection: Select the cluster centroids as seeds;

First Step: Automatic Seed Selection Selecting optimum number of cluster using Davies Bouldin Criterion Resulting Clustered Image

First Step: Automatic Seed Selection Cluster Centroids are selected as seeds;

Second Step: Labeling the Seeds  The prior knowledge of the labels, i.e., bleeding, non-bleeding, and background will be used;  The seeds representing the background will invariably have a value {R, G,B} = { 0; 0, 0 };  The remaining seeds will be labeled as bleeding or non-bleeding by a previously trained SVM classifier;

Second Step: Labeling the Seeds Bleeding Non-bleeding

Third Step: Segmentation using GrowCut

Data and Ground Truth Ground- truth Image Original Image

Evaluation Measure  Accuracy  Efficiency  Reproducibility

Evaluation Measure: Accuracy

Accuracy: Experimental Results Accuracy MeasureInteractiveProposed (AGC) Table 1: Comparison Of Result Obtained From Interactive And Proposed Automatic Segmentation

Accuracy: Experimental Results

Evaluation Measure: Efficiency Time (sec/frame)InteractiveProposed (AGC) Table 2: Comparison Of Average Time Required For Interactive And Proposed Automatic Segmentation

Evaluation Measure: Reproducibility

Reproducibility: Experimental Results  Interactive: CV= 7.54%  Proposed: CV= 0%

Evaluation Measure: Degree of Agreement  Linear Regression and Pearson Correlation Coefficient  Bland Altman Analysis

Experimental Result: Linear Regression

Experimental Result: Correlation  Both interactive and proposed method are highly correlated with ground truth segmentation;  Interactive: Pearson Correlation Coefficient, r=  Proposed (AGC): Pearson Correlation Coefficient, r=

Experimental Result: Bland Altman Analysis Interactive Segmentation Automatic GrowCut

Experimental Result: Bland Altman Analysis  Both interactive and proposed method have similar biases measured using Bland Altman Analysis;  Interactive GrowCut: Bias = 15% of mean area;  Proposed (AGC): Bias = 20% of mean area;  Both methods are not significantly different from the manual segmentation;

Comparison with Other Methods  Unsupervised GrowCut (UGC)  Fuzzy C-means Segmentation (FCM)

Qualitative Comparison UGCFCM Proposed (AGC)Ground truth

Quantitative Comparison UGCFCMProposed (AGC) DSC MR Time (sec) Table 3: Comparison of Segmentation Performance of Proposed Method (AGC) with UGC And FCM

Conclusion and Future Work Classify Test Images Segmentation using AGC Update Training Data Train Classifier Feedback of Physician Manually Annotated Image Decision

Acknowledgements